Rhys' Law

If someone else doesn't have this already, which they almost certainly do, I'm totally claiming this as Rhys' Law :

The value of a press release and the probability that the reported discovery is correct is anti-correlated with the grandiosity of the claims.

It's probably not a linear relation. Maybe more like a galactic hydrogen density profile : flat near the middle out to some quite large distance (importance of the claim), but then it drops precipitously beyond the stellar disc (as claims move from "probably" and "show" to "mystery solved" and "proof").

Publish or perish at least produces some hilarious author names

Hoover, a physicist at Lawrence Livermore National Lab, had tried unsuccessfully to get a paper published in two leading journals. So he added a co-author from a prestigious-sounding institute, the Institute for Advanced Studies at Palermo, Sicily, and resubmitted the work. Sure enough, the paper was accepted and published. He did this several times with the same result. But the name Hoover chose—Stronzo Bestiale—was a sly tell: In Italian, it means “giant asshole.” And yet Bestiale remains in the scientific literature, just like Hoss Cartwright. So does Galadriel Mirkwood, an Afghan hound that belonged to biologist Polly Matzinger of the National Institutes of Health. She was fed up with the use of passive voice in scientific papers, and decided to add her pup’s name to a paper in protest.

In astronomy the use of the passive voice is severely frowned upon... errr, I mean, we hates it, precious ! We hates it !

It’s tempting to laugh off some of these antics, which seem driven by ego and self-interest. But they also underscore a painful truth: Unless the evaluation of scientists—and the all-important doling out of funding—can be wrenched away from bean-counting metrics, history is likely to repeat itself. Tomorrow’s metrics gamers may come up with some other ruse, and spoofers like Morgenstern will invent the next Hoss Cartwright in response. Taking time to read and evaluate a selection of a job applicant’s papers takes far more time than plugging a bunch of numbers in to a matrix. But it’s precisely that output, not metrics, that science is supposed to be about. The agencies that fund grants and committees that hire and promote academic researchers need to get back to doing the hard job of assessing the value and quality of candidates’ scientific work rather than leaning on the crutch of overly simplified publication metrics.

It's the over-reliance on simplified metrics that's the problem here. A publication record is a useful thing, but relying on sheer numbers is a terrible idea. Hence my previous suggestion of a more nuanced journal/publication ranking system, where one could see how many papers of particular types and review quality a researcher has. Even then, to rely entirely on numbers would be a fatal mistake, because you can't quantify research quality. It's fundamentally impossible. All you can do is to try and make the current system better.

http://nautil.us/issue/42/fakes/why-fake-data-when-you-can-fake-a-scientist

For the record...

I'm just going publically on the record to state that the EM drive does not work and it will go the way of cold fusion and all the other pseudoscientific claims before it. That is all. Have a nice day.

Expecting the unexpected

Interesting project.

The case study of the Nobel-prize-winning discovery of pulsars by Jocelyn Bell is instructive. A talented and persistent PhD student studying interstellar scintillation (and thus expanding the observational phase space), and who knew her instrument intimately, recognised that ‘bits of scruff’ on the chart recorder could not be terrestrial interference, but represented a new type of astronomical object (Bell Burnell 2009). As a result, she discovered pulsars.

...A present-day Jocelyn Bell is unlikely to understand the instrument well enough to distinguish astrophysical phenomena from instrumental effects, and would not be able to sift through the petabytes by hand, searching for something unusual. On the other hand, failure to identify unexpected effects may mean missing out on the most important science to emerge from ASKAP. It is therefore necessary to plan explicitly to build techniques to make unexpected discoveries, rather than hoping to stumble across them.

We have therefore started a project called “WTF”, which explicitly aims to mine EMU data to discover unexpected science that is not part of our primary science goals, using a variety of machine-learning techniques and algorithms. Although targeted specifically at EMU, we expect this approach to have broad applicability to astronomical survey data.
https://arxiv.org/abs/1611.02829

Visualising 3D data in arbitrary coordinates

I had a paper accepted. It doesn't seem very important now though, because America has decided to take all that was great and good about itself and crap all over it. Still, you can read the full version here.
https://arxiv.org/abs/1611.02517

Spherical volumetric data in realtime

More experiments with all-sky HI data using frequency to set the radial distance. The realtime display in Blender gives a rather different appearance to the rendered views (see recent posts in this category) because faces are one-sided in the realtime view - so the opposite half of the spheres aren't visible. Gives a more interesting and subtle effect than the rendered view.

The Next Cat Fight ?

Lots of press releases lately about the expansion of the Universe not accelerating. This doesn't mean there's no dark energy (whatever that may be) continuing to drive the expansion - the expansion rate seems to be more of a constant, whereas without dark energy it should be slowing down.

Or is it ? Maybe not. According to this paper it's accelerating after all. They claim that the previous authors have done a shoddy bit of statistics on the data, ignoring selection effects at different distances and other independent evidence for the acceleration. I'm not going to comment on who's right because I'm nowhere near qualified enough to judge the statistical methods, but they conclude :

Even without external constraints, this work demonstrates that a more accurate model for the supernova analysis greatly increases the significance of acceleration. We conclude that the analysis in N16 is both incorrect in its method and unreasonable in its assumptions, leading the authors to question a result that is quite secure when addressed properly.

So there.
https://arxiv.org/abs/1610.08972