hierarchical probabilistic calibration

Today Lily Zhao (Yale) visualized for me some of the calibration data they have for the EXPRES spectrograph at Yale. What she showed is that the calibration does vary at very high signal-to-noise, and that the variations are systematic or smooth. That is, the instrument varies only a tiny tiny bit, but it does so very smoothly and the smooth variations are measured incredibly precisely. This suggests that it should be possible to pool data from many calibration exposures to build a better calibration model for every exposure than we could get if we treated the data all independently.

Late in the day, we drew a graphical model for the calibration, and worked through a possible structure. As my loyal reader knows, I want to go to full two-dimensional modeling of spectrographs! But we are going to start with measurements made on one-dimensional extractions. That's easier for the community to accept right now, anyways!


forecasting tools; beautiful spectrograph calibration

Our five-person (Bedell, Hogg, Queloz, Winn, Zhao) exoplanet meeting continued today, with Winn (Princeton) working out the elements needed to produce a simulator for a long-term EPRV monitoring program with simple observing rules. He is interested in working out under what circumstances such a program can be informative about exoplanets in regimes that neither Kepler nor existing EPRV programs have strongly constrained, like near-Earth-masses on near-Earth-orbits around near-Sun stars. And indeed we must choose a metric or metrics for success. His list of what's needed, software-wise, is non-trivial, but we worked out that every part of it would be a publishable contribution to the literature, so it could be a great set of projects. And a very useful set of tools.

Zhao (Yale) showed me two-dimensional calibration data from the EXPRES instrument illuminated by their laser-frequency comb. It is astounding. The images are beautiful, and every single line in each image is at a perfectly known (from physics!) absolute wavelength. This might be the beginning of a very new world. The instrument is also beautifully designed so that all the slit (fiber, really, but it is a rectangular fiber) images are almost perfectly aligned with one of the CCD directions, even in all four corners of the image. Not like the spectrographs I'm used to!


do we need to include the committee in our model?

Josh Winn (Princeton) and Lily Zhao (Yale) both came in to Flatiron for a couple of days today to work with Megan Bedell (Flatiron), Didier Queloz (Cambridge), and me. So we had a bit of a themed Stars and Exoplanets Meeting today at Flatiron. Winn talked about various ways to measure stellar obliquities (that is, angles between stellar-rotation angular momentum vectors and planetary system angular-momentum vectors). He has some six ways to do it! He talked about statistical differences between vsini measurements for stars with and without transiting systems.

Zhao and Queloz talked about their respective big EPRV programs to find Earth analogs in radial-velocity data. Both projects need to get much more precise measurements, and observe fewer stars (yes fewer) for longer times. That's the direction the field is going, at least where it concerns discovery space. Queloz argued that these are going to be big projects that require patience and commitment, and that it is important for new projects to control facilities, not just to apply for observing time each semester! And that's what he has with the Terra Hunting Experiment, in which Bedell, Winn, and I are also partners.

Related to all that, Zhao talked about how to make an observing program adaptive (to increase efficiency) without making it hard to understand (for statistical inferences at the end). I'm very interested in this problem! And it relates to the Queloz point, because if a time allocation committee is involved every semester, any statistical inferences about what was discovered would have to model not just the exoplanet population but also the behavior of the various TACs!


normalizing flows; information theory

At lunchtime I had a great conversation with Iain Murray (Edinburgh) about two things today. One was new ideas in probabilistic machine learning, and the other was this exoplanet transit spectroscopy challenge. On the former, he got me excited about normalizing flows, that use machine learning methods (like deep learning) and a good likelihood function to build probabilistic generative models for high dimensional data. These could be useful for astronomical applications; we discussed. On the latter, we discussed how transits work and how sunspots cause trouble for them. And how the effects might be low dimensional. And thus how a good machine-learning method should be able to deal with it or capture it.

In the afternoon I spent a short session with Rodrigo Luger (Flatiron) talking about the information about a stellar surface or about an exoplanet surface encoded in a photometric light curve. The information can come from rotation, or from transits, or both, and it is different (there is more information), oddly, if there is limb darkening! We talked about the main points such a paper should make, and some details of information theory. The problem is nice in part because if you transform the stellar surface map to spherical harmonics, a bunch of the calculations lead to beautiful trigonometric forms, and the degeneracy or eigenvector structure of the information tensor becomes very clear.


eclipsing binaries

I had a good conversation with with Laura Chang (Princeton) today, who is interested in doing some work in the area of binary stars. We discussed the point that many of the very challenging things people have done with the Kepler data in the study of exoplanets—exoplanet detection, completeness modeling, populations inferences— are very much easier in the study of eclipsing binary stars. And the numbers are very large: The total number of eclipsing binary systems found in the Kepler data is comparable to the total number of exoplanets found. And there are also K2 and TESS binaries! So there are a lot of neat projects to think about for constraining the short-period binary population with these data. We decided to start by figuring out what's been done already.


