writing by tweeting

I spent the day working on my document about releasing data and code. I tweeted (tm) some of the ideas in the paper and started responding to the storm of replies. The twitters are excellent for getting ideas from the community!

the Bronx; the baryon acoustic feature

Although perhaps this doesn't count as research, I spent today at public middle school CIS 303 in the Bronx, for Career and College Day. I met lots of kids (and lots of other people talking about their careers) and said words and answered questions about my career and how I got here. The format was panels, interviewed by classrooms of kids. Most interesting idea of the day (and it was from other panelists): Success in a career requires empathy and the ability to listen. That's deep! Strongest impression of the day: Comparing this public middle school to that of my own daughter, I can (still) see a lot of disparity in the NYC public school system, and that disparity isn't just about money: It is also about discipline, school organization, and academic priorities. (These disparities are what got me studying education way back in the late eighties when I was in college.)

At the beginning of the day, I did get a bit of research in thanks to Jeremy Tinker (NYU), who showed the Blanton–Hogg group meeting what is currently going on with (finishing) BOSS data analyses and (starting) eBOSS ones. The combination of baryon acoustic feature scale measurements and redshift-distortion measurements lead to very strong constraints on cosmological parameters. I'd like to say more! But papers will appear within weeks.

not-so-latent variable latent variable model

I had a conversation today with Boris Leistedt about his work to build a latent model of galaxy SEDs and get template-space-marginalized photometric redshifts. I proposed that he instantiate the latent variables as observables (like rest-frame colors or line strengths or something); this will help the model break degeneracies and sensibly order the templates in the latent space. That is, it should regularize or simplify the model for inference. That's just an intuition. But it might also help people who have drunk less of our Kool Aid to understand!

release your data and code; diffraction microscopy

Having sent my cosmology inference draft to various friendlies for them to beat it up, I returned to other priorities. I have the goal to finish two more papers before my sabbatical ends. The first would be something (in the Data Analysis Recipes series) about why you should (or shouldn't) release your data and code. I keep making the same arguments over and over in person and by email, so I should write them down once and for all! This is exactly why I started the fitting-a-line document o-so-many years ago. The second is a paper on diffraction imaging with very few photons. I booted up both of these projects today.

On the first, I made a draft table of all the things that come in on the pro and con sides for releasing data and code. There are lots of overlaps, and lots of things appear in both the pro and the con column! For example, documentation: This is a con, because it is a burden, but a pro because you are encouraged to do it (and benefit from it). And it applies to both data and code.

On the second, I worked through the mathematics of the Ewald sphere, in preparation for generalizing my code so that it doesn't have to work in the small-angle limit.

finished a paper!

Sabbatical is an unreal experience. In case the loyal reader was wondering How awesome is your job?, let me add to the list of awesome the fact of having a year (every seven, in principle) with no teaching or committee duties, in which I can do whatever I want! One piece of evidence that this is happening right now is that I just finished the third (zeroth draft) first-author paper of this academic year, which is about six times as many first-author papers I complete in a normal year (yes, my normal rate is 0.5 first-author per year; thankfully my junior colleagues are far more productive and permit my co-authorship).

Of course these three first-author papers are not really done: On one I need to respond to referee, on one I am waiting for a bit of work from collaborators, and this new one is still very, very rough. But it is fully drafted, from end to end. The subject is: How we compare cosmological simulations to cosmological data, and the incorrect inferences we might be drawing by the wrongness of all this.

training a photometric-redshift method with a single redshift

Boris Leistedt dropped in today and we discussed his methods to build a physically possible model of galaxy spectral energy distributions and therefore photometric redshifts, but with an exceedingly flexible model. His method is brilliant because it is entirely data-driven (no fixed templates) and yet it respects the physics of special relativity (the Doppler shift), which the machine-learning methods do not.

He made the amusing point that his method can be trained with a training set that contains literally a single galaxy with a spectroscopic redshift! That is, if you even have only one single redshift, you can put photometric redshifts (with, admittedly, large error bars) on all the other photometric galaxies! That is a property that no other data-driven method has. The point is that if you have multi-wavelength data on a single galaxy with a redshift, you can make rough predictions about how other galaxies would look at other redshifts.

His real breakthrough is the idea of using Gaussian processes to put priors on the spectral energy distributions (templates): If the SEDs are drawn from a Gaussian process, then all of the photometry (which consists of linear projections of the SEDs) is also drawn from a Gaussian process. We discussed the magic of all of this.

I also read a proposal by Daniela Huppenkothen, and wrote words in my inference-of-variances paper.

ABC

For my inference-of-variances project, I got ABC working. It is delivering a correct posterior, even at a reasonable (finite) distance threshold. All is good! I put a figure showing this into the nascent paper.

Dr Greg Green

It was my pleasure to sit on the PhD defense committee of Gregory Green (Harvard) today. I had to do it remotely (for uninteresting reasons). Green has built a three-dimensional map of the dust in the Milky Way, by modeling every single star in the PanSTARRS data. This is an impressive feat computationally, since it is a huge problem, and also probabilistically, since most things you can write down are either intractable or wrong.

Being a good probabilistic reasoner, Green did something both tractable and correct, and got a beautiful map. His tours of the map in his presentation were mesmerizing. He was cagey about spiral structure; his method wouldn't necessarily find it even if it were there.

