#sdss2020, day 3+1

If yesterday was day 3, then the one-day working meeting on Monday regarding SDSS-IV was day zero, and today—a working meeting for SDSS-V—was day 3+1. The highlight for me today was a discussion led by Hans-Walter Rix (MPIA) and Kevin Covey (WWU) of what we might do with extra fiber–visits.

SDSS-V is a robot-positioned multi-object fiber-fed spectroscopic survey, with both optical and infrared spectrographs. It works in a few modes, but most of them involve jumping around the sky, taking (relatively) short spectroscopic observations of hundreds of stars at a time. The operations are complex: There are multiple target categories with different cadence requirements, and there are positional constraints on what the fiber robots can do. All this means that there are many, many (like millions of) unassigned fiber–visits.

The range of projects proposed was breathtaking, from Cepheids to microlensing events to nearby galaxy redshifts to quasar catalogs. And all of them so clever and thought-out that they were all compelling. And this wasn't even an official call for proposals: It was just a brainstorming session. Towards the end, there were some ideas (that I loved) about taking a union of the star-oriented suggestions and making a project with excellent data volume and legacy value. I love this Collaboration.

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housekeeping data; Milky Way

Last night or this morning (not sure which) Lily Zhao (Yale) made some nice discoveries about the EXPRES instrument: She found that the housekeeping data (various temperature sensors, chilled water load, etc) correlate really well with the instrument calibration state as defined by our hierarchical (principal-component) model. That's interesting, because it opens up the possibility that we could interpolate the non-parametric calibration parameters in the housekeeping space rather than the time space. That would be cool. Indeed, the time is just a component of the housekeeping data!

In the afternoon, Kathryn Johnston (Columbia) hosted a group of people for the L2G2—the local Local Group group—for talks and discussions. There were many good discussions, led by Jason Hunt (Flatiron). Highlights for me included, first, work on detailed abundances with the cannon by Adam Wheeler (Columbia), who did a great job of describing (and re-implementing) the method, and extending it to do better things. Another was a talk on SAGA by Marla Geha, who showed that the Local Group satellites might be group satellites rather than galaxy satellites. This in the sense that they look more like the satellites of a more massive object than satellites of Milky Way or M31 analogs. It was a great day of great discussions.

#MLringberg2019, day 5

Today was the final day of Machine Learning Tools for Research in Astronomy. I gave my talk, which was about causal structure. One thing I talked about is the strong differences (stronger than you might think) between generative and discriminative directions for machine learning. Another thing I talked about is the way that machine-learning methods can be used to denoise, deconvolve, and separate signals when they are designed with good causal structure.

Right after me, Timmy Gebhard (MPI-IS and ETH) gave an absolutely excellent talk about half-sibling regression ideas related to instrument calibration (think Kepler, TESS, and direct imaging). He beautifully explained exoplanet direct imaging and showed how his improvements to how they are using the data change the results. He doesn't have the killer app yet, but he is spending the time to think about the problem deeply. And if he switched from one-band direct imaging to imaging spectroscopy (which is the future!) I think his methods will kill other methods. He also spoke about the causal-inference philosophy behind his methods really well.

My talk slides are here and I also led the meeting summary discussion. My summary slides are here. The summary discussion was valuable. In general, the size and style of the meeting—and location in lovely Ringberg Castle—led to a great environment and culture at the meeting. Plus with great social engineering by Ntampaka, Nord, Pillepich, and Peek. The latter made progress on a community-driven set of Ringberg Recommendations, which might end up as a long-term outcome of the meeting.

#MLringberg2019, day 2

Today was the second day of Machine Learning Tools for Research in Astronomy. Two personal highlights were the following:

Soledad Villar (NYU) spoke about adversarial attacks against machine-learning methods used in astrophysics. Her talk was almost entirely conceptual; she talked about what constitutes a successful attack, and how you find it. My expectation is that these attacks will be very successful, as my loyal reader knows! The examples she showed were from stellar spectroscopy. Her talk was interrupted and followed by extremely lively discussion, in which the room disagreed about what the attacks mean about a method, and whether they are revealing or important. That was some fun controversy for the meeting.

