The Secret History of Dark Matter

One of the nice things about doing all my academic reading on a digital tablet is that I can download papers I'd like to read purely for the sake of interest. I don't get much chance to actually read them, but it's better than having a huge list of bookmarks I'll never check, or a stack of printed papers so large it could qualify as a carbon sequestering facility.

Finally, I managed to get round to reading one such paper, and it turned out to be a thoroughly worthwhile read. Maybe a bit on the lengthy side, but then that's what this blog is for.

Anyway, the popular narrative history of dark matter goes something like this. Jan Oort and Fritz Zwicky made some early claims that they might have found it back in the 1930s, but it was all from purely weak observational evidence. There wasn't any particular theoretical reason for it, so everyone ignored it until the 1970s when Vera Rubin and others started finding that galaxies were rotating much too quickly. Et voila, paradigm shift, everyone got very excited, and this resulted in the modern cosmology we know and love.

This paper* makes some important revisions to what actually happened. Rather than being pure observational luck, Zwicky had clear theoretical motivation for dark matter. Some of his underlying reasons for expecting dark matter have long since been thoroughly refuted, but some have intriguing similar aspects to modern theories. And he was even, quite likely, actively searching for it, with his technique being one that's still in regular use today. His sample size was absolutely shite – seven or eight galaxies would never be enough to convince anyone, but the method was sound. 

* I'm unsure of the provenance of the article. It appears to be only uploaded to preprint servers with no hint of whether it's submitted to a journal or not. 

What he did not do was stumble on a result he couldn't explain and invent the idea of dark matter as an ad hoc "fudge factor". More on this later, but even from its earliest days, there were already strong theoretical reasons to believe dark matter existed before observations started to get ahead of the game. Today, everyone knows about Zwicky, but most people forget the theoretical paradigms in which he operated. This paper attempts to set the record straight.

What follows is my summary of the paper. I've tried only to shorten and simplify the content rather than put too much of my own spin on things.

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Zwicky did not at all like the idea of an expanding Universe. Today, this is as well-established as any result in science can be, but at the time, he had good reasons to be skeptical. The difference in redshifts caused by expansion of space and motion through space were not yet fully appreciated, and the "breathtaking speeds" of galaxies moving at thousands of kilometres per second therefore seemed ludicrous. From a contemporary vantage point this seems weird, but when you only have a meagre handful of data points and they seem to be implying that something outlandish is happening, most of the time it's actually quite sensible to bet on your pre-existing ideas. 

This is what led to Zwicky's "tired light" hypothesis as an alternative to cosmological expansion. The idea was that photons would lose energy as they travelled for long distances, becoming redder and redder. Galaxies might indeed be at stupendous distances – I see no indication Zwicky ever doubted this monumental discovery from Hubble – but their speeds might be an illusion. 

The thing is, what would cause a photon to become tired ? Zwicky's answer was that there must be some intervening material, unseen through direct observation, but inferable through its effects on photons. And he wasn't the first to suggest that there could be some quantity of dark matter out there, with the authors suggesting that actually the opposite hypothesis – the idea that all matter must be luminous – was regarded as equally audacious.

The prehistory of dark matter is long and complicated. And it really is prehistory, because it really seems to be only much later that we get to the idea of a genuinely new type of substance, the concept of a material that only interacts with our own through gravity. None of the earliest ideas ever suggested it was anything other than matter which was perfectly normal, just in a state where it was bloody difficult to see. This includes things like Mitchell's "dark stars", which generated photons but which were trapped by the star's massive gravitational field; unseen planets like Neptune that just hadn't been spotted yet; most crucially of all, Einstein and de Sitter's cosmology required dark matter to maintain a flat Universe.

So Zwicky's motivation for dark matter seems to be neither quite that he was the curmudgeonly contrarian of popular lore (though he definitely was a cantankerous git) nor a visionary ahead of his time. Rather he was operating in conditions which are not really directly comparable to the modern scientific view at all. He had three main motivations for expecting dark matter, and these are best understood on their own terms. First, he thought this could explain away the expanding universe, which he viewed as a problem rather than a reality*, through his tired light hypothesis. Second, he knew dark matter could greatly help with the Einstein de Sitter (EdS) model, which Zwicky seems to have favoured. And third, he also thought it could explain the origin of cosmic rays.

* This is much my own view on dark matter. I don't quite understand why many people treat it as apparently obviously problematic and in need for explanation, rather than accepting that this is just what the data shows. I'm simplifying here, but you get the point.

Tired light is well known, and long since refuted, but the problem of cosmic rays is more often forgotten. Nobody understood where these high energy particles were coming from, but since they appeared to be uniform across the sky, they either had to be from something very nearby or very far away indeed. Since there were no obvious nearby sources (that is, within our own small patch of the Milky Way) that might explain them, a cosmic origin was very reasonable. Not knowing about active galactic nuclei and the like, Zwicky's dark matter seemed like a pretty good site for their origin. I have to say I don't quite understand the author's argument as to why Zwicky didn't believe they could originate from anything luminous, but still, if they originated from low density matter filling all of space, this would naturally explain their uniform distribution across the sky.

He also had a good reason to search in clusters. Lord Kelvin had suggested in 1904 that galaxy dynamics could give clues to their total mass independently of their brightness, and Zwicky extended this to clusters (incidentally he also realised that, in partial contradiction to Einstein, a galaxy cluster might be so massive that gravitational lensing there might be so strong as to be detectable).

This meant that Zwicky had very solid grounds for targeting Coma. He doesn't set out his motivations in his own paper, so all this is a contextual reading by the modern authors, but in my view it's a compelling one. Zwicky had good reasons to believe dark matter could be detected in clusters by examining their dynamics, and similarly convincing arguments for its existence which were completely independent of dynamical considerations. It's entirely credible that his observations were done deliberately for this very purpose, and his putative discovery of dark matter wasn't something that he just happened to have stumbled on by chance at all. 

The most interesting aspect of this to me is that dark matter was a prediction of relativity. I've long wondered if an early detection of dark matter would have throttled relativity in the cradle, but the answer from this paper is a clear "no"... but not for reasons we could still justify today. The thing about relativity is that it required dark matter for a flat Universe, but the amount required would have been far larger than in our modern estimates. Today's cosmology uses dark matter to explain the dynamics of galaxies and clusters, but the cosmological constant (and inflation) to keep the universe flat. At the time, it seems the equivalent mass density of the constant wasn't considered to be sufficient to do the job of flattening space. 

And it's important to remember that Zwicky was still making one hell of an extrapolation. From his seven or eight galaxies, he assumed that the rest of the Coma cluster galaxies (hundreds strong) were in stable equilibrium, and thus derive a value for the total mass which happened to be in agreement with the predicted dark matter content needed for the EdS model. But given that even Hubble's constant was not at all well-constrained at the time (Hubble's first value as 500 km/s/Mpc; today we think it's around 71 km/s/Mpc), the error bars on this were massive. So again, relativity predicted dark matter, but not directly, and not for reasons we can now sustain. History turns out to have been more complicated than my counterfactual musings.

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The final part of the paper is more philosophical, considering whether Zwicky's idea really constitutes an ad hoc hypothesis. Certainly it seems not to have been something he just invented on the fly; he may have even been deliberately searching for it. As far as Zwicky's particular idea goes, the answer here is a decisive "no". He may very well have been doing the classic scientific model of hypothesis testing, with his observations set in a clear theoretical framework – and was definitely not trying to save Newtonian gravity from relativity, as has been claimed.

What about ad hoc hypotheses more general ? The paper gives quite a thorough discussion on the different perspectives on these, noting that the existence of Neptune was arguably just such a case. Explaining the orbits of the other planets was difficult without an extra one hitherto undetected, but there was no other good reason to expect the existence of such an object. And of course Neptune did turn out to exist, which completely scuppers the notion that "ad hoc" automatically means "wrong". 

In some extreme views, there are no ad hoc hypotheses at all : they can't be clearly defined and depend too much on circumstance, and since they can turn out to be right, there's no point in distinguishing them from any other hypothesis. The authors here note that both proponents of modern dark matter and those of modified gravity view the other as embracing ad hoc hypotheses in a pejorative sense : to dark matter adherents, modified gravity does nothing except explain rotation curves; to modified gravity researchers, dark matter does nothing except... explain rotation curves. And such hypotheses can be both conservative (seeking to save existing ideas, like Neptune in a Newtonian framework) and progressive (like Zwicky's dark matter in an EdS universe).

I think my take remains that the most important thing for a hypotheses is testability rather than how many ideas it explains. True, we might get a bit suspicious if an idea is invoked to explain a single, unique observable, but this is all we should do, rather than insisting the idea was no good. If you have no other grounds to suspect something, invent it on the fly to explain just one thing, and don't have any reason to expect you'll be able to use your idea elsewhere... then your idea might just be of low overall importance rather than actually wrong. It may, in fact, be perfectly reasonable to suspect the existence of a particular planet or galaxy based on observational evidence, and this may be of locally extreme importance : it just isn't likely to alter anything fundamental.

Where the concept of an ad hoc hypothesis does start to become more problematic, I think, is where it is invoked to explain the fundamental basis of a theory. If you need it to explain a single observation, but without this the whole theory collapses, then this should give you pause for thought. By no means does it suggest the hypothesis is wrong, but it's clearly better if your idea explains multiple things or a general situation rather than just one specific thing.

Neither the modern or Zwicky's concept of dark matter constitute such a thing. Both were and are used to explain multiple lines of evidence. In Zwicky's case, some of those aspects were his own ideas but some were completely independent. In that sense, say the authors, Zwicky should be recognised as the "quantifier, not the discoverer, of dark matter". He used methods both of his own and others devising to explain both his own and others observations ; the idea of dark matter itself was not original to Zwicky. Here irony piles atop irony. His findings were correct but unconvincing, with his paper not cited for 25 years; his value agreed with a theoretical framework which turned out to be completely wrong; his basic technique correct but involving a wild extrapolation. 