It was a great PhD defense based on absolutely great work. At the end of his talk he discussed ways he might do things that are even righter in the future, given our prior beliefs about the interstellar medium (and lots of new data). That's super interesting, and we hope to discuss when his dust settles (so to speak). Congratulations Dr Green!

variance of variances

I spent the day trying to understand the frequentist properties of empirical (sample) variances, and the properties (expectation value and variance) of estimators of the variance of the distribution that generated the sample. This is all related to my issues with cosmological inference, that I am trying to write up. I am not surprised cosmologists have made mistakes here; it is hard to understand even in the most trivial situation. I am working out the trivial case to make analogies to the real case.

radial velocity pipeline hacking

I spent small bits of time between things writing in my short paper about inferring the variance of a distribution, given only samples from that distribution. I am trying to have a companion paper for the ABC paper being written by Hahn and Vakili about large-scale structure. Foreman-Mackey gave me some advice this week about how to pitch the paper, and I am trying to obey it.

In the afternoon I had a long phone call with Megan Bedell (Chicago) about how high-precision radial velocity spectrograph data are turned into radial-velocity measurements. She wants to reconsider parts of the pipeline, without rebuilding the whole pipeline from scratch (which is something I would like to do some day). She is currently analyzing her data with a closed-source pipeline, which is, apparently, the standard practice in this field. That has all sorts of bad properties, including that no-one can assert with confidence what the code is doing, nor check it! Bedell and I discussed the stage at which the code goes from a cross-correlation function (as a function of velocity) at every Echelle order to a single velocity. Apparently we can get the data-reduction black box to return enough information that we can reprocess this step.

Dr. Adrian Price-Whelan

It was my great pleasure to sit on the PhD defense committee for Adrian Price-Whelan (Columbia) today. He spoke about using phase-space structures, especially streams of stars, to constrain the gravitational field in the Milky Way. He pointed out that we should call it the “gravitational field” not the “gravitational potential” because the latter is only known up to a constant. (I think the argument is deeper: The potential exists only to be differentiated, never observed!)

Two important ideas that were under heavy discussion both in Price-Whelan's talk and in the discussion afterwards were the following: Price-Whelan may have the first ever empirical evidence for dynamical chaos outside of planetary systems. His evidence is in the form of morphological predictions for stellar streams. This argument is weak at present, but if the evidence grows stronger, he will be responsible for a new kind of fundamental measurement.

The other idea is that we might be making big mistakes interpreting the Milky Way entirely in terms of simple, integrable models. As my loyal reader knows, I have ranted about this for years, but my rants have been vapor-ware. Price-Whelan (along with Pearson at Columbia and Bonaca at Yale, I should add) is close to being able to address this point directly and quantitatively.

In addition to all of these things, Price-Whelan has delivered some great probabilistic methods and code, contributed heavily to important open-source astronomy projects, and led our community in education regarding code, data analysis, and research practice. It has been a great pleasure and opportunity for me to be involved in his work for (what his thesis points out is) the last decade!

Chicago

I spent the day at the University of Chicago, hosted by Andrey Kravtsov (Chicago). Kravtsov and I discussed the great simplicity (on large scales) of the galaxy population, and also the teaching of stellar physics, in which he has made some very interesting innovations. Since he is a true scholar, we also talked briefly about the impact on philosophy of science when the models are fundamentally computational. He recommended this book, around which maybe we should build a reading group next year at the Simons SCCA.

I met with the large group of John Carlstrom (Chicago) and collaborators. They showed me the SPT data, and it is incredible: The CMB fluctuations are measured at enormous signal-to-noise and the point sources and SZ clusters are sharp points. Totally unlike any previous data. I met with Arieh Königl (Chicago), who predicted that we ought to see really cool relationships between planetary systems and the chemical abundances in their protoplanetary disks, mirroring things Schlaufman (OCIW) said to me a few weeks ago. Dan Holz (Chicago) and I discussed low-hanging fruit in the aftermath of the LIGO and exoplanet disk physics. We came around quickly to model interpolation or emulators or surrogates. Just like we have been discussing with Rix, Ting, and Conroy. With both students and postdocs I discussed possibilities for improving stellar radial velocity measurements and photometric calibration. What a great group of people at Chicago!

every radial velocity currently available

I spent some time with Adrian Price-Whelan discussing what we need to do to get ready for Gaia. We concluded that radial velocities will be extremely valuable; we should assemble every radial velocity for any star in the TGAS set prior to the Gaia data release. This might involve convincing surveys that have not yet done data releases to release some information on relevant subsamples. That would be a fun community-building project. After that, we discussed crazy projects related to life and intelligence. Price-Whelan is in the gap between thesis completion and defense, so he is currently open to crazy.

single transits, surfaces, and variance

[I have been out on vacation; hence the lack of posts recently.]

Out of the blue came an email from So Hattori (NYUAD), who has found many single transits (long-period planet candidates) in the Kepler data. This is awesome! Foreman-Mackey and I discussed the goals for Hattori's project, and its relationships with Foreman-Mackey's current project (which has found a Saturn analog and has some occurrence rate results).

In the morning, I spent time with Leslie Greengard discussing various matters related to the description (on a computer, say) of continuous (and infinitely differentiable) surfaces. There are some outstanding problems, which seem like simple math problems but are unsolved. This has nothing to do with anything I am working on, but I could get hooked. Of course my position is that the determination of a surface given control points ought to be cast as an inference problem!

The remainder of the day was spent planning and outlining my argument in my “Inference of Variance” project: How the question of inferring the variance of a process (that generated some points or data, say) is related to the problem of cosmological parameter estimation, and how we can help the latter with work on the former.

fiber positioners for spectrographs

In a short research day, I had a very useful conversation about robot fiber positioners for multi-object spectrographs with Peter Mao (Caltech), who is working on the Prime Focus Spectrograph for Subaru. He gave me a sense of the cost scale, the human effort scale, and the technical precision of such systems. This is critical information for the Letter of Intent that I and various others are putting in for the use of the SDSS hardware after the end of the current survey, SDSS-IV. We would like to go really big with the two APOGEE spectrographs, but if we want to do really large numbers of stars (think: millions or even tens of millions) we need to have robots place the fibers.