2018-05-17

disk heating; cutting bait

Jonathan Bird (Vandy) is in town for two days, to finish a paper on heating in the Milky Way disk. The model is a hierarchical probabilistic model that generates the ages and vertical velocities of all the red-clump stars in a big part of the APOGEE data, where the ages come from C and N abundances from Ness and the velocities come from APOGEE and Gaia. He gets very precise answers! But there are deviations between the data and the model in the space of the data, and we debated how important these are to our conclusions.

Lauren Anderson (Flatiron) decided today that she has to down-select from many Gaia DR2 projects to one single Gaia DR2 project. Good idea! And in discussing this with her, I realized that I also needed to do this. We didn't get to final decisions.

2018-05-16

6-volume, myspace, rules, tellurics

Too many things today for one blog post! So just a rapid-fire list. Matt Buckley (Rutgers) and Adrian Price-Whelan (Princeton) and I discussed whether we could, in practice, measure phase-space six-volumes given a point-set in Gaia or a future data set. It isn't clear, so we started by designing some extremely simple simulations to test.

Price-Whelan and I discussed our myspace project to find the nonlinear transformation of the phase-space data near the Sun to make the phase-space structure as compact or informative as possible. We have a plan for implementation of the data-science side of the project, but we have no idea whether anything we find will be interpretable!

We had our first Stars Meeting under the new rules that we established last week. The objectives are, more-or-less: We want the presenters to be less prepared and we want the audience to be more engaged. We created some rules or guidelines to help achieve these objectives. And the meeting went well! Among other things that happened in this meeting, Price-Whelan showed a forming star cluster he found in the Milky Way halo, possibly connected to the Magellanic gas stream, and John Brewer (Yale) showed micro-tellurics (tiny atmospheric absorption lines) found in some of the very first R=150,000 EXPRES spectra.

On that last point: Brewer found these tellurics by observing a B star, which has no narrow lines (and almost no lines at all), so the narrow absorption lines must be intervening. Megan Bedell (Flatiron) has a data-driven method for finding tellurics even in very featured, narrow-lined spectra, by exploiting the causal structure: Star lines move with the star, atmosphere lines move with the atmosphere! She confirms at least qualitatively, at least some of Brewer's lines. I expect that we have some nice points to make in the comparison.

Oh, and: Unmodeled telluric absorption might be the limiting systematic in exoplanet RV surveys, right now or in the near future.

2018-05-15

Argh proposals!

My day was made low-research by the realization on waking that NASA ADAP proposals are due on Thursday, and not next week as I had, perhaps self-servingly, believed. That blew most of my day. Only research highlight was giving an informal talk at the NYU Center for Data Science, where I gave the crowd some idea of why and where we do data science in astronomy.

2018-05-14

integral-field spectroscopy

Today Dou Liu (NYU) gave a presentation of his thesis research as part of his candidacy exam. His first project has been to adapt the ideas in spectro-perfectionism from the spectral domain to the spatial domain to combine irregular, dithered imaging. He is applying this to integral-field spectroscopic data in MaNGA, which is part of SDSS-IV. He showed that he can get better angular resolution than the standard data-analysis methods, which are generally radial-basis-function interpolations of the data. One of his goals is to produce generally useful tools. Another is to re-process all of the MaNGA data. A great contribution, betterizing existing data that have already been hugely productive.

2018-05-11

data science and larva behavior

I had the great pleasure of being on the oral qualifying exam for Rui Wu (NYU), who is looking at the behavior and neural computation of fruit-fly larvae. She told us about her research so far (in preparation for her PhD project), in which she has built a fully data-driven model of larval behavior, classifying multiple different behaviors in an unsupervised model. She can also show that behavioral changes are correlated with changes to larval stimulus. She did all this by dimensionality-reducing video data with a set of clever techniques.

I learned an immense amount in her seminar. One is that they can genetically modify the larvae so that their olfactory senses can be stimulated with light! That's crazy but makes for better experimental techniques. Another is that they can read from individual neurons simultaneously with monitoring large-scale behavior. The fly is a model neural system that does complex things but with very few neurons, so there is a hope of reverse engineering the full computation. A truly out-there idea is that if the computation and behavior is understood, the larvae could be controlled or driven like an engineering system.

