I talked about David W. Hogg's "Why do we do astrophysics?" in our seminar today.
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Hogg has done that several times:
Magnitudes, distance moduli, bolometric corrections, and so much more
This pedagogical document about stellar photometry - aimed at those for whom astronomical arcana seem arcane - endeavours to explain the concepts of magnitudes, color indices, absolute magnitudes, distance moduli, extinctions, attenuations, color excesses, K corrections, and bolometric corrections. I include some discussion of observational technique, and some discussion of epistemology, but the primary focus here is on the theoretical or interpretive connections between the observational astronomical quantities and the physical properties of the observational targets.
ADS (ui.adsabs.harvard.edu)
A likelihood function for the Gaia Data
When we perform probabilistic inferences with the Gaia Mission data, we technically require a likelihood function, or a probability of the (raw-ish) data as a function of stellar (astrometric and photometric) properties. Unfortunately, we aren't (at present) given access to the Gaia data directly; we are only given a Catalog of derived astrometric properties for the stars. How do we perform probabilistic inferences in this context? The answer - implicit in many publications - is that we should look at the Gaia Catalog as containing the parameters of a likelihood function, or a probability of the Gaia data, conditioned on stellar properties, evaluated at the location of the data. Concretely, my recommendation is to assume (for, say, the parallax) that the Catalog-reported value and uncertainty are the mean and root-variance of a Gaussian function that can stand in for the true likelihood function. This is the implicit assumption in most Gaia literature to date; my only goal here is to make the assumption explicit. Certain technical choices by the Mission team slightly invalidate this assumption for DR1 (TGAS), but not seriously. Generalizing beyond Gaia, it is important to downstream users of any Catalog products that they deliver likelihood information about the fundamental data; this is a challenge for the probabilistic catalogs of the future.
ADS (ui.adsabs.harvard.edu)
Data Analysis Recipes: Products of multivariate Gaussians in Bayesian inferences
A product of two Gaussians (or normal distributions) is another Gaussian. That's a valuable and useful fact! Here we use it to derive a refactoring of a common product of multivariate Gaussians: The product of a Gaussian likelihood times a Gaussian prior, where some or all of those parameters enter the likelihood only in the mean and only linearly. That is, a linear, Gaussian, Bayesian model. This product of a likelihood times a prior pdf can be refactored into a product of a marginalized likelihood (or a Bayesian evidence) times a posterior pdf, where (in this case) both of these are also Gaussian. The means and variance tensors of the refactored Gaussians are straightforward to obtain as closed-form expressions; here we deliver these expressions, with discussion. The closed-form expressions can be used to speed up and improve the precision of inferences that contain linear parameters with Gaussian priors. We connect these methods to inferences that arise frequently in physics and astronomy. If all you want is the answer, the question is posed and answered at the beginning of Section 3. We show two toy examples, in the form of worked exercises, in Section 4. The solutions, discussion, and exercises in this Note are aimed at someone who is already familiar with the basic ideas of Bayesian inference and probability.
ADS (ui.adsabs.harvard.edu)
@knud @vicgrinberg He can do that. Not many others would get past the moderators with these kind of "papers".
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@knud @vicgrinberg He can do that. Not many others would get past the moderators with these kind of "papers".
@hannorein @knud yeah...
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@knud @vicgrinberg He can do that. Not many others would get past the moderators with these kind of "papers".
Which is a shame. The 2022 writeup on distance moduli, magnitudes, k-correction and other things is better than anything I've seen in a textbook so far. So in my opinion it's a very valuable resource.
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"People are always the ends, not merely the means. [...]
When we employ a graduate student to perform some work, it absolutely must be because the graduate student will benefit from that work, not merely because that work needs to get done."
3/6
@vicgrinberg So obvious, and yet this seems like a controversial statement in much of academia.
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"You can’t do science if you don’t live within a network of trust. You have to trust your coauthors, you have to trust the literature, and you have to trust the machinery and tools that you use."
"A trusted partner is one that takes responsibility for their work."
5/6
@vicgrinberg This, 1000 times
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"Why won't you work with xyz, are you not a team player?" neglects the lack of trust that had been demonstrated time and time before, and yet people don't seem to think it matters. -
"Practice of astrophysics cannot be learned from reading. [...] If you want to become an astrophysicist, it isn’t sufficient to read or take classes. You have to do it, and doing it requires doing novel things, that haven’t been done before, and which connect to important scientific questions in the literature."
2/6
@vicgrinberg In paleontology, there is (and has been) a small but noisy cadre of what have been referred to as "armchair paleontologists": people who "do" paleontology (mostly taxonomy, really) without ever going and studying actual specimens; only making pronouncements based on what others have published. Some have made positive contributions, but most have been decidedly detrimental to the science. They're often so loud that they are difficult to simply ignore, and many have done real damage.
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@vicgrinberg IMHO if you win that point, all the others will be won at the same time.
I am not an astrophysicist or even a scientist (other than by education).
My job is mostly strategy. Foresee where and when battles will be fought and why... and to win them before anyone else knows they will happen.
We live in a time of historical crisis not seen for centuries.
José Ortega y Gasset defined such historical crisis as a change of fundamental ways of thinking.
The last crisis for him was the Renaissance where the belief-based mindset battled the knowledge-based mindset. The belief-based mindset lost.
Now we see the knowledge-based mindset being attacked. And the best way to defeat it would be to make a discourse about knowledge impossible.
As long as that discourse is alive, science cannot be vanquished.
But by destroying the network of trust, you make discourse impossible. You end up "my book says" vs. "the other book says" and with no way of resolving that other than by belief.
That is why my finger pointed there...
@vicgrinberg @masek This is why librarians and others teaching about the evaluation of information sources must clearly connect the format, process, etc. to the creators and reviewers of the products! Ultimately, 'do you trust the resource?' is, 'do you trust the people involved in the process of it existing?' #library
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"People are always the ends, not merely the means. [...]
When we employ a graduate student to perform some work, it absolutely must be because the graduate student will benefit from that work, not merely because that work needs to get done."
3/6
@vicgrinberg
I don't really understand this one. Will work that 'needs to get done' in most cases not also benefit the student? -
@vicgrinberg
I don't really understand this one. Will work that 'needs to get done' in most cases not also benefit the student?@brunthal "most cases" does the heavy lifting here. But you can easily hire a student to do necessary work that will not benefit them (or benefit them very little) or where they learn very little for their future or where they are mainly misused as cheap (teaching) labor
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@brunthal "most cases" does the heavy lifting here. But you can easily hire a student to do necessary work that will not benefit them (or benefit them very little) or where they learn very little for their future or where they are mainly misused as cheap (teaching) labor
@brunthal cases I've seen: students hired to do projects that are mainly instrument calibration, with hardly any supervision on the science side of their project and no tangible result of the thesis in terms of career prospects inside or outside academia.
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@brunthal cases I've seen: students hired to do projects that are mainly instrument calibration, with hardly any supervision on the science side of their project and no tangible result of the thesis in terms of career prospects inside or outside academia.
@brunthal and there is of course the framing - what drives the employment, the need to get the work done and the PhD candidate may learn something along the way? Or is it about the PhD candidate learning and growing and on the way some necessary work gets done?
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