stellar surface imaging

Today Rachael Roettenbacher (Yale) gave the CCA Colloquium, on Doppler imaging and interferometry and other methods for imaging stellar surfaces. In conversations with Roettenbacher and others (including Lily Zhao at Yale and Megan Bedell and Rodrigo Luger at Flatiron), I'm coming around to the position that extremely precise radial-velocity measurements of stars will require accurate models of rotating, spotty, time-evolving stellar surfaces. At the end of her talk, there were some discussions about this point: Should EPRV surveys be associated with stellar surface imaging campaigns? Probably, if we want to characterize true Earth analogs!

ASKAP

Nick Pingel (ANU) came by Flatiron and impressed us all with discussions of ASKAP, which is one of the pathfinders to the SKA. The most impressive thing I learned is that the feeds for the telescope array are themselves dipole arrays, so you can synthesize multiple beams at each telescope, and then synthesize an aperture for each beam. That's a great capability for the array, but of course is also an engineering challenge. He said scary things about what the calibration looks like. It really made me wish I had got closer to radio astronomy in my life!

optimization is the worst

After the incredibly valuable Milky Way Group Meeting discussion of the spectrophotometric parallaxes, Eilers (MPIA) and I simplified our model, re-factored the code, and re-ran. And, despite the fact that the new model is provably better than the old model, everything failed. The reason is: Our objective isn't convex. Not only that, but there is an enormously high-dimensional degenerate bad optimum that is hard to avoid. That sent us back to the books: Optimization is hard!

The trick we settled on (and you are allowed to do many, many tricks here) is to take the very highest signal-to-noise stars (in terms of Gaia parallax) to optimize an initialization and then do our final optimization with all stars, but starting off from that initialization. That is, we burn in to the optimum using the best stars first. It's a hack but it worked, and now the better model is performing the way it should be. That's good! Because it is discouraging when you refactor your code and everything goes worse.

A MPIA Galaxy Coffee, Wolfgang Brandner (MPIA) described the new GRAVITY results on the perihelion passage of S2 at the Galactic Center. The perihelion passage shows gravitational and transverse-Doppler redshifts and puts an amazingly strong constraint on the geometry and kinematics of the Galactic Center.

gravitational clustering, gravitational interferometry

Today Michael Joyce (LPNHE) gave a great talk about analytic and conceptual directions towards understanding nonlinear gravitational growth of structure in the Universe. He focused on the stable-clustering approximation, which dates back to Peebles, is very predictive over a range of scales, and can be used to test simulations. At lunch afterwards, we discussed the great importance of studying gravity analytically, a point made often and well by Roman Scoccimarro (NYU).

Prior to the seminar, Ellie Schwab-Abrams (AMNH) and I discussed self-calibration for pulsar timing arrays, which we think and hope could lead to a new era of gravitational interferometry and enormously increase the sensitivity to long-term gravitational-wave signals. We decided to start by solving the radio-astronomy problem, which has yet to be solved in the literature, because no radio telescope has the problem that the relative velocities of it's elements are unknown!

group meeting, self-calibration for GR, writing

Today we had the first-ever Astronomical Data Group Meeting. The rules are: You must bring a plot, and you get a time period of (1 hr)/N where N is the number of people in the room to get feedback. It was fun: All of the plots (even Foreman-Mackey's) related to the Gaia DR2 data. I asked the crew whether the stars below the main sequence in the Gaia color–magnitude diagram are very low in metallicity? And if so, shouldn't we take spectra? Anderson thinks maybe they are just issues with crowded fields. That is, issues in the data. Problems with chasing outliers!

After that I had long sessions with Ellie Schwab-Abrams (CUNY), and Jonathan Bird (Vanderbilt). Schwab-Abrams and I are trying to convert my question about self-calibrating gravitational-wave pulsar-timing arrays into the equivalent question about self-calibrating radio telescopes. It is very similar! But we have to take into account the 6-space position not the 3-space position, and we also have to deal with light travel time issues that we can't control with delay lines! But the payoff is immense: I naively expect a factor of more than a billion increase in sensitivity of the arrays if we can do it. Yes I said billion. I hope I'm right.