2018-05-10

moving things to the right of the bar

At lunch I gave the Flatiron CCA a taste of the science going on with Gaia DR2 in a short lunch talk. And before that I prepared my slides. All that counts as research by my rules.

My research highlight of the day, however, was a conversation with Neige Frankel (MPIA) about her probabilistic model for radial migration in the Milky Way disk. She doesn't have a good quantitative model for the selection function for her data, so she doesn't want her model to generate the three-space positions of the stars in her sample. At the same time, the structural parameters of the Milky Way disk are important. So she wants a model for the stellar properties, conditioned on the stellar positions. There are two ways to do this. The first is to write a graphical model where arrows only come from (and never go to) the stellar positions. We did that, but Frankel doesn't like that option, because the model only has simple analytic form when the arrows go to the positions.

The other option is to use the factorization formula p(a, b) = p(a|b) p(b). The stellar positions can be moved from the left side of the vertical bar to the right side by dividing by a pdf for the positions. We wrote that down, drew the relevant graphical models, and discussed changes to her text. She has beautiful results, TBA.

2018-05-09

Hipparcos binaries and Gaia velocity field

Now that Gaia DR2 has happened, we still organize parallel-working time at Flatiron for people to get together and hack on their Gaia projects. I worked on two things in this meeting today. In the first, I wrote to Floor van Leeuwen (Cambridge) about his Hipparcos data, because I learned last week that he released individual-visit astrometry for every star (hooray!), and that the visit astrometry has never really been comprehensively searched for arbitrary binary stars. I'm not sure I will do this! It's hard. But a project along these lines would be a great preparation for future Gaia data.

My second project in Gaia workshop was to talk out something with Adrian Price-Whelan (Princeton) that connects to things I have been talking about with Rix and others: In a very, very local sample (very near the Sun), the stars in velocity-space show a lot of informative structure. That is, some disk orbits are over-populated, and some under-populated. As you grow the region around the Sun, the structure in velocity space gets fuzzier, because the local velocity structure is a function of local position. Rix's view (which is sensible) is that we should look at this structure not in velocity space but in action space. That's a good idea! But if the structure is caused by non-axisymmetry in the disk, the actions computed in an axisymmetric potential won't be the clearest space. Let's find that space using the data themselves, and then interpret the transformation from more naive coordinates in terms of the dynamics. Price-Whelan and I came up with a first-step project, and an objective function to optimize. It looks do-able.

2018-05-08

big, fast viz of Gaia

I dealt with non-research things today! But late in the day, I got in some Gaia DR2 visualization time with Lauren Anderson (Flatiron)> She can in-real-time manipulate the entirety of DR2 (>1 billion stars) in a Jupyter notebook using vaex, which was designed (beautifully) for this purpose. We looked at halo substructure. I'm sure I'm seeing things in the halo, but how to know?

2018-05-07

Gaia halo stars; neutron lifetime

In the morning, Lauren Anderson (Flatiron) and I discussed Gaia DR2 projects. First we talked about things we could do with David Blei (Columbia) and his group, who have variational methods for extremely large inferences of the types we would like to do. We drew some graphical models (and posted them on twitter). Then we looked at halo red giants selected by parallax and color. Sagittarius shows up beautifully, and now it is time to start to look at other features. The data are incredibly rich.

Alberto Sirlin (NYU) gave the brown-bag talk, on the neutron lifetime. He showed that the neutron lifetime and a certain coupling are related, and showed that measurements of each, and their combination, are consistent, for at least some measurements. There are interesting puzzles though: Some kinds of lifetime measurements disagree with other kinds, and there was a step change in the coupling measurements in 2002-ish. So there are hints of new physics, but also a consistent no-new-physics story. He also showed that the simplest new-physics scenarios are not sensible. The neutron lifetime is important for many things, but especially big-bang nucleosynthesis.

2018-05-04

translation, geometry, deep learning, fast GPs

Friday-morning parallel-working session was brief today. I talked to Shiloh Pitt (NYU) about verifying matrix identities using numerical methods. And then we went downstairs for a mini-workshop at NYU CCPP organized by Kyle Cranmer (NYU) and Glennys Farrar (NYU) about physics and data science.

Cranmer led it off with an informal discussion of the different language used by statistics and computer science and applied math and physics. There are lots of words used differently, or that trigger different things. He mentioned “bias” and “correlation” and the uses or meanings of graphs and flowcharts. During the talks more words came up. One subtle one is that data scientists think of a data record as a point in data space (so, say, an image is a point in image space). That isn't always natural for physicists.