In this reading, Zwicky comes through as both a revolutionary and a staunch conservative. He had the best of ideas, he had the worse of ideas. But, pretty decisively, it seems we should give up on any misconception he simply invented dark matter to explain a few errant galaxies. Rather, in this particular case, he seemed to have been doing good science, the best he could do at the time – subsequently revised, but that's exactly what any good scientist can hope for. Zwicky did indeed push the boundaries forward, and if others had paid a bit more attention, the history of cosmology could have been completely different.

Another One Bites The Dust

One of my all-time favourite dark galaxy candidates was discovered by FAST back in 2023. An isolated gas cloud with no obvious optical counterpart, it also had an apparently flat rotation curve. This is the quintessential dark galaxy candidate : small, to be sure, but it would be simply unrealistic to hope for more.

Two small concerns stopped it just short of the Platonic ideal that would convert skeptics into believers. First, its flat rotation curve was marginal, because it wasn't well-resolved by FAST. The 500m telescope is enormous, but to see structure properly, you need something like the multiple-kilometre baselines of the VLA. Second, the optical data of the area wasn't especially deep, and it was always possible that a faint optical counterpart might be lurking there.

Earlier this year came two papers in quick succession, both targeting this same object. The first used deep optical imaging to search for faint stellar emission, while the second used the VLA to better resolve the HI detection itself. Both came to the same conclusion. This isn't a dark galaxy, it's just an unusually faint object.

The first paper is straightforward enough. They pointed a relatively small 1.4m telescope at the gas cloud for a whopping 27 hours to get really deep data, and lo and behold, a galaxy appears ! A small, faint one, to be sure, but it's very clear. It's a little bit offset from the position determined by FAST, but this is entirely consistent with FAST's big beam. And then, just for good measure, they observed this for three hours on a much larger 6m telescope to get the optical redshift... and bingo, it matches that of the HI cloud. So this is definitely the optical counterpart, no ifs or buts.

The odd thing is that the stellar mass of this object is much higher than the upper limit FAST determined could evade detection. Based on the discovery paper, I said that this would almost certainly turn out to be something really interesting, but the stellar mass of the counterpart turns out to be almost exactly ten times greater than what FAST predicted could be there. This makes it unusually faint, but by no means exceptionally so. The authors don't care to venture a guess as to why the upper limit from FAST was so much more optimistic.

The second paper arrives at the same conclusion by a different method. The better resolution of the VLA means they can more accurately locate the centre of the gas cloud. They don't have the same deep optical data as the first team, but by carefully stacking PanSTARRS data, they can do well enough. And lo, the same galaxy emerges from the noise. They estimate the stellar mass to be about half that of the first team, but factor two variations in stellar masses are quite normal at the best of times – and with the noisier optical data this group have to work with, this isn't surprising at all. It's still much more massive than the original upper limit.

The VLA data doesn't show the same flat rotation curve as the FAST data did, but it does clearly show that the gas has ordered motions. That's actually the more relevant factor, demonstrating that it is indeed rotating and so likely stable. So again, it's definitely a galaxy, and a pretty normal one at that.

To be fair it is notably faint. The second team are open to a bit more speculation than the first, suggesting that this might be something similar to the notorious Ultra Diffuse Galaxies but a bit smaller. And it's true that while being a bona fide dark galaxy would have been way cooler, it's still important to study objects like this : they still raise the question of what keep star formation suppressed, albeit not quite so dramatically as originally suggested.

Nor does this rule out the notion of "dark galaxies" more generally. What I think we can rule out, pretty definitively, is the idea of massive dark galaxies. There may very well be absolutely no truly dark galaxies as massive as our own Milky Way (although for sure there may be some very faint ones), or even a fraction of our mass. Okay, sure, in all the unimaginable vastness of the cosmos, there might be one or two hiding somewhere. But as a population, this is something I think we can now safely rule out.

What we can't and shouldn't rule out is the idea of dark minihalos. The reason dark galaxies originally gained traction was to explain the "missing satellite" problem of the Milky Way, where the very smallest dark matter halos never accumulate enough gas to form stars. This would explain the huge discrepancy between theory and observations – and these kinds of dark galaxies are still very much permitted by the data. Indeed, it was only a few theoretical models that allowed for massive dark galaxies at all, so dark satellites remain as plausible now as they did 30 years ago. 

Finding those, however, is turning out to be bloody difficult. The challenge, I think, lies mainly on the theoretical end, in proving whether we really expect any of them to be detectable with radio telescopes at all – and if not, in establishing some other way of verifying their existence. At the moment they feel a little bit unfalsifiable : not to a fatal extent by any means, but to a degree where we should definitely be going "hmm" and doing a good bit of head-scratching. Nothing wrong with that... it wouldn't be research if you knew what you were doing.

An Assemblage of Fluff

I'm happy to report that the refereeing process for my latest paper was one of those instances where I couldn't honestly find anything to complain about. In fact the reviewer provided several papers to cite which were, unusually, all genuinely relevant and interesting. And they weren't even an obvious case of "please cite me !" as so often happens with suggested citations ! Goodness, a reviewer actually providing interesting things from a desire to be helpful, whatever next...

Anyway, if you don't want to read my paper or the linked blog post you don't have to. It's basically a very simple one : we found an Ultra Diffuse Galaxy apparently losing gas in the Virgo Cluster. You might remember another such claim (which I've covered a few times previously, of which this post summarises the current state of affairs) which has proven controversial. This one, I think, should be relatively unambiguous as far as the main claim goes. What will likely cause more of a discussion – if anyone notices it at all – is the assertion that this might, like other UDGs, have a depleted dark matter content.

But what I want to do here is give a bit of a speed summary of the other papers I ended up reading as part of this. The referee seems to be of a background from the world of globular clusters, which is far outside my area of expertise. I tend to not pay enough attention to the studies of globular clusters in UDGs, which is a bit silly because that's a whole other area in which UDGs have proven controversial.

One of the claims in my latest paper was that if our UDG was typical of other UDGs in clusters, this implies that many of those also lack dark matter. The referee pointed out that there are different claims regarding the globular cluster content of field and (galaxy) cluster UDGs, meaning that they likely don't have the same origin... which rather limits the impact of what we can state regarding this one object. This led me on a paper chase down a rabbit hole, and for the sake of not having to write out everyone's names the while time, here are the links and abbreviations :

Forbes+2020 : F20; Lim+2020 : L20; Benavides+2021 : B21; Grishin+2021 : G21; Junais+2022 : JA22; Jones+2023 : JO23; Hartke+2025 : H25;  Sandoval Ascencio+2025 : SA25. 

Right then, with no less than eight different papers to cover, let's do this one thematically. There are a number of issues which most of the authors addressed in different ways, so I'll try and unify what they were all getting at.

Where do they become diffuse ? 

That is, are they born big and fluffy or do they have big fluffiness thrust upon them ? Opinions differ, but here the consensus is reasonably clear. B21 argue that some field UDGs are too isolated for environment to have had much of an effect so they must be intrinsically large objects, saying that infall into a cluster will change other aspects but not size. F20, L20, and JO23 all imply that UDGs must have been large before cluster infall, but only on the circumstantial grounds that the differences between field and cluster UDGs are too stark to allow the one to evolve into the other. Only G21 argues directly against this, saying that modelling shows the removal of gas from UDGs significantly changes their gravitational potential, thus allowing stellar orbits to considerably expand.

I'll go through some of the more detailed arguments below, but I generally agree with the majority here. There are more than enough UDGs detected in the field that there simply doesn't seem to be any real need to invoke environment to explain the size of UDGs. It's entirely possible that some UDGs which now seem isolated lonely hermits were once denizens of more hedonistic environments, but it's not credible that this is true for all or even most of them. 

I do think both B21 and G21 might be overstating things though. Removal of gas certainly does have the capability of making some galaxies more diffuse, but this alone can't possibly explain the majority of isolated UDGs. In short, environment is likely only a contributing factor, and not the dominant one.

Where are they quenched ?

Closely related to where they become diffuse is the question of where they stop forming stars. Broadly, most cluster UDGs are red and dead, whereas many field UDGs are blue, gas rich, and star forming. But there's a big selection effect here : cluster UDGs are easy to spot whereas field ones are much harder. The issue is distance. It's perfectly reasonable to assume that a large population of galaxies which is aligned on the sky with a cluster, and not found in control fields, is at the same distance as that cluster. In the field you don't have any such corroboration. Gas measurements can provide distances, but that leads to the selection effect : if you select by gas, of course you'll find that field UDGs are blue, gas-rich party animals.

Still, overall this picture holds up very well. Hardly any cluster members have been detected with gas, which is not unexpected : it makes complete sense than an infalling UDG will lose its gas and stop forming stars – in fact, it can hardly avoid it. The issue is more whether this can also happen in the field rather than whether it happens in clusters at all. While B21 say that even apparently very isolated, quenched UDGs are probably "backsplash" galaxies (that is, cluster members that escaped), SA25 say they've found at least two UDGs where this is unlikely.

It isn't clear to me why SA25's field-quenched galaxies can't be the same "used to be cool" UDGs that once hung around in rowdy galaxy clusters, as B21 prefer for similar objects. But it's crucial here that B21's conclusions are from simulations, not observations. How, then, could we tell them apart observationally ? How could we say that an isolated UDG was once quenched in a cluster versus in situ ? This is far from obvious. 

Clearly, clusters can and do quench UDGs, but whether they can also quench in the field is not at all settled. The potential mechanism by which this would happen turns out to have very interesting consequences, but we need to examine a few other points before we get back to this.

How do field and cluster globular cluster populations in UDGs differ ?