Bird is finishing his paper on the age–velocity relationship in the disk. We went over discussion points. I recommended explicitly challenging the assumptions and saying what we think would happen if we relaxed them, both in terms of the results and in terms of model complexity. My problem (as it often is in projects) is that I care about the method much more than the astrophysical results.

chemistry in protoplanetary disks

My research highlight today was a great talk by Ilse Cleeves (Harvard) about ALMA observations of the dust and molecular gas in proto-planetary disks. She showed that you can see chemical gradients in the disks, including rings and lines of formation of molecules. Much of what's visible is on the outer surfaces of the disk, which is illuminated by the young, accreting star, because the interior parts of the disk are optically thick. Because the chemical models are heavy and imperfect, I proposed looking at latent-variable models to describe the observed molecular abundances; maybe there are interesting features to be found even without relying on physical or chemical models?

The most remarkable thing she showed is time-domain chemical results: She can see chemical changes in real time as the disk responds to (presumably) stellar flares on day-ish time-scales! We discussed methods for distinguishing different kinds of events that might be triggering the chemical changes. Expect denser time sampling in the future; it's ALMA proposal-writing season!

Technically, my favorite part of Cleeves's work is that she does all of her model-fitting and hypothesis comparing in the visibilities. That is, she doesn't make an image from the interferometric data and then model it: She takes her models to the Fourier domain and compares to the raw data. That's classy.

Gaia-based training data, GANs, and optical interferometry

In today's Gaia DR2 working meeting, I worked with Christina Eilers (MPIA) to build the APOGEE+TGAS training set we could use to train her post-Cannon model of stellar spectra. The important idea behind the new model is that we are no longer trying to specify the latent parameters that control the spectral generation; we are using uninterpreted latents. For this reason, we don't need complete labels (or any labels!) for the training set. That means we can train on, and predict, any labels or label subset we like. We are going to use absolute magnitude, and thereby put distances onto all APOGEE giants. And thereby map the Milky Way!

In stars group meeting, Richard Galvez (NYU) started a lively discussion by showing how generative adversarial networks work and giving some impressive examples on astronomical imaging data. This led into some good discussion about uses and abuses of complex machine-learning methods in astrophysics.

Also in stars meeting, Oliver Pfuhl (MPA) described to us how the VLT four-telescope interferometric imager GRAVITY works. It is a tremendously difficult technical problem to perform interferometric imaging in the optical: You have to keep everything aligned in real time to a tiny fraction of a micron, and you have little carts with mirrors zipping down tunnels at substantial speeds! The instrument is incredibly impressive: It is performing milli-arcsecond astrometry of the Galactic Center, and it can see star S2 move on a weekly basis!.

self-calibrating pulsar arrays, and much more

I had a great conversation with Chiara Mingarelli (Flatiron) and Ellie Schwab (AMNH) today about pulsar-timing arrays and gravitational-wave sources. We are developing some ideas about self-calibration of the arrays, such that we might be able to simultanously search for coherent sources (that is: not just stochastic backgrounds) and also precisely determine the distances to the individual pulsars to many digits of accuracy!. It is futuristic stuff, and there are lots of ways it might fail badly, but if I am right that the self-calibration of the arrays is possible, it would make the arrays a few to tens of times more sensitive to sources! We started with Mingarelli assigning us some reading homework.

In the Stars group meeting, we had a productive discussion led by Megan Bedell (Flatiron), Andrew Mann (Columbia), and John Brewer (Yale) about things learned at the recent #KnowThyStar conference. There are some new uses of machine learning and data-driven models that I might need to spend some time criticizing! And it appears that there are some serious discrepancies between asteroseismic scaling relations for stellar radii and interferometric measurements. Not bigger than those expected by the stellar experts, apparently, but much bigger than assumed by some of the exoplanet community.

Prior to that, in our weekly Gaia DR2 prep working session, we discussed the use of proper motion as a distance indicator in a post-reduced-proper-motion world. That is: The assumptions underlying reduced proper motion are not great, and will be strongly violated in the DR2 data set. So let's replace it with a much better thing!

Adrian Price-Whelan (Princeton) showed some incredible properties of (flowing from beautiful design of) the astropy coordinates package. Damn!

inference of bolometric fluxes

Rix and I discussed many data analysis problems in the morning today. We have been discussing the possibility of measuring the bolometric fluxes of stars with very little (possibly vanishing) dependence on spectral assumptions. (The idea is: If you have enough bands, the spectrum or SED is very strongly tied down.) If we can combine these with other kinds of measurements (of, say, effective temperature), we can make predictions for interferometry without (heavy use of) stellar models! One constant that comes up in these discussions is 4πGσ (the gravitational constant times the Stefan-Boltzmann constant (have I mentioned that I hate it when constants are named after people?)). Is this a new fundamental, astronomical constant?