Pheno 2019, day 3

I spent the day at Pheno 2019, where I gave a plenary about Gaia and dark matter. It was a fun day, and I learned a lot. For example, I learned that when you have a dark photon, you naturally get tiny couplings between the dark matter and the photon, as if the dark matter has a tiny charge. And there are good experiments looking for milli-charged particles. I learned that deep learning methods applied to LHC events are starting to approach information-theoretic bounds for classifying jets. That's interesting, because in the absence of a likelihood function, how do you saturate bounds? I learned that the Swampland (tm) is the set of effective field theories that can't be represented in any string theory. That's interesting: If we could show that there are many EFTs that are incompatible with string theory, then string theory has strong phenomenological content!

In the last talk of the day, Mangano (CERN) talked about the future of accelerators. He made a very interesting point, which I have kind-of known for a long time, but haven't seen articulated explicitly before: If you are doing a huge project to accomplish a huge goal (like build the LHC to find the Higgs), you need to design it such that you know you will produce lots and lots of interesting science along the way. That's an important idea, and it is a great design principle for scientific research.



I spent a bit of research time today writing up my ideas about what we might do with The Snail (the local phase spiral in the vertical dynamics discovered in Gaia data) to infer the gravitational potential (or force law, or density) in the Milky Way disk. The idea is to model it as an out-of-equilibrium disturbance winding up towards equilibrium. My strong intuition (that could be wrong) is that this is going to be amazingly constraining on the gravitational dynamics. I'm hoping it will be better (both in accuracy and precision) than equilibrium methods, like virial theorem and Jeans models. I sent my hand-written notes to Hans-Walter Rix (MPIA) for comments.


not much

My only research events today were conversations with Eilers, Leistedt, and Pope about short-term strategies.


Dr Alex Malz!

Today it was my great pleasure to participate in the PhD defense of my student Alex Malz (NYU). His dissertation is about probabilistic models for next-generation cosmology surveys (think LSST but also Euclid and so on). He showed that it is not trivial to store, vet, or use probabilistic information coming from these surveys, using photometric-redshift outputs as a proxy: The surveys expect to produce probabilistic information about redshift for the galaxies they observe. What do you need to know about these probabilistic outputs in order to use them? It turns out that the requirements are strong and hard. A few random comments:

On the vetting point: Malz showed with an adversarial attack that the ways cosmologists were comparing photometric-redshift probability outputs across different codes were very limited: His fake code that just always returned the prior pdf did as well on almost all metrics as the best codes.

On the requirements point: Malz showed that you need to know all the input assumptions and priors on any method in order to be able to use its output, especially if its output consists of posterior information. That is, you really want likelihood information, but no methods currently output that (and many couldn't even generate it because they aren't in the form of traditional inferences).

On the storage point: Malz showed that quantiles are far better than samples for storing a pdf! The results are very strong. But the hilarious thing is that the LSST database permits up to 200 floating-point numbers for storage of the pdf, when in fact the photometric redshifts will be based on only six photometric measurements! So, just like in many other surveys that I care about, the LSST Catalog will represent a data expansion, not a data reduction. Hahaha!

It was a great talk, and in support of a great dissertation. And a great day.


Dr Mandyam

Today I had the pleasure of serving on the PhD committee for Nitya Mandyam Doddamane, who defended her thesis on the measurement of star-formation rates and stellar masses in spectroscopic surveys of galaxies. She compared different stellar populations models, based on different parts of the galaxy spectral energy distributions, and galaxy environments, to make inferences about which galaxies are and aren't forming stars. She has some nice examples that use environment to break some degeneracies in interpretation. In that sense, some of what she did was a causal inference. She also looked at aperture biases, comparing fiber spectroscopy to integral-field spectroscopy from various SDSS surveys. Her results are nice, and were beautifully presented, both in the talk and in the thesis. Congratulations Dr Mandyam!