Globular clusters are a component of galaxies I probably don't know enough about. They're dense little starballs that orbit throughout a galaxy's halo, and as JO23 describe, they're thought to form at the very first stages when a galaxy assembles. Establishing how many GCs a UDG has, though, seems a matter of some difficulty. Most studies appear to do this statistically : they use some selection criteria to see how many GC-like objects they find in a control field, then count how many similar objects they find in close proximity to a UDG. This means that in some cases, the estimated GC count for a galaxy can actually be negative since it might have less GCs than the control field predicts.

The situation is not at all clear. F20 found significant variations in the UDG-GCs within the Coma cluster, with some being especially rich compared to comparable non-cluster galaxies, while others were more typical of the populations seen in dwarf galaxies. Fornax dwarfs appear to have fewer GCs per galaxy, while L20 find that Virgo UDGs have more GCs than Fornax but less than in Coma. 

JO23 go on to make a bold assertion that since gas-rich field UDGs have less GCs than in clusters, this definitively rules out field infall as the major origin of cluster UDGs. To be fair, it's very difficult to see how an infalling UDG could gain globular clusters, as long as the standard paradigm that GCs only form early in the universe is indeed correct. This is a pretty strong argument, but I'd add two major caveats : first, we know star formation can be triggered by the onset of ram pressure stripping, so maybe GC formation is also possible; secondly, less speculatively, I'd like to have much more secure data on the cluster GC population. Pretty much everyone seems to agree on a bimodality of cluster GC populations, with "multiple pathways to UDG formation" being an almost ubiquitous phrase in all of these papers.

What is their total mass ?

This bimodality is strongly evident when it comes to their mass estimates as well. F20, L20, J22, JO23, and SA25 all suggest that at least some UDGs are basically normal galaxies : the extreme tail-end of a population that happens to have a very low surface brightness, but with the same formation mechanism at work. Some of these authors (more than I was expecting) quite strongly support the notion that some UDGs are likely "failed giants", with a total mass comparable to the Milky Way, while allowing other UDGs to be essentially just faint dwarfs. 

JO23 expresses this last option most bullishly, dismissing the interesting claims – and I think here very unfairly – of UDGs lacking in dark matter as being problems of data quality. In this view there's nothing much to explain, since most UDGs would be entirely normal objects, just the extreme end of those with typical characteristics. This is an especially weird and surprising perspective to me, since Jones has been at the forefront of research into so-called "Blue Blobs" : stellar structures which resemble galaxies but appear to lack dark matter !

F20 and others express in my view a more reasonable opinion. Some UDGs are quite likely just extreme dwarfs while others are failed giants, and everyone accepts that multiple formation mechanisms are at work to some extent. It was very surprising to me that only JO23 mentioned the possibility of a dark matter deficit, and then only in critique – but this may reflect the GC-centric bias of these studies.  Because they're relatively easy to detect, GCs have become popular as a proxy to estimate the total mass of UDGs, and this is far easier than trying to measure the dynamics directly. It feels like the overall mood of the community here is geared towards measuring dark matter excess through GCs rather than deficit, which it itself quite interesting. But as L20 point out, this requires extrapolations across four orders of magnitude so maybe this isn't such a good idea.

What effect does environment have ?

J22 run some detailed modelling to see if ram pressure can reproduce the observed properties of cluster UDGs. They find that it can, which isn't that surprising : as you'd expect, it makes them red and dead. Furthermore they find that without this gas-loss based quenching, they can't get results which agree with observations. It seems like ram pressure stripping is very much a key aspect of the evolution of cluster-member UDGs. But JO23, again I think rather unfairly*, criticises this on the grounds of the GC population. The thing is that J22's study was based in Virgo, where from L20 we know that the GC population isn't much like those of other clusters, and we also know that GCs vary considerably even in the same regions. 

* Apologies to Jones, but while I've often told anyone who'll listen about the importance of BBs, I can't say I agree with this particular work all that much.

It's also worth noting that the L20 UDGs agree with J22's scenario quite nicely, being generally redder and more numerous towards the cluster centre, but L20 themselves note that this is in contrast to previous findings. So there's a lot of controversy and complications here, and I wonder if there's a degree of mixing apples with oranges. Sure, not all GC changes can be explained as the result of field infall, but that doesn't invalidate the result that ram pressure is needed to explain other UDG changes – and those GC differences might not apply in Virgo anyway. Oh, and the L20 sample is rather small, just for good measure.

On another front, H25 present the case of a very interesting individual object which might just poke JO23 gently in the eye. They describe a UDG aligned with a stripped ionised gas filament seen from a larger cluster member galaxy, speculating that we're seeing a UDG actually in the process of formation. This object has no GCs of its own, which would definitely be at odds with F20/JO23's claims that cluster UDGs have more GCs than field UDGs. 

Now that's potentially a really cool and weird result, but unfortunately this object is (to my mind)... totally unconvincing. It's perfectly smooth and structureless, with a considerable offset from the gas filament rather than a neat alignment. It's also red, implying it's already been stripped and quenched, but this makes very little sense if it's still in formation or was formed very recently. They suggest dust could explain this, but dust is seldom detected in low surface brightness objects (if it was external dust, this should be directly measurable by how much other objects in the vicinity appear reddened). The age estimate of several gigayears is also at odds with a recent formation, which ought to suggest an age of < 1 Gyr. 

In my view this is no more than a projection effect, a perfectly innocent gas filament happening to appear near the UDG on the sky but without any true association between the two.

What I mainly learned from all of this is that UDGs are complicated.

The overwhelming consensus is that there are many different ways to skin a cat : almost certainly, some UDGs form by different mechanisms than others. The issue is where the balance lies, in establishing if any one scenario is more prevalent than the alternatives, and trying to find some common factors to give us a unified picture of what's going on.

Certainly I think the argument that field and cluster UDGs have different GC populations is a very powerful and important one. Were it the other way around, the situation would be different. It's relatively easy to imagine how a GC-rich field UDG could lose its GCs in a cluster, but gaining them ? Maybe not impossible, but it's hard to see that happening much.

The final interesting point is that both SA25 and JO23 propose a mechanism by which UDGs could be fluffed up even in isolation. They say that since they're extremely diffuse objects, they might experience bursty star formation rather than the more usual stable, continuous variety that occurs in brighter objects. This would periodically redistribute the gas, keeping it at a relatively low density. Every so often it would recollapse to a denser level and trigger another burst of star formation, and the cycle would repeat, eventually blowing out all their gas permanently and quenching them completely. UDGs might even then be a normal phase of evolution in dwarf galaxies : a temporary, transient state rather than a genuine type of object.

Intriguing... but I'm skeptical. If this was the case then I'd have expected simulations to have predicted this well ahead of time, along with the results of an apparent deficit of dark matter. We also don't see especially extended gas discs in field UDGs, nor (to my knowledge) any particular offset between the gas and stars. What few resolved gas observations we do have look like normal, stable, circular-ish discs. 

All this underlines just how far we have to go. We have a great deal of data in some areas, but in others we have a woeful lack of even the basics. We don't have a common framework to decide when a UDG might be a failed giant or a dark matter-deficient object; weirdly, these two very different circumstances appear to result in objects which are optically indistinguishable. The old cliché is in this case emphatically true : more research is needed.

Weaponising dark matter

Stephen Baxter's Xeelee sequence revolves around a war between baryonic and non-baryonic life forms. One memorable sequence features a pulsar being hurled at the Great Attractor (because reasons). Today's paper feels like it could easily fit within such a realm of the gloriously far-fetched, albeit it's not without some reasonable evidence too.

This is just a four-page letter so I'll keep this one very short indeed. Like the last paper, they claim to have found the signature of a very small dark matter halo, but this one's even smaller. The last one was about a billion solar masses, extremely small by galaxian standards but not outrageously so... this one, by contrast, is probably no more than a few tens of millions of solar masses, with a lower limit of just a few thousand.

Such features are certainly predicted in cosmological simulations. Basically, the higher the resolution, the more small dark halos result. But below a certain limit, nobody ever expected to have much chance of ever detecting them, since they'd have so little gravity they'd never attract enough gas to form a single star. And once you add in all the baryonic matter to the simulations (the boring normal matter of stars and gas), presumably most of the smallest ones would be disrupted.

The claim here is they've found a bullet wound in the Milky Way resulting from a collision with one of these minihalos. Actually, again like the last paper, this is not a discovery announcement so much as an independent confirmation by a different method. The original discovery came back in 2017 in the form of a molecular gas cloud with an unexpectedly high line width. 30 km/s is small by the standards of galaxies (the Milky Way would be more like 400 km/s), but with no stars to drive the motion, dynamics have an obvious appeal. Without any other visible material (at least nowhere near enough), dark matter is at least heavily implied.

Here they use Gaia data to look at the velocity of the stars in the vicinity and discover they have a vertical velocity anomaly : in a small region of the disc, the average velocity of the stars perpendicular to the disc drops, even while their dispersion increases. The original CO blob is slap-bang in the middle of this VVA, with the VVA being very much larger than the CO blob. Which would be an awfully suspicious coincidence.

A lump of dark matter colliding with the disc could certainly cause this. Its small size is certainly consistent with a very small halo. But I have no familiarity with stellar dynamics on these scales at all, so I can't tell you how unusual such features are and their figures don't really give much of an indication. They also don't even consider other explanations, which I suppose is fair given the limited available space, but it would have been nice to have mentioned something (besides the obvious impossibility of stellar winds and the like, there apparently being no stars here). And how much such features would we expect to find, if these minihalos exist in the numbers predicted by simulations ? Finally, their tie-in to Ultra Compact Galaxies more generally – which are largely thought to be the stripped cores of more massive galaxies – is just too speculative even for a letter.

In short, it's definitely a very interesting feature to report, but it's going to take a lot more work to say anything definitive about what it actually is. 

A dark RELHIC of an earlier age

The last time I tried to count the number of times objects had been claimed to be the first dark galaxy candidates, I stopped at ten because I got bored. Today's paper adds another one to the list.