Joan Bruna (NYU) gave a nice talk about the geometric properties of deep learning, keying off of the success of convolutional neural networks. He said many interesting and insightful things, but here are a few that stuck with me: The convolutional symmetry at small scales in image space aids the NN in finding a distance metric (or something like that) between images that respects symmetries or structure that is really there. And it does that tractably, or in reasonable time. He claims that any compact symmetry group can be incorporated: That is, he claims that deep learning models can be made to exactly respect any symmetry that has certain properties. That's very exciting for physical applications. Distances between nodes on a graph also represent a geometry; it can be extremely different from geometry on simple manifolds! But the same ideas apply: If there are symmetries, they can be respected by the deep learning algorithms.

Life intervened! But by the end of the day, I made it to Flatiron to see a talk by Dan Foreman-Mackey (Flatiron) about data science, interdisciplinarity, open science, and finding planets around other stars. He gave a lot of credit to his interdisciplinary collaborations. He also mentioned the kinds of translation issues that Cranmer opened with at NYU. On the technical side, he showed his Gaussian-process methods and code and the near-linear scaling that they deliver. As I like to say: If you are doing linear algebra faster than N-squared (and he is, by far) then you can't even represent your matrices. That is, building the matrix itself is already N-squared. After his talk the Flatiron applied mathematicians were in heated arguments about exactly why (in a math sense) his methods are so fast. Foreman-Mackey's code is making possible things in astrophysics that have never been possible before.

2018-05-03

betterizing Gaia parallaxes

Because of various bits of bad luck, it was a low-research day today. The one real research thing I got into today was exploring all the nearly-geometric approaches to improving Gaia parallaxes. The idea is: If you are a hard-core astrometrist, you only believe geometric distances. And Gaia measures those! But how can you improve upon Gaia without bringing in additional assumptions about stars, stellar photospheres, stellar evolution, and so on? The answer is that you can't, trivially. However, you can think about approaches that use very minimal additional information, and nothing so dirty and gastrophysical as a stellar model:

You can use joint information of all the stars to improve every individual star! This is what we did in Anderson et al. We assumed that all stars come from a stationary distribution in color and magnitude, but we used a very flexible model for that and trained it entirely on purely geometric information. So it was like an amplification of the geometric information latent in the larger data set, applied to each individual star.

What Dustin Lang (Toronto), Megan Bedell (Flatiron), and I are thinking about is whether we can use stars that appear to move together to make new information. That is, if two stars are co-moving and near each other in an angular sense, they are very likely to be close in radial distance. So we can combine parallax information, and improve both stars. That is a purely geometric method, although it does make (fairly weak) assumptions about the existence of binary stars.

On another thread, Boris Leistedt (NYU) and I are thinking about how to use proper motion to constrain distances. This definitely makes strong assumptions about the Galaxy, but they are very reasonable and testable, and they exist only in the kinematic domain (not the gastrophysical). So that's promising. But it's early days.

To do better than Gaia, you have to make additional assumptions. Duh! But what are the most anodyne and conservative assumptions that we can make that still have the effect of betterizing parallax or distance inferences?

2018-05-02

spots, variational inference, bad sci-fi

After the #GaiaDR2 week and all the knock-on consequences, I'm starting to feel a little strung out this week! But I pulled it together for Stars meeting at Flatiron. Brett Morris (UW) was in town, and he talked about the degeneracies between transit depths and star spot statistics and other observables. He is generalizing the star surface model to properly capture those uncertainties. That's important for downstream inferences.

David Blei (Columbia) graced us with his presence. He categorized inference problems into a nested classification, with Gibbs-like problems in the center and fully implicit (you can do simulations but nothing else) problems on the outside. We have problems across this spectrum. He talked about how variational methods capitalize on optimization advances to deliver posterior approximations; this has limitations, but it is far faster than MCMC in most high-dimensional situations. He talked about many other things as well, and we looked at points of contact for collaborations. We are interested in scaling up things we did in the million-source TGAS to the billion-source Gaia DR2.