Tobias Buck (AIP) looked at methods to translate from image to image (like horse to zerbra!) but in the context of two-d maps of galaxies. Like translate from photometric images into maps of star formation and kinematics. It's a promising area, although the training data are all simulations at this point. I asked him whether he could translate from a two-d galaxy image into a three-d dark-matter map. He was skeptical, because the galaxy is so much smaller than its dark-matter halo.

At one of the coffee breaks, Josh Peek (STScI) proposed that we craft some kind of manifesto or document that helps practitioners in machine learning in astronomy make good choices, which are pragmatic (because it is important that machine learning be used and tried) but also involve due diligence (to avoid the “just throw machine learning at it” problem in some of the literature). He had the idea that we have the right people at this meeting to make something like this happen. I noted that we tried to do things like that in our tutorial paper on MCMC sampling, where we try to both be pragmatic but also recommend achievable best practices. The challenge is to be encouraging and supportive, but also draw some lines in the sand.

predicting one population of transients from another

Tyler Pritchard (NYU) convenes a meeting on Mondays at NYU to discuss time-domain astrophysics. Today we had a discussion of a very simple idea: Use the rates of short GRBs that are observed and measured (using physical models from the MacFadyen group at NYU) to have certain jet–observer offset angles to infer rates for all the way-off-axis events that won't be GRB triggers but might be seen in LSST or other ground-based optical or radio surveys. Seems easy, right? It turns out it isn't trivial at all, because the extrapolation of a few well-understood events in gamma-rays, subject to gamma-ray selection effects to a full population of optical and radio sources (and then assessing those selection effects) requires quite a few additional or auxiliary assumptions. This is even more true for the bursts where we don't know redshifts. I was surprised to hear myself use the astronomy word "V-max"! But we still (as a group) feel like there must be low-hanging fruit. And this is a great application for the MacFadyen-group models, which predict brightness as a function of wavelength, time, and jet–observer angle.

stellar twins and stellar age indicators

In the stars group meeting at CCA, Keith Hawkins (Columbia) blew us away with examples of stellar twins, identified with HARPS spectra. They were chosen to have identical derived spectroscopic parameters in three or four labels, but were amazingly identical at signal-to-noise of hundreds. He then showed us some he found in the APOGEE data, using very blunt tools to identify twins. This led to a long discussion of what we could do with twins, and things we expect to find in the data, especially regarding failures of spectroscopic twins to be identical in other respects, and failures of twins identified through means other than spectroscopic to be identical spectroscopically. Lots to do!

This was followed by Ruth Angus (Columbia) walking us through all the age-dating methods we have found for stars. The crowd was pretty unimpressed with many of our age indicators! But they agreed that we should take a self-calibration approach to assemble them and cross-calibrate them. It also interestingly connects to the twins discussion that preceded. Angus and I followed the meeting with a more detailed discussion about our plans, in part so that she can present them in a talk in her near future.

abundance dimensionality, optimized photometric estimators

Kathryn Johnston (Columbia) organized a Local-Group meeting of locals, or a local group of Local Group researchers. There were various discussions of things going on in the neighborhood. Natalie Price-Jones (Toronto) started up a lot of discussion with her work on the dimensionality of chemical-abundance space, working purely with the APOGEE spectral data. That is, they are inferring the dimensionality without explicitly measuring chemical abundances or interpreting the spectra at all. Much of the questioning centered on how they know that the diversity they see is purely or primarily chemical rather than, say, instrumental or stellar nuisances.

At lunch time there were amusing things said at the Columbia Astro Dept Pizza Lunch. One was a very nice presentation by Benjamin Pope (Oxford) about how to do precise photometry of saturated stars in the Kepler data. He has developed a method that fully scoops me in one of my unfinished projects: The OWL, in which the pixel weights used in his soft-aperture aperture photometry are found through the optimization of a (very clever, in Pope's case) convex objective function. After the Lunch, we discussed a huge space of generalizations, some in the direction of more complex (but still convex) objectives, and others in the direction of train-and-test to ameliorate over-fitting.