To be fair, not all dark galaxy claims are equal. Some would say a galaxy only counts as dark if it really has no stars at all, others that it just needs to be sufficiently dominated by its gas and/or dark matter. Others would insist that it has to have certain dynamics or only be found in the nearby universe*. Most would probably demand it had a primordial origin rather than just being stripped out of a galaxy, but not everyone would agree. So that list of ten could very plausibly be extended or contracted considerably.

* Pretty much everyone agrees that galaxies started off as dark, so we accept that dark galaxies did exist at one point. The controversy is over whether any still remain dark today.

This paper concerns a very particular type of dark galaxy they annoyingly dub a RELHIC. Why annoying ? Because we also have radio relics, which are completely different beasts : they're incomparably larger and more diffuse, and have little or no direct relation to individual galaxies. These objects, on the other hand, are Reionisation Limited HI Clouds, a term coined by one of the authors that I'd urge them to stop using. 

But no matter. What they report on is a very interesting object that in some ways is the sort of dark galaxy candidate everyone wants to find. One of the major reasons to suppose such objects exist at all is that they would solve the long-standing missing satellite problem, whereby simulations produce far more galaxies than are actually observed. The idea is that while the physics of gravity is pretty simple, the physics of star formation is anything but. So maybe these objects do exist, it's just that they've never formed stars. This is now the widely-accepted explanation for missing satellites – indeed, arguably there isn't such a problem at all any more, as (in some models) the only such "dark galaxies" are now so small and so lacking in stars that we wouldn't detect them.

One of the complexities of the physics behind these is that of the Epoch of Reionisation. The first stars as thought to have been super-powerful monsters powerful enough to ionise most of the gas in the early Universe, heating it to the point where it would be driven out of the smallest galaxies completely. Models show that below a critical mass threshold, galaxies would lose all of their gas and never form any stars at all. It's not quite a sharp cut-off, with some near or slightly above the threshold able to form some stars before reionisation brought the process to a permanent halt, but it's close.

Such objects are thought to be extremely difficult to find. Their HI masses should be only a few million times the mass of the sun, about a hundred times less than a typical dwarf galaxy. And their line widths might be only 20 km/s or less, barely wider than the HI line itself . In principle these objects could be almost numberless, just bloody hard to spot. In contrast, most dark galaxy candidates that hit the headlines are much bigger, and usually by the author's own admissions fairly exceptional – massive dark hulks that are relatively easy to find despite being so rare as to indicate little or nothing about how most galaxies form.

Here the authors present a candidate discovered with China's mighty FAST telescope. In contrast to this awful RELHIC term, I can't fault them for the name of this particular object : Cloud 9. Yes, really. Apparently this was first reported in 2023 but I seem to have missed that paper when it came out. 

Here they report on deep Hubble images and confirm that it's really, really dark, with no more than a few thousand solar masses of stars against it's million or so of gas. That, together with its line width of just 12 km/s (!) and small size (1.4 kpc radius), with an estimated halo mass that's extraordinarily close to the mass threshold, make it a compelling RELHIC candidate. It's certainly one of the darkest objects ever found, which is always a pretty cool thing to find.

But just how good, exactly ? My verdict would be... yeah, this one's pretty interesting. Is it definite ? By no means. But it's a good candidate, and absolutely needed to be published.

Glancing at the discovery paper, it seems that Cloud 9 is a little over 100 kpc from M94 itself, with HI clouds closer to the galaxy that are clearly some form of debris. 100 kpc is quite far, but only a few times the size of a large galaxy, and certainly there are many extended streams known which are much larger than this. So this cloud could be a leftover far-flung bit of debris as well, but like Cloud 6 in Leo, it doesn't really fit the general pattern of the other clouds. 

A perhaps more serious difficulty would be that estimating the total mass of the feature must be extraordinarily difficult. HI tends to be found at ~10,000 K, corresponding to a line width of 10 km/s. A width of 12 km/s tells you pretty much nothing beyond the thermal state of the gas, so inferring its dynamics from this is... well, my worry is that you simply can't get a meaningful estimate when things get this narrow. Even if there was no dark matter here at all, the line width wouldn't get much narrower because this is about as narrow as the line can get. It's not a matter of better data in this case : nothing will help, at least not very much.

Another issue is the question of how long the cloud could survive, and conversely, how long it's been in existence. Currently its gas density is well below the threshold for star formation. Assuming it began life so small that the density would reach the threshold (so as to have always been dark), and given its expansion velocity and small size, it would have taken perhaps a hundred million years to reach its present size (without dark matter, as expected if it's just debris). In a another hundred megayears or so it'll double its and likely be undetectable. So if it's debris, we're detecting it at an unusual point in its existence, but sadly this doesn't constrain things too much.

I think this is a case where what's needed is a good set of simulations, especially given the timing constraints from the size of the cloud. What kind of interactions could affect M94 that would produce debris like this ? How often are such things formed, and are those simulations compatible with all the other data of the system ? What happens to existing minihalo RELHICS like this one in a system like this, where there's clear evidence that M94 experienced a merger – can they survive in such a place ?

This one's going to take a lot of work to answer. It would be easy to dismiss this as just another bit of HI fluff... and it might be. But it's so close to what we expect minihalos to be like that the workload might be worth it. And perhaps, just maybe, some other clouds already lurking in the data aren't the boring bits of debris we all thought they were.

The galaxies that seemed magical are actually just very lazy

Today, two papers for the price of one ! The one is a sequel to the other and they're both quite technical, so let's knock off two birds with one stone in the bush and other mixed metaphors. Paper I is here and paper II is here.

The papers in essence address two related questions on Ultra Diffuse Galaxies – the big faint fuzzy things that often seem to lack dark matter, which I've covered here ad nauseum. Neither paper much addresses the dynamics (i.e. total mass) of the objects, but rather the other fun aspect of these galaxies : why are they so wretchedly bad at forming stars ? Many of them have tonnes of gas, so why aren't they forming stars like normal galaxies are ? Why are they so large and yet so faint ?

The first paper deals with the basics. It tries to determine if the star formation efficiency of UDGs really is weirdly low, or if this is only a selection or measurement effect. Spoiler alert : it really is low. So the second paper then address why this might be – is there something missing in our basic model of star formation, or is it just due to the peculiars and particulars of of these particularly peculiar systems ?

Paper I begins by collecting a sample of 22 UDGs and 35 more typical dwarfs of comparable mass.  The UDGs all have atomic HI gas detections, though low resolution so essentially all we know is the mass of gas, nothing about its structure. But it seems that at first glance, UDGs are indeed of systematically lower star formation efficiencies : their star formation rate is less than that of other galaxies of similar gas masses, and given their stellar masses they have more gas than expected. Both of these effects are modest though. It's quite apparent that the population as a whole is systematically offset from the rest, but they're all still within the general scatter. Interestingly, they also note that the properties of the HUDGs (HI-detected UDGs) aren't much affected by environment.

The first question they tackle is whether these objects are really different in terms of their gas content, or if this is just a selection effect. That is, the HI observations might be limiting what can be detected at all. It could be that those of lower gas fractions do exist, it's just that the data isn't sensitive enough to show them. But they find that's not the case : they should be able to find considerably less gassy-objects, so these don't seem to exist at all. These HUDGs are not the tip of a less-gassy iceberg.

Next, their main topic. While the average star formation efficiency of HUDGs appears low, this could just be due to the statistics from using the total gas mass and stellar mass, which smooths over the whole structure of the galaxy. It could be that the star formation efficiency is actually quite normal, just restricted in area. For example the gas could all be concentrated in the centre and forming stars pretty normally, but when averaged over the whole galaxy, this would be "washed out" and it would look like the galaxy was rubbish at forming stars. Overall, they would be, but locally, they wouldn't be that bad. This would imply a significant population of older stars outside the gas-dominated regions.

To test this they use spectral energy distribution fitting. Basically what this does is use many different data points across the optical, UV and IR spectrum to estimate the stellar ages as accurately as possible – this is the best we can do in terms of estimating a galaxy's star formation history. Ideally we'd also like to have resolved measurements of the HI, which sadly they don't have here. But they do SED-fitting for many different points per galaxy, so they can see if the low SFE is something that varies throughout each object or if it's low everywhere.

I'll skip over the details of the SED fitting because I don't understand any of it; I only note that they stress they aren't constructing detailed star formation histories here, just enough to answer their main questions. Their main result is that the low SFE is true on all scales from big to small. It's not just that the gas could be more extended, it's that UDGs are bad at forming stars full stop, even if the gas density is higher. While UDGs are large given their stellar masses, they're not especially large considering their HI masses.

There are a lot of different scaling relations to juggle here, but the end result is very simple : UDGs aren't good at converting gas to stars even when they've got plenty of it. The obvious next question is, of course, why are they so bad at this ? For this we need the sequel.

Paper II takes quite a different approach and is all about modelling. There's a really popular and widely-used relation between the surface density of gas and its star formation activity, but there have been indications for many years that we ought to be using the true, volumetric (3D) density instead. This is harder to measure directly so some assumptions have to be made, but it can be done. 

Here they consider a particular version of the volumetric star formation law that depends on the different components of a galaxy together : the gravity from the atomic and molecular gas, the stars, and the dark matter. All have different gravitational contributions. For instance in the centre of a galaxy everything might be extremely dense, whereas further out their might still be lots of atomic gas and stars but less molecular gas, and on the very outskirts only atomic gas and dark matter. So even if the atomic gas has the same surface density, it may need the extra gravity of the stars to help pull it together and collapse.

Again I shall spare you the technical details of the model. This time my essential note is that they consider UDGs to be rather more dark matter dominated than ordinary galaxies, which flies against the prevailing winds in the last few years. More on that in a moment.

They find that this more complex model... works ! It can explain the star formation rates of both normal galaxies and UDGs under very reasonable assumptions : there's no need for any additional physics, no weird mechanism or alien interference that suppresses the star formation. There are many uncertainties, but their sensible default assumptions are enough to give a good result without any sort of fine-tuning needed. So UDGs are, in a sense, pretty normal.