Late in the day, Rabbi Dan Ain (Because Jewish) and I did an event with Brian Sheppard (Seton Hall) at Caveat NYC, using the (bad) 80s film Short Circuit as our jumping-off point. It was ill-attended, but seriously fun.

2018-05-01

LIGO; GD-1

Right after lunch today, Ben Farr (Oregon) gave a seminar about how gravitational waves are detected by LIGO from a data-analysis perspective. He has been a big part of the team that made the LIGO results so probabilistically righteous! He gave some credit to the injection tests in the system for demonstrating that the Bayesian techniques were the highest-performing methods. He showed results on black-hole spins: He finds that either spins are low in amplitude, or else they tend to be aligned with one another and the orbital plane. That's already a fairly substantial result with 6-ish systems; and there will be many more soon. After his talk we discussed noise modeling.

Late in the day, I had a phone call with Ana Bonaca (Harvard) to discuss next steps after her beautiful paper on GD-1 in Gaia. I pitched a method for simulating dark-matter-subhalo interactions with streams that Kathryn Johnston (Columbia) and I worked out many years ago. We discussed how to make simulations that are realistic enough to be useful now.

2018-04-30

halo and Gaia

Lauren Anderson (Flatiron) and I looked at some simple selections of M-type giant stars in the Milky Way halo, to see if we see Sagittarius and other halo structures. They didn't jump out as obviously as I expected! But then we looked back at the Majewski et al paper and saw that their color selection was certainly not trivial! At the suggestion of Hans-Walter Rix (MPIA), we also looked at the Gaia CMD paper and the bimodal halo stars that were shown at the DR2 press conference. I am sure that the Milky Way halo will be full of interesting things! Now let's find them.

Also Megan Bedell (Flatiron) and I discussed our BetterTogether project, which has multiple goals. Her work today is to find planet hosts with comoving companions. One issue is that Kepler has such a low angular resolution, the matching between Kepler and Gaia might be difficult or ambiguous. Something to think about, given that we can't really re-photometer either dataset.

2018-04-27

#GaiaDR2 zero-day workshop, day 3

Today was the third and final day of our Gaia DR2 zero-day workshop. My goodness it was a fun week. Many of the participants told me that they would remember this week for the rest of their lives! Now that's not something I hear every day. And the participants here in NYC were very much focused on learning what is in the data, and exploring the data. There was no sense of trying to rush out publications or results. I loved the atmosphere.

In my own research today, I worked with Dustin Lang (Toronto) to understand the SDSS-III spectra that overlap the white-dwarf parts of the UV color–magnitude diagram that David Schiminovich (Columbia) and Lang showed yesterday. It wasn't obviously simple, but I have ideas about making a latent-variable model for it: Predicting spectra from photometry!

In the lunch-time check-in session there were some really impressive results. One was a big model for stellar physical parameters, and extinctions in a two-component model by Eddie Schlafly (LBNL). He pointed out that since Gaia gives distances and colors, it is sensitive to even fully gray extinction. So it provides a new window into extinction. Since his model involves simultaneously modeling stellar multi-band photometry (combined from many missions) along with the intrinsic properties of every star, it got big fast. I think it was at 800,000 parameters today. Optimized! That's pretty good for day three.

Another beautiful set of results at the check-in were visualizations of tidal features: Sarah Pearson (Columbia) visualized the tidal tails of Palomar 5, hoping to find them extend further than ever before. Chervin Laporte (UVic) visualized the anti-center stream and made the case that all of its kinematic properties are consistent with it being a tidal arm coming off the Milky Way from an interaction (with Sagittarius, I guess?). The morphology of the anti-center stream really is sharp, like a fold caustic.

In more general data-understanding categories, Sergey Koposov (CMU) scanned through proper-motion space, showing us low-parallax (that is, non-close) stars in different proper-motion bins. That highlighted a lot of streams, clusters, and anisotropies. And Andy Cassey (Monash) showed us how good (or bad) astrometric excess variance (and also radial-velocity excess variance) is at detecting binary stars. The answer is: Promising, but not calibrated usefully yet. In an ideal world we would build a self-calibrated model of what causes the variance and then use the residuals to detect binarity.

There were many more impressive things today, about the nearby volume, about comoving stars, about detailed chemical abundances, about the GD-1 stream (and possible progenitor!), and about kinematics of the disk and kinematics of the bar; too many things to mention here. Thank you, Gaia Collaboration.