We also discussed with Coryn Bailer-Jones (MPIA), Morgan Fouesneau (MPIA), and Rene Andre (MPIA) actually putting the bolometric-flux project into practice with real data. Fouesneau and Andre seem to have working code!

I also had the pleasure of reading and giving comments on some writing Bailer-Jones has been doing for a possible new textbook on computational data analysis. This is exciting!

web cams and James Bradley

Over lunch, Markus Pössel (MPIA) mentioned that he can measure the sidereal day very accurately, using a fish-eye or wide-field web cam pointed at the sky. This led us to a discussion of whether it would be possible to repeat Bradley's experiments of the 1700s that measured stellar aberration, precession of the Earth's axis, and nutation. Pössel had the very nice realization that you don't have to specifically identify any individual stars in any images to do this experiment; you can just do cross-correlations of multi-pixel time series. That's brilliant! We decided to discuss again later this month along with a possible (high school) student researcher.

Before that, Roberto Decarli (MPIA) and I discussed various projects. The most interesting is whether or how you can "stack data" (combine information from many images or many parts of an image) but in interferometric imaging data. Decarli has shown that you can do this stacking in the fourier space rather than in the image space. That's excellent, because the noise properties of the data are (conceivably) known there, but never understood properly in the image space. I gave him my usual advice, which is to replace the stacking with some kind of linear fit or regression: Stacking in bins is like linear fitting but under hard assumptions about the noise model and properties of the sources. We agreed to test some ideas.

interferometry, meet probability

Great morning chats with Jahnke (MPIA) about Euclid survey and calibration strategy and Kapala (MPIA) about comparing H-alpha emission to C+ emission in M31. After that, Malte Kuhlmann (MPI-IS) arrived to discuss generative probabilistic approaches to radio interferometry (think: replacing CLEAN). We discussed his compressed-sensing-inspired methods (which produce nicely regularized point estimates) and confronted him with some highly astronomical considerations: How do we create something that is not just righteous but also gets rightly adopted by a significant user base? And: How do we make it so that we can use the output of our code to more responsibly evaluate significance and more responsibly propagate uncertainty than what is currently industry-standard in radio astronomy? On the former, a key idea is that whatever we do has to fit into the standard radio-imaging workflow. On the latter, the key idea is that we need to exercise a justified likelihood function. I have a very good feeling about this project. Kuhlmann's background is in math and stats, which is a good background for bringing important new ideas into astrophysics. The day ended with a great talk by Watkins (MPIA) about omega Cen, the content of which also inspired a spirited discussion of globular clusters on the 17:48 bus.

data analysis consulting, mixture models

Julianne Dalcanton (UW) gave a great Galaxy Coffee talk about mapping the dust in M31, by a very clever mixture-model fitting approach, fitting the extinctions towards red-giant stars in the PHAT data. She shows amazing angular resolution and incredible relationships with emission measures from infrared and millimeter. And that after a great Galaxy Coffee talk from Aaron Dutton (MPIA) summarizing the meeting "The Physical Link between Galaxies and their Halos". He did the very clever / sensible thing of choosing the three things about the meeting that most impressed him, and only talking about those.

I got back into my "data analysis guru" mode today, with long conversations with a group (including Smolcic and Groves and many others) that is trying to detect very faint sources in very deep JVLA imaging of blank fields: How do you know that the sources you are seeing are real, and how do you measure their properties? Much of the non-triviality comes from the fact that the raw data are interferometry visibilities and the maps are made with (relatively speaking) black boxes. I was a very strong advocate of jackknife (or full likelihood modeling, which is a great plan but outrageously hard given where everyone is right now).

I also spoke a bit with Watkins (MPIA) and van de Ven (MPIA) about modeling dynamical systems in the presence of a large-amplitude background, such that the model must include not just the dynamical system of interest (a stellar cluster, in this case) but also the foreground or background (the non-cluster stars of the galaxy or Galaxy, in this case). I worked through my understanding of the mixture models that the Loyal Reader (tm) knows so much about. It gets confusing when the mixture amplitudes are conditioned on observables or data that are not explicitly being modeled in the likelihood; for example in the Watkins case, the velocity distribution is a mixture of components, but the mixture amplitudes depend on position. I originally recommended modeling position and velocity simultaneously, but given the crazy selection of stars they face, it is better to model velocity conditioned on position. This makes the mixture less trivial.