They also find that the model isn't very sensitive to how much molecular gas the UDGs are assumed to contain. That's bad news for anyone trying to detect their molecular content : essentially it implies they could well have very little of it. They can even estimate just how much molecular gas they expect them to have, given their estimated star formation rates and the (rather surprisingly but repeatedly established) independent finding that molecular gas generally has a constant depletion timescale of about 2 Gyr. In short, their conclusion is that detecting the molecular component will be Bloody Difficult. Dwarf galaxies are already hard to detect, but UDGs will be even worse.

What about that choice of a relatively massive dark matter halo ? They explore this, and rather surprisingly it doesn't matter much. It seems that the density of the dark matter is anyway assumed to be low so that reducing the total mass doesn't make a great deal of difference. In fact this can give slightly better agreement with the measured star formation activity, but unfortunately, they say there are too many other uncertainties to say if their model prefers no dark matter at all to a normal mass halo.

So, there we have it. There's no need for any weird physics here : star formation in UDGs apparently follows the same laws as in every other galaxy. The difference isn't so much the gas content itself as the state it happens to find itself in, just as a puppy can be an energetic ball with the madness of a thousand caffeinated suns or the sleepiest thing since Slothy the Sleepy Sloth swallowed an entire bottle of Nytol after a mug of warm milk in a comfy armchair. 

Does this mean UDGs aren't weird at all though ? Nope ! You might remember that there were previous claims that UDGs are actually just normal galaxies if you redefine how to measure their radius. That was true, but doesn't mean that the other radius estimates were wrong : it still points to UDGs being anomalous, just not in the way we'd understood. 

Here we still don't know what sets the initial conditions of UDGs. Why do they start out so differently to normal galaxies ? Is it their dark matter halos (or lack thereof) and if so, is this compatible with our simulations of the sort of halos we expect to actually exist ? And how come their globular cluster populations appear to be very different to other galaxies ?

We still don't know. We do know that UDGs are weird, but as to whether they're pointing to a deep flaw in our models or just some incompleteness or other... the jury's out. Or more accurately, the trial hasn't even started yet : we need more evidence before we can even really begin. But at least what this paper adds is what evidence we should go after. If UDGs are found to have chonks of molecular gas, that would falsify their model straight away. If they're not, we keep investigating.

Signal boost

One man's trash is another man's treasure, or so the old saying goes. And in astronomy, one man's signal is another man's noise. 

The classic example of this is dust in the Milky Way. If you're interested in dust, then you're a deeply weird person... or just interested in star formation. Dust "grains", which are actually about the size of smoke particles, are thought to be critical sites for star formation because they allow atomic gas to lose energy, cool, and collide with each other to form molecular gas. This is much denser than atomic gas, so eventually this can lead to the cloud collapsing to form a star.

But if you're less of a weirdo, dust just gets in the way. It ruins our majestic sky by blocking our view of all the stars, especially along the plane of the disc of the Galaxy. Some regions are much worse than others, but it's present at some level pretty much everywhere across the sky.

In radio astronomy we have a much more subtle and interesting problem. Of course we always want our observations to be as deep and sensitive as possible. But sometimes, it turns out, the noise in our data can actually be to our advantage – though it comes at a price.

Consider a typical spectrum of a galaxy as detected in the HI line. If you aren't familiar with this, take a look at my webpage if you want details. Basically, it shows us how bright the gas from a galaxy is emitting (that is, how dense it is) at any particular velocity. Even without knowing this rudimentary bit of information though, you can probably immediately identify the feature of interest in the signal :

All the spectra shown in this post are artificial, generated with a simple online code you can use yourself here.

We don't need to worry here about why the signal from the galaxy has the particular structure that it does. No, what I want to talk about today is the noise. That's those random variations outside the big bright bit in the middle.

This example shows a pretty nice detection. It's easy to see exactly where the profile of the galaxy ends and the noise begins. But even within the galaxy, you can see those variations are still present : they're just lifted up to higher values by the flux from the galaxy. Basically the galaxy's signal is simply added to the noise.

Now if you still have an analogue radio, you'll know that if you don't get the tuning just right, you'll hear the sounds from your station but only against a loud and annoying background hiss. The worse the tuning, the worse the noise. So you might well think that the following claim is more than a little dubious :

Fainter signals can be easier to detect in noisier data.

So counterintuitive is this that one referee said it "makes no sense whatsoever", doubling down to label it "bizarre" and "not just counterintuitive, but nonsensical".

This is wrong. I'll point out that the claim comes not from me but from Virginia Kilborn's PhD thesis  (now a senior professor). So how does it work ?

The answer is actually very simple : signal is added to noise. That is, regardless of how noisy the data is, the signal from the galaxy is still there. Let me try and do this one illustratively. Suppose we have a pure signal, completely devoid of noise, and for argument's stake we'll give it a top-hat profile (about a quarter of galaxies have this shape, so this isn't anything unusual) :

The "S/N" axis measures the signal to noise, a measure of how bright things look given the sensitivity of the data. The numbers in this case are garbage because I set the noise to zero.

Now let's add it to two different sets of noise, purely random (Gaussian), of exactly the same statistical strength but just different in their exact channel-to-channel values :

Note that even with this purely random noise, you can still see apparent ripples and variations in the baseline outside the source : even random noise, to the human eye, looks structured.

Oh ! What happened there ? Why is the second signal so much clearer than the first ? You can still see the first one, to be sure, but it's marginal, and could easily be mistaken for some weird structure in the baseline. The second isn't great either, but it looks a lot better than the first.

Noise is typically random. That means that some parts of it will have bits of higher flux while other parts will be a bit lower. If we add our signal to the higher flux bits, the total apparent flux in our source gets higher. That is, the real flux in our source obviously doesn't change, but what we would measure would be greater than if the noise wasn't there. And of course the opposite could happen too : we could have noise dimming that makes the source harder to detect as well as easier.

Noise boosting (shown in the carefully-chosen example above), on the other hand, is no less important but far less expected. Every once in a while, a faint signal will happen to align with some bright parts of the noise and turn a marginal signal into a clearer one. This doesn't really work in the sorts of audio radio signals you get on a household radio set as these are much too complex, but the signals of a galaxy are a good deal simpler. And all we need to detect them is (for this basic example at least) pure flux, which the noise can readily provide.

(As an aside, you might notice that the the actual peak levels in these cases aren't much different, though the average level inside the source profile is higher in the second case. While peak levels most certainly can be affected by noise boosting and dimming, what's absolutely crucial here is what detection method we're using to find the signals. I'll return to this below.)

Of course, there are limits to this : it will only work for signals which are comparable in strength to the noise. As the noise level gets higher, the random variations will increasingly tend to "wash out" our signal. Now the level of the signals we can receive varies hugely depending on the nature of our data set, but the signal-to-noise ratios (S/N or sometimes SNR) tend to be fairly constant. That is, a signal which is ten times the typical noise value (the rms) has the same statistical significance in any data set, but the actual flux value it corresponds to can be totally different. But expressing signal strength in terms of the noise level makes things very convenient : a five sigma (5σ) source just means something that's five times brighter than the typical noise level.

So suppose we have a 2σ source which happens to align with a 3σ peak in the noise : bam, we've got ourselves a quite respectable 5σ detection*. But if we keep the flux level of the signal we're adding the same and increase the noise level, then the ratio of S/N will go down. Instead of adding 2σ to 3σ, we'll be adding ever lower and lower values : the "sigma level" of the noise won't change, but that of the signal certainly will. Pretty quickly we won't be shifting that 3σ peak from the noise by any appreciable degree. We'll be adding the same flux value but to an ever-greater starting level. 

* Sometimes five sigma is quoted as a sort of scientifically universal gold-standard discovery threshold. This is simply not true at all, because if you have enough data, you'll get that level of signal just by chance alone. Far more importantly, the noise in real data is often far from being purely random, so choosing a robust discovery threshold requires a good knowledge of the characteristics of the data set.

As a possibly pointless analogy, consider lions. If you go from having no lions to one lion, you've just put yourself in infinitely more danger. If you add a second lion you're in even more trouble. But if you've got ten lions and add one more, you won't really notice the difference.

"But hang on," you might say, "surely that means that your earlier claim that fainter signals are more detectable in nosier data can't possibly be correct ?". A perfectly valid question ! The answer is that it depends on how we go about detecting the signals. The details are endless, but two basic techniques are to search for either a S/N ratio or a simple flux threshold. These can give very different results to each other.

Now if you use S/N, which is generally a good idea, then indeed signals of lower flux levels generally don't do well in increasingly nosier data because of the ever-smaller relative increase in the signal. And of course, the effect of the noise to not merely obscure but actually suppress the signal will get ever greater, since there's just as much chance of aligning with a low-value region of the noise as a high-value region. 

But S/N is not the only way of detecting signals. You might opt instead to use a simple flux threshold instead : it's computationally cheaper, easier to program, and most importantly of all it gives you more physically meaningful results. If you do it that way, then it's a different story. When you add a signal to noise the flux level always increases, making it much easier to push the flux above your detection threshold by this method. Which makes noise boosting very much easier to explain.

Note here the change of axes values compared to the previous examples. All I did was increase the noise level by ~25% and here both peak flux and S/N levels have increased. It might not look easier to detect visually, but statistically, by some measures this one is more significant than the previous cases !

Even more interesting is the so-called Eddington bias. What this means is that any survey will tend to overestimate the flux of its weakest signals : those signals which are so faint that they can only be detected at all thanks to chance alignments with the noise. In this case, that when someone comes along and does a deeper survey, they'll often find that those sources have less flux than reported in the earlier, less-sensitive data.

There are plenty of other subtleties. The signal might not need to be perfectly superimposed on a noise peak : if it's merely adjacent to it, that can create the appearance of a wider, brighter signal which can be easier to detect to some algorithms (and people !). And of course while we'd like noise to be perfectly uniform and random, this isn't always the case. Importantly, the rms value doesn't tell us anything at all about the coherency of structures in the data, as so powerfully shown by the ferocious Datasaurus. 