Galactic archaeology

It's a long story, but we have been experimenting continuously with the rules and principles underlying the weekly Stars and Exoplanets Meeting that we run at Flatiron for the NYC astrophysics community. One of the things I say about it is that if you want a meeting to be open, supportive, easy, and community-building, it has to have a strong set of draconian rules! In our most recent set of discussions, we have been talking about theming the meetings around specific science themes. Today was our first experiment with that! Joss Bland-Hawthorn (Sydney) is in town, so we themed the meeting around Galactic Archaeology. We had five short discussions; here are some highlights:

Megan Bedell (Flatiron) showed her incredibly precise 35-element (?) abundance measurements vs stellar age for her Solar twin sample. The abundances are very closely related to the age (for this sample that is selected to have Solar [Fe/H]). Suroor Gandhi (NYU) showed her results on the dependence on dynamical (or really kinematic) actions on [Fe/H] and age for low-alpha and high-alpha stars in the local Milky Way disk. These show that the two different sequences (high and low alpha) have different origins. And Rocio Kiman (CUNY) showed her M dwarf kinematics as a function of magnetic activity that could be used to constrain a disk heating model. All three of these presentations could benefit (for interpretation) from a forward model of star formation and radial migration in the Milky Way disk, along with heating! This is related to things I have done with Neige Frankel (MPIA) but would require extensions. Simple extensions, though.

Adam Wheeler (Columbia) showed us abundances he has measured all over the Milky Way from LAMOST spectroscopy, training a version of The Cannon with GALAH abundances. It's an amazing data set, and he asked us to brainstorm ideas about what we could do with it. He seems to have features in his catalog that look similar to the midplane issues that were causing me existential angst this past August. Bland-Hawthorn said that he sees similar things in the GALAH data too.

And Bland-Hawthorn himself talked about the possibility that some future instrument could measure stellar accelerations and get the Milky Way acceleration field directly! He started by commenting on the conclusions of the Bonaca et al work on a possible dark-matter perturber acting on the GD-1 stellar stream. His remarks played very well with things Bonaca and I have been discussing around making a non-parametric acceleration map of the Milky Way.

In summary: A great experiment!



After lunch, Alice Shapley (UCLA) gave a great Astro Seminar about what we can learn about high-redshift galaxies with multi-band photometry and infrared spectroscopy and, soon (or we hope soon!), JWST. There are hopes of seeing a consistent story in the star-formation rates, the build-up of mass, and the metallicity evolution in the stars and the interstellar medium.

At the end of the day, Andy Casey (Monash), Soledad Villar (NYU), and I met to discuss Villar's generation of APOGEE spectra of stars with a GAN, and how we might validate that. We discussed various options, but we are more-or-less converging on the idea that the spectra have to tell consistent or sensible stories about temperature and logg. I have ways to operationalize that. But one of the funny things is that real spectra of stars don't tell consistent stories! Because the physical models aren't awesome. So we can only require that the generated spectra do no worse than the real spectra.


EPRV at Yale

I spent an absolutely great and energizing day at Yale today, with the groups of Debra Fischer (Yale) and Jessie Cisewski-Kehe (Yale), who are working together to bring the best in hardware and the best in statistics to the hard problem of making (much) better than m/s-level radial-velocity measurements. We talked about many things, but highlights included:

How do you put uncertainty estimates on extracted spectral pixels? In the 2d-to-1d extraction step, the estimation of a single 1d spectral pixel is a modification of a least-square fit in the 2d image. How to put a good uncertainty on that, especially when the model isn't strictly linear least squares? We discussed Fisher-information estimates, which are best-case estimates, and also bootstrap or jackknife estimates, which are probably more conservative. The nice thing is that the EXPRES spectrograph (Debra Fischer's instrument) has many 2d pixels per 1d pixel, so these empirical methods like jackknife are possible.

What parts of the spectrum are most sensitive to activity? One approach is to find activity labels and perform a regression from spectral pixels to activity labels. Bo Ning (Yale) is taking this approach, with strong regularization to force most pixels to zero out in the regression. He finds plausible results, with the centers of certain lines contributing strongly to the regression. We discussed the kinds of tests one can do to validate the results. Ning also has evidence that the ability to find good activity indicators might be a strong function of spectral resolution, which is good for projects like EXPRES and ESPRESSO, which have very high resolution.

How can we measure radial velocities in the presence of stellar variability? We now think that stellar variability is the tall pole in EPRV. If it is, we have some deep and fundamental questions to ask here, since the whole edifice of relative RV measurement relies on the source being constant in time! We discussed different approaches to his ill-posed problem, including using only spectral information about RV that is somehow orthogonal to the spectral variability, or placing strong priors on the RV signal to separate it from the variability signal, or performing some kind of causal-inference-like regression. There is room for good theory here. Parker Holzer (Yale) is working on some theory along the orthogonality lines.


not much

Today was a low-research day! But I did have a brief conversation with Anu Raghunathan (NYU) about making her box-least-squares code more pythonic and more modular. And I did work a bit on the abstract and typographic macros in my M-type dwarf paper with Jessica Birky (UCSD).