In general we would all be better off if we understood mixture models much better. They obviate hard classification and capture a lot of our ideas about how our data are generated.

Ballard

Sarah Ballard (UW) came in for the day to give an outstanding talk about habitability and exoplanets. Her talk was a beautiful balance between the general (exoplanet populations and host stars; you can't characterize a planet if you can't characterize its star; M dwarfs have lots of planets but bad spectral models; etc) and the specific (how Kepler 61b went from being habitable and Earth-like to not either). She pointed us to some great literature on the statistics of planets and false positives. Among the many tidbits from the talk: She repeatedly called exoplanet researchers exoplaneteers. She predicted that there would be a rocky, habitable-zone planet discovered in 2013. She made the point that directly measured stellar diameters (from optical interferometry) have revolutionized the field. I can't help mentioning also that the talk was hilarious and interactive, with many audience members participating, just like a seminar (in my mind) should be. I love my job!

writing down my thoughts

After great conversations in Valchava, visions of sugar-plums and spike-slab priors have been dancing in my head. So I spent today (and much of the past weekend) writing furiously a couple of documents. The first is a white-paper regarding a justified probabilistic approach to radio interferometry data. This captures things my loyal reader has been reading about for the last few weeks, plus many new ideas coming from Valchava.

The second document is a white-paper regarding the possibility of calibrating the pixel-level flat-field map (that is, many millions of parameters) for an imaging camera using the science data alone (that is, no sky or dome flats). This latter project would work by looking at consistency among neighboring pixels given (and this requires assumptions) an observing program that observes without trying to put particular science objects in particular locations. One of the things I love about this project is that sky flats can be very misleading: The smooth sky illuminates the detector differently than a star does, because there are often unbaffled stray light paths and reflection paths. I have been thinking about these issues because they might be applicable to the Euclid mission, which currently plans to rely on internal LED illuminators for calibration information.

I am not sure why I write these white papers. They are a huge amount of work, and although they are great for organizing and recording my ideas, almost none of them ever results in a real publication.

Valchava workshop, day five

Today was radio-interferometry day. Rix and I explained our issues with CLEAN, and we discussed with the computer vision types. Fergus described CLEAN as a conservative greedy algorithm, which is very appropriate. In the end we had some good ideas about how to attack the problem, with agreement from everyone. The only point of concern was between those (Harmeling representing) who thought we might be able to proceed without strong priors and those (Fergus and Schuler representing) who thought that strong priors would be necessary to deal with the spatial frequencies at which there is no u-v coverage. At the end of the day I was assigned the task of writing down what we learned and agreed upon. I started on that.

One interesting thing about CLEAN is that it averages visibilities on a grid in u-v so that it can use the fast fourier transform. This is a great idea, but makes forward modeling harder. We are hoping we can stay fast without this trick. In our vision (codenamed NAELC) we never transform in that direction anyway.

modeling galaxies and PSFs

Mykytyn continued to work on our measurements of huge galaxies, after a lot of fiddling around with various kinds of galaxy radii. He also figured out that we can measure M51a no problem, and M51b will probably work too, so we can do some pretty big galaxies with The Tractor.

While he was shepherding fits, I was building a data-driven model of the psf from the Fizeau Interferometer called LMIRcam on LBT (thanks to Andy Skemer of Arizona). Here's an example (below). The left is the data, and the right is my model. It looks just like a textbook example of an optical interferometry PSF (which up to now have only appeared in texbooks, never in the readout data from real instruments)!

PHAT Camp, day four; optical interferometry

Foreman-Mackey and I worked out the differences between, as it were, the Bovy view of the world and the Foreman-Mackey view of the world (well, the world of variable-rate Poisson processes subject to censoring). Of course the two views are mathematically identical in the end!

As work continued in PHAT land, I escaped for an hour to see an outrageous talk by Andy Skemer (Arizona) on high-resolution imaging with the LBT. He showed an absolutely beautiful interferometric image at four microns of a star made with the LMIRcam interferometer. It looked like what we see in textbooks! He also showed a video of the image over time, showing that the phase difference between the telescope's two mirrors varies stochastically with time, probably because of atmospheric variations. Foreman-Mackey and I discussed whether we could model these data—the individual-telescope AO PSF is so stable, the different images have only one degree of freedom, which is the unknown phase difference between mirrors. That's a one-parameter family of PSFs and 0.04 arcsec resolution in the good direction.