For the eye the effects of this can be extremely complex, and are poorly understood in astronomy. If you have very few coherent noise structures, for example, you might think this nice clean background would make fainter structures easier to spot. But actually, I have some tentative evidence that this isn't always the case : the eye can be lulled into a false sense of emptiness, whereas if there are a few obvious structures to attract attention, you start to believe that things are present so you're more likely to identify structures. No doubt if your data was dominated by structures then the eye would, in effect, perceive them as background noise again and the effect would diminish, but this is something that needs more investigation. My guess is there's a zone in which you have enough to encourage a search but few enough that they don't obscure the view. 

Ultimately, getting deeper data is always the better option. For every source noise-boosted to detectability, there'll be another which is suppressed and hidden. But this explains very neatly that supposedly "nonsensical" result*, especially if your source-finding routine is based on peak flux : of course if you add signal to noise and have the same flux threshold in your search, you're more likely to find the fainter signals in the nosier data... up to a point. Set your threshold too low and you'll just find spurious detections galore, but hit the sweet spot and you'll find signals you otherwise couldn't.

* Kudos to the referee for accepting the explanation; I never heard of any of this until a couple of years ago either. Extragalactic astronomy is full of stuff which isn't that difficult but seems feckin' confusing when you first encounter it because it isn't formally taught in any lectures !

There's nothing weird about noise boosting then. Mathematically it makes complete sense. But when you first hear about it it sounds perplexing, which just goes to show how deceptively simple radio astronomy can be. Noise boosting, at least at a basic level, is quite simple, but simple isn't the same as intuitive.

ChatGPT-5 versus... DeepSeek

My excitement for ChatGPT-5 continues to defy the Will Of The Internet. Sod y'all, this is feckin' awesome ! This is the upgrade I've been waiting for.

It seems only fair to continue testing it, however. It performed extremely well against my own knowledge, but how does it compare to other chatbots ? After all, there's not much point in me getting all excited for this one in particular if others are actually doing comparably well.

I was intrigued by this article which pits it against DeepSeek, mainly in more real-life situations than what I've been testing. To save you a click, the "clear winner" was DeepSeek, with the unexpected proviso that GPT-5 is better at creativity and explanations – which again is in defiance of the judgement of reddit, who think that GPT-5 is absolutely dreadful at this sort of thing.

Before I report my own science-based testing, some of the author's preferences in the AI responses are... questionable. In brief :

1. Logic puzzle : I find GPT-5's response much clearer here. DS's longer, "different interpretations" of a really very simple "puzzle" only add unnecessary confusion.
2. Mathematics : I tend to agree with the author that DS gives more of a tutorial in its response, though GPT-5's walkthrough is easier to follow. Neither was asked to teach the general case, however, only a specific example. I'd call this one a dead heat.
3. Project planning : Seems like a hopelessly vague prompt to me. The output doesn't seem meaningful.
4. Budgeting : I tend to agree that DS is slightly better here, but by a whisker : it's more instructive. Purely a personality difference though, not a matter of actual content.
5. Parenting : They seem exactly equal to me. No idea why the author prefers DS or thinks GPT-5 is "less organised".
6. Lunches : Not sure what the author plans to do with half a banana, nor do they show the whole display so it's hard to check this one. As it is, I'd agree DS is better here for giving better instructions, but I can't check if it really stayed in the budget or not.
7. Stories : A toss-up; very subjective.
8. Culture : I agree with the author that GPT-5 is clearly better; DS's list isn't a good summary in this case.
9. Social media : Both are crap. DS might be better but it's a case of shit sandwich or shit hotdog.

So while the author prefers DS in seven of the nine tests, I'd say that only seven responses have meaningful prompts with enough output shown for a fair evaluation. I prefer DS only once (maybe twice in the last case), GPT-5 twice, and find they're not differentiable in the other cases. Certainly not a clear winner for anyone.

I should add that I've rather gone off DeepSeek some time ago. It can give good, insightful results (for a while I strongly preferred it to ChatGPT), but it's unreliable – both in terms of its server and its output. It can also over-think the problem, and though being able to see its reasoning can be helpful (sometimes this can be better than the final output), it also means there's a lot of content to wade through. This becomes tiresome and counterproductive.

So for this test what I thought I would do is try to ignore the reasoning and just compare the final outputs. I'm not going to go to the same lengths as I did when testing GPT-5 against my own reading of papers. Instead I'll just ask DS some of the same questions I already recently asked GPT-5 (in-anger, real use cases) and compare the initial responses.

1) Effects of ram pressure stripping on the line width of a galaxy

Suppose a galaxy losing gas through ram-pressure stripping, with the angle to the wind being close to edge-on. What, if anything, is the expected effect on its HI line width ? Is it likely to reduce, increase, or not change the measured line width ? Consider the cases of moderate to strong stripping, corresponding to deficiencies of 0.5 and 0.7 respectively. Please also provide relevant citations, if possible.

Both agree that the width will decrease, but they disagree somewhat by how much. DeepSeek raised the important issue of turbulence, but that's it's only point in its favour : most of its numerous citations weren't especially relevant and certainly weren't pertinent to the specific claims it was making. Far worse was that it got "edge-on" completely wrong, taking this to mean exactly the opposite of how the term is used. GPT-5 gave a tighter response which was much more focused on the problem and with much better citations.

Winner : GPT-5, easily.

2) How do SMUDGES get their measurements ?

Hi ! I have a question about the SMUDGES catalogue of Ultra Diffuse Galaxies. I'd like to know how they compute their reported magnitude values. Does this involve fitting a surface brightness profile or do they just do aperture photometry ? If the later, how to they do they set the aperture size ? If the former, are they reporting total integrated magnitude within some radius or are they extrapolating ?

GPT-5 couldn't figure this out from web-based results so I had to feed it the paper. Once I did that it gave a concise, clear explanation. DS got the result correct using a web search with no uploads needed, and its explanation was longer but easier to follow. On the other hand, GPT-5 also came with the significant caveat that SMUDGES actually report both values, which is not correct.

Winner : DS for accuracy. GPT-5 was clearer, and it got the main point right, but it hallucinated a caveat (though it corrected this on asking it where in the paper that the second value was given) and made the answer more complicated than it needed to be.

3) Integrating the light in a galaxy

About the Sersic profile... suppose I have a galaxy with a Sersic index of 0.51. How much larger than the effective radius would I have to go to enclose at least 98% of the light ?

Both models gave the same answer, but GPT-5 took 38 seconds whereas DS took more than ten minutes (!). The explanations each model provided were equally unclear to me as a non-mathematician.

Winner : GPT-5 for sheer speed.

4) The safety of HIP2FITS

I'm curious about the HIPS2FITS service of astroquery. I have code that seems to work very well at extracting data from different surveys using a specified pixel scale, field of view, coordinates etc. What I'd like to know about is how the pixel values are rescaled when the pixel resolution is different from the standard survey data. In particular, I want to know if aperture photometry in DS9 will still be accurate, i.e. if the sum of the flux values within the same aperture will still be approximately the same. Presumably it can't be identical since now region will, by definition, enclose a different area if the pixel scale has changed, but will it be close if the scale is not too different ? When I requested a substantially different pixel scale for SDSS data (10" per pixel rather than the nominal 0.4") I got a very different result using the same aperture. Is this something that can be accounted for e.g. by changing the units in the FITS header, or by specifying some other parameter in astroquery (or by post-processing the result in some other way) ? Or should I always ensure I only use the standard pixel scale if I want accurate photometry ?

Here the answers are very different. GPT-5 says a straightforward no, you can't do photometry on the data and it's not a simply matter to correct it due to the regridding (though if the scale change is small, this is possible). DS also says no, but that it is a relatively simple matter of correction for the adjusted area – but recommends keeping the original scale whenever possible. Its suggested correction was, however, likely nonsense. It also said weird things like "even for the same source" and gave various other caveats that felt incoherent.

Winner : GPT-5 for clarity. 

5) How are stellar masses calculated ?

I'm curious about how galaxy stellar masses are estimated from photometric measurements. For example, I've seen several different recipes using, say, g-i or g-r colours, and I notice that they can give quite different result for the same object. How are these recipes derived, and how accurate are they ?

Both bots gave extremely similar answers here with no obvious major discrepancies. I couldn't honestly say I preferred either answer.

Winner : dead heat.

6) Star formation from GALEX

How can I quantitatively estimate the star formation rate of a galaxy using SDSS and GALEX data ?

This was an extremely simple prompt and both models gave similar answers. GPT-5 gave slightly more useable answers with more explanations; DS was a little less clear as to what was going on.

Winner : GPT-5, but it was close (and with the caveat that I haven't checked in detail as the responses were both quite long).

7) Analysing the large scale environment

I've got a student investigating an HI data cube of the Leo Group. We've already studied the Leo Ring volume and he's extending our study into the background volume, ~2.000 - 20,000 km/s. We'd like to be able to characterise the environment of our detections, i.e. to say if they're in any major known groups, clusters, or large-scale structures such as filaments. What's the best approach to do this ? I guess we can simply search for groups and clusters in NED, but maybe there's a better way (also this wouldn't tell us about the larger stuff). And we'd like to know any vital information about such structures, e.g. if something is particularly well-known for any reason.

For the only time in these tests, DS complained its server was busy. On a second attempt it gave a response which was pretty basic and not very helpful; "you can look up this data set" or "use this software", or worse, suggesting a comparison to simulations. GPT-5 gave much more helpful citations and guided instructions in what to do with our own data and how we could compare it to other catalogues. 

Winner : GPT-5, easily.

The verdict : ChatGPT-5 won five of the seven tests. DS only won once, with the other being a dead heat. Even leaving aside that GPT-5 had a narrow victory in one case, GPT-5 is the clickbaity "clear winner" here.

To be fair, I'd switched to DeepSeek because it was giving me better discussions than the contemporary model of ChatGPT, even when they first added reasoning. It's by no means a bad model, but it's unreliable compared to GPT-5. Its citations are frequently of dubious relevance, it seems to hallucinate more (the example of GPT-5 hallucinating here is the only example I've found of this so far*, and that was minor), and if you want its best output you'll have to wade through an awful lot of its reasoning processes. It's also slower than GPT-5, sometimes by an order of magnitude. And I've also found that DeepSeek rejects my uploads as violating policies even when there's nothing offensive in them; it won't even discuss them at all but just shuts down the discussion immediately.

* Excluding when you ask for it to analyse a paper by giving it a link rather than uploading. In that case it becomes worse than useless, so never do this.

Still, I have to admit that this does tend to curb my enthusiasm for GPT-5, but only a little. DeepSeek's answers were generally better in comparison to GTP-5 than I was expecting; DeepSeek gets most of the way there when it works. If GPT-5 is a revolution compared to previous OpenAI offerings, then it's only an incremental upgrade compared to DeepSeek. An important one, to be sure, but nevertheless incremental.

On the other hand, it's all about thresholds : how you cross them doesn't matter nearly as much as the fact you've crossed them at all. An increment which gets you across the line is just as useful as if you got there from a standing start. And if DeepSeek gets most of the way there most of the them, GPT-5 gets even further almost all of the time. With only one minor failure here compared to DeekSeek's two majors, the effect is non-linear. Even if GPT-5 doesn't drastically improve upon DeepSeek, it does so by more than enough I now see no point in using DeepSeek at all. 

That about wraps up the LLM-testing for the foreseeable future. Now back to the usual posts in which I explain science to the masses... whether I do this better than the chatbots I leave to readers to decide.

ChatGPT-5 Versus Me

It's time for another round of evaluating whether ChatGPT is actually helpful for astronomical research.

My previous experiments can be found here, here, and here. The first two links looked at how well ChatGPT and Bing performed when analysing papers I myself know very well, with the upshot being an extreme case of hit-and-miss : Occasional flashes of genuine brilliance wrapped in large doses of mediocrity and sprinkled with total rubbish, to quote myself. All conversations had at least one serious flaw (though arguably in one case, which was factually and scientifically perfect but had crippling format errors).

The third link tested ChatGPT's vision analysis by trying to get it to do source extraction, which was a flat-out failure. Fortunately there have been other tests on this which show it does pretty badly in more typical situations as well, so I'm not going to bother redoing this.

With the release of ChatGPT-5, however, I do want to redo the analysis of papers. If I can have ChatGPT give me reliable scientific assessments of papers, that's potentially a big help in a number of ways, at the very least in determining if something is going to be worth my time to read in full. For this one I picked a new selection of papers as my last tests were a couple of years ago, and I can't claim I remember all their details as well as I did. 

Because all the papers cover different topics, there isn't really a good way to standardise the queries. So these tests are designed to mimic how I'd use it in anger, beginning with a standardised query but then allowing more free-ranging, exploratory queries. There's no need for any great numerical precision here, but if I can establish even roughly how often GPT-5 produces a result which is catastrophically wrong or useless, that's useful information.

I began each discussion with a fairly broad request :

I'd like a short summary of the paper's major findings, an evaluation of its scientific importance and implications, what you think the major weaknesses (if any) might be and how they could be addressed. I might then ask you more detailed, specific questions. Accuracy is paramount here, so please draw your information directly from the paper whenever possible – specify your sources if you need to use another reference.

Later I modified this to stress I was interested in the strengths and weaknesses of the scientific interpretation as well as methodology, as GPT-5 seemed to get a little hung up on generic issues – number of sources, sensitivity, that sort of thing. I followed up the general summaries with specific questions tailored to each individual paper as to what they contained and where, this being a severe problem for earlier versions. At no point did I try to deliberately break it – I only tried to use it.

Below, you can find my summaries of the results of discussions about five papers together with links to all of the conversations.

0) To Mine Own Research Be True ?

But first, a couple of examples where I can't share the conversations because they involve current, potentially publishable research (I gave some initial comments already here). I decided to really start at the deep end with a query I've tried many times with ChatGPT previously and got very little out of it : to have it help with a current paper I'm writing, asking it to assess the merits and problems alike – essentially acting as a mock reviewer. 

* Of which the management, like the rest of us, is generally sensible about such things. We all recognise the dangers of hallucinations, the usefulness and limitations of AI-generated code, etc. Nobody here is a fanboy nor of the anti-AI evangelical sort.

Previously I'd found it to be very disappointing at this kind of task. It tends to get hung up on minutia, not really addressing wider scientific points at all. For example, if you asked it for which bits should be cut, it might pick out the odd word or sentence or two, but it wouldn't say if a whole section is a digression from the main topic. It didn't think at scale, so to speak. It's hard to describe precisely but it felt like it has no understanding of the wider context at all.; it discussed details, not science. It wasn't that using it for evaluations was of no value whatsoever, but it was certainly questionable whether it was a productive use of one's time.

With the current paper I have in draft, ChatGPT-5's response was world's apart from its previous meagre offerings. It described itself as playing the role of a "constructively horrible" reviewer (its own choice of phrase) and it did that, I have to say, genuinely very well. Its tone was supportive but not sycophantic. It suggested highly pertinent scientific critiques, such as the discussion on the distance of a galaxy – which is crucial for the interpretation in this case – being too limited and alternatives being fully compatible with the data. It told me when I was being over-confident in phrasing, gave accurate indications of where I was overly-repetitive, and came up with perfectly sensible, plausible interpretations of the same data. 

Even its numbers were, remarkably, actually accurate* (unlike others I haven't seen it make some classic errors in basic facts and numbers, including the number of specified letters even in fictional words; I tried reproducing some of these multiple times but couldn't). At least, that is, those I checked, but all those I checked were on the money – a far cry indeed from older versions ! Similarly, citations were all correct and relevant to its claims : none were total hallucinations. That is a big upgrade.

* ChatGPT itself claims that it does actual proper calculations whenever the result isn't obvious (like 2+2, for which training data is enough) or accuracy is especially important.

When I continued the discussion... it kept giving excellent, insightful analysis; previous versions tended to degenerate into incoherency and stupidity in long conversations. It wasn't always right – it made one major misunderstanding to one inquiry that I thought it should have avoided* – but it was right more than, say, 95% of the time, and its single significant misunderstanding was very easily corrected**. If it was good for bouncing ideas off before, now it's downright excellent. 

* This wasn't a hallucination as it didn't fabricate anything, it just misunderstood the question.
** And how many conversations with real people feature at least one such difficulty ? Practically all of them in my experience.

The second unshareable test was to feed it my rejected ALMA proposal and (subsequently) the reviewer responses. Here too the tone of GPT-5 shines. It phrased things very carefully but without walking on eggshells, explaining what the reviewer's thought processes might have been and how to address them in the future without making me feel like I'd made some buggeringly stupid mistake. I asked it initially to guess how well the proposal would have been ranked and it said second quartile, borderline possibility for acceptance... praiseworthy and supportive, but not toadying, and not raising false hopes.

When I told it the actual results (lowest quartile, i.e. useless), it agreed that some of the comments were objectionable, but gave me clear, precise instructions as to how they could be countered. Those are things I would find extremely difficult to do on my own : I read some of the stupider claims ("the proposal flow feels a bit narrative"... FFS, it damn well should be narrative and I will die on this hill) and just want to punch the screen*, but GPT-5 gave me ways to address those concerns. It said things like, "you and I know that, but...". 

* No, not really ! I just need to bitch about it to people. Misery loves company, and in a perverse bit of luck, nobody in our institute got any ALMA proposals accepted this year either.

It made me feel like these were solvable problems after all. For example, it suggested the rather subtle reframing of the proposal from detection experiment (which ALMA disfavours) to hypothesis testing (which is standard scientific practise that nobody can object to). This is really, really good stuff, and the insight into what the reviewers might have been thinking, or not understanding, made me look at the comments in a much more upbeat light. Again, it had one misunderstanding about a question, but again this was easily clarified and it responded perfectly on the second attempt.

On to the papers !

1) The Blob(s)

This paper is one of the most interesting I've read in recent years, concerning the discovery of strange stellar structures in Virgo they attribute to being ram pressure dwarfs. Initially I tried to feed it the paper by providing a URL link, but this didn't work. As I found out with the second paper, trying to do it this way is a simply mistake : in this and this alone does GPT-5 consistently hallucinate. That is, it claims it's done things which it hasn't done, reporting wrong information and randomly giving failure messages.

Not a great start, but it gets better. When a document is uploaded, hallucinations aren't quite eliminated, but good lord they're massively reduced compared to previous versions. It's weird that its more general web search capabilities appear rather impressive, but give it a direct link and it falls over like a crippled donkey. You can't have everything, I guess.

Anyway, you can read my full discussion with ChatGPT here. In brief :

• Summary : Factually flawless. All quoted figures and statements are correct. It chose these in a sensible way to give a concise summary of the most important points. Both scientific strengths and weaknesses are entirely sensible, though the latter are a little bland and generic (improve sensitivity and sample size, rather than suggesting alternative interpretations).
• Discussion : When pressed more directly for alternative interpretations, it gave sensible suggestions, pointing out pertinent problems with the methodology and data that allow for this.
• Specific inquiries : I asked it about the AGES clouds that I know are mentioned in this paper (I discovered them) and here I encountered the only real hallucination in all the tests. It named three different AGES clouds that are indeed noteworthy because they're optically dim and dark ! These are not mentioned in this paper at all. When I asked it to check again more carefully, it reported the correct clouds which the authors refer to. When I asked it about things I knew the paper didn't discuss, it correctly reported that the paper didn't discuss this.
• Overall : Excellent, once you accept the need to upload the document. Possibly the hallucination might have been a holdover from that previous attempt to provide the URL, and in my subsequent discussions I emphasised more strongly the need for accuracy and to distinguish what the paper contained from GPT-5's own inferences. This seems to have done the trick. Even with these initial hiccups, however, the quality of the scientific discussions was very high. It felt like talking with someone who genuinely knew what the hell they were talking about.

2) The Smudge

This one is about finding a galaxy so faint the authors detected it by looking for its globular clusters. They also find some very diffuse emission in between them, which is pretty strong confirmation that it's indeed a galaxy of sorts.

At this point I hadn't learned my lesson. Giving ChatGPT a link caused it to hallucinate in a sporadic, unpredictable way. It managed to get some things spot on but randomly claimed it couldn't access the paper at all, and invented content that wasn't present in the paper. Worse, it basically lied about its own failures.

You can read my initial discussion here, but frustrated by these problems, I began in a second thread with an uploaded document here. That one, I'm pleased to say, had no such issues.

• Summary : Again, flawless. A little bland, perhaps, but that's what I wanted (I haven't tried asking it for something more sarcastic). The content was researcher level rather than general public but again I didn't ask for outreach content. It correctly highlighted possible flaws like the inferred high dark matter content being highly uncertain due to an extremely large extrapolation from a relatively novel method.
• Discussion : In the hallucinatory case, it actually came up with some very sensible ideas even though these weren't in the paper. For example, I asked it about the environment of the galaxy and it gave some plausible suggestions on how this could have contributed to the object's formation – the problem was that none of this was in the paper as it claimed. Still, the discussion on this – even when I pushed it to ideas that are very new in the literature – was absolutely up to scratch. When I suggested one of its ideas might be incorrect, it clarified what it meant without changing the fundamental basis of its scenario in a way that convinced me it was at least plausible : this was indeed a true clarification, not a goalpost-shifting modification. It gave a detailed, sensible discussion of how tidal stripping can preferentially affect different components of a galaxy, something which is hardly a trivial topic.
• Specific inquiries : When using the uploaded document, this was perfect. Numbers were correct. It reported correctly both when things were and weren't present in the article, with no hallucinations of any kind. It expanded on my inquiries into more general territory very clearly and concisely.
• Overall : Great stuff. Once again, it felt like a discussion with a knowledgeable colleague who could both explain specific details but also the general techniques used. Qualitatively and quantitatively accurate, with an excellent discussion about the wider implications.

3) ALFALFA Dark Galaxies

My rather brief summary is here. This is the discovery of 140-odd dark galaxy candidates in archival ALFALFA HI data. The ChatGPT discussion is here. This time I went straight to file upload and had no issues with hallucinations whatsoever.

• Summary : Once again, flawless. Maybe a little bland and generic with regard to other interpretations, but it picked out the major alternative hypothesis correctly. And in this case, nobody else has come up with any other better ideas, so I wouldn't expect it to suggest anything radical without explicitly prompting it to.
• Discussion : It correctly understood my concern about whether the dynamical mass estimates are correct and gave a perfect description of the issue. This wasn't a simple case of "did they use the equation correctly" but a contextual "was this the correct equation to be using and were the assumptions correct" case, relating not just to individual objects but also their environment. Productive and insightful.
• Specific inquiries : Again flawless, not claiming the authors said anything they didn't or claiming they didn't say anything they did. Numbers and equations used were reported correctly.
• Overall/other: Superb. I decided to finish by asking a more social question – how come ALFALFA have been so cagey about the "dark galaxy" term in the past (they use the god-awful "almost darks", which I loathe) but here at least one team member is on board with it ? It came back with answers which were both sociologically (a conservative culture in the past, a change of team here) and scientifically (deeper optical data with more robust constraints) sensible ideas. It also ended with the memorable phrase, "[the authors are] happy to take the “dark galaxy” plunge — but with the word “candidate” as a fig leaf of scientific prudence."

4) The VCC 2034 System

This is a case of a small fuzzy patch of stars near some larger galaxies, possibly with a giant HI stream, which has proven remarkably hard to explain. The latest paper, which I summarise here, discounts the possibility that it formed from the long stream as it apparently doesn't exist, but (unusually) doesn't figure out an alternative scenario either. The ChatGPT discussion is here.

• Summary : Factually perfect, though it didn't directly include that the origin of the object was unknown. Arguably "challenges simple ram-pressure stripping scenarios and suggests either an intergalactic or pre-cluster origin" implies this, but I'd have preferred it to state it more directly. Nevertheless, the most crucial point that previous suggestions don't really hold up came through very clearly.
• Discussion : Very good, but not perfect. While it didn't get anything wrong, it missed out the claims in the paper against the idea of ram pressure dwarfs more generally (about the main target object of the study it was perfect). With some more direct prompting it did eventually find this, and the ensuing discussion was productive, pointing out some aspects of this I hadn't considered. I'm not entirely convinced this was correct, but no more than I doubt some of the claims made in the paper itself – PhD level hardly means above suspicion, after all. And the discussion on the dynamics of the object was extremely useful, with ChatGPT again raising some points from the paper I'd completely missed when I first read it; the discussion on the survival of such objects in relation to the intracluster medium was similarly helpful.
• Specific inquiries : Aside from the above miss, this was perfect. When I asked it to locate particular numbers and discuss their implications it did so, and likewise it correctly reported when the paper didn't comment on a topic I asked about. 
• Overall : Not flawless, but damn good, and certainly useful. One other discussion point caused a minor trip-up. When I brought in a second paper (via upload) for comparison and mentioned my own work for context, it initially misinterpreted and appeared to ignore the paper. This was easily caught and fixed with a second prompt, and the results were again helpful. By no means was this hallucination – it felt more like it was getting carried away with itself.

5) An Ultra Diffuse Galaxy That Spins Too Slowly

This was a paper that I'd honestly forgot all about until I re-read my own summary. It concerns a UDG that initial observations indicated lacked dark matter entirely, but then another team came along and found that would be unsustainable and it was probably just an inclination angle measurement error. Then the original team came back with new observations and simulations, and they found it does have some dark matter after all – at a freakishly low concentration, but enough to stabilise it. The ChatGPT discussion is here.

• Summary : As usual this was on the money, bringing in all the key points of the paper and giving a solid scientific assessment and critique. Rather than dealing with trivialities like sample size or simulation resolution, it noted that maybe they'd need to account more for the effects of environment or using different physics for the effects of feedback on star formation. 
• Discussion : As with the fourth paper, this was again excellent but not quite complete. It missed out one of my favourite* bits of speculation in the paper that this object could tell us something directly about the physical nature of dark matter. It did get this with direct prompting, but I had to be really explicit about it. To be fair, this is just one paragraph in the whole article, but reading between the lines I felt it was a point the author's really wanted to make. On the other hand, that's just my opinion and it certainly isn't the main point of the work. 
• Specific inquiries : Yep, once again it delivered the goods. No inaccuracies. It reported the crucial points correctly and described the comparisons with previous works perfectly. Again, it didn't report any claims the authors didn't make, 
• Overall : Excellent. I allowed myself to branch out to a wider discussion of the cold dark matter paradigm and it came back with some great papers I should check out regarding stability problems in MOND. It sort of back-peddled a little bit on discussions about the radial acceleration relation, but this was more a nuanced clarification than revising its claims : CDM gets RAR as a result of baryonic physics tuning, but it gets this for free as a result of tuning for other parameters rather than directly for RAR itself; MOND gets RAR as a main feature. If that's not a PhD level discussion then I don't know what is.

* More generally, it seems pretty good at picking up on the same stuff that I do, but it would be silly to expect 100% alignment.

Summary and Conclusions

On my other blogs I've gone on about the importance of thresholds. Well, we've crossed one. Even the more positive assessments of GPT-5 tend to label it as an incremental upgrade, but I violently disagree. I went back and checked my earlier discussion with GPT-4o about my ALMA proposal and confirmed that it was mainly spouting generic, useless crap... GPT-5 is a massive improvement. It discusses nuanced and niche scientific issues with a robust understanding of their broader context. In other threads I've found it fully capable of giving practical suggestions and calculations which I've found just work. Its citations are pertinent and exist. 

This really does feel like a breakthrough moment. At first it was a cool tech demo, then it was a cool toy. Now it's an actually useful tool for everyday use – potentially an incredibly important one. Where people are coming from when they say it gets basic facts wrong I've honestly no idea. The review linked above says it gave a garbage response when fed a 160+ page document and was anything but PhD-level, but in my tests with typical length papers (generally 12-30 pages) I would absolutely and unequivocally call it PhD level. No question of it.

This is not to say it's perfect. For one thing, even though there's a GUI setting for this, it's very hard to get it to stop offering annoying follow-up suggestions it could do. This is why you'll see my chats with it sometimes ends with "and they all live happily ever after", because I had to put that in my custom instructions to give it an alternative ending (in one memorable case it came up with "one contour to rule them all and in the darkness bind them"*). Even then it doesn't always work. And it always delivers everything in bullet-point form : no doubt this can be altered, but I haven't tried... generally I don't hate this though.

* I really like the personality of GPT-5. It's generally clear and to the point, straightforward and easy to read, but with the occasional unexpected witticism that keeps things just a little more engaging.

Of course, it does still make mistakes. Misinterpretations of the questions appears to be the most common, but these are very easily spotted and fixed. Incompleteness seems to be less common but more serious, but I'd stress that expecting perfection from anything is extremely foolish. And actual hallucinations of the kind that still plagued GPT-4 are now nearly non-existent, provided you give it rigorous instructions.

So that's my first week with GPT-5, a glowing success and vastly better than I was expecting. Okay, people on reddit, I get that you missed the sycophantic ego-stroking personality of GPT-4, so whine about how your virtual friend has died all you want. But all these claims that it's got dumber, and has an IQ barely above that of a Republican voter... what the holy hell are you talking about ? That makes NO sense to me whatsoever.

Anyway I've put my money where my mouth is and subscribed to Plus Watch this space : in a month I'll report back on whether it's worth it.