binaries, velocities, Gaia

Early in the day, I discussed with Hans-Walter Rix (MPIA) the wide-separation binaries that Adrian Price-Whelan (Princeton) and I are finding in the Gaia DR1 data. He expressed some skepticism: Are we sure that such pairs can't be produced spuriously by the pipelines or systematic errors? That's important to check; no need to hurry out a wrong paper!

Late in the day, I had two tiny, eensy breakthroughs: In the first, I figured out that Price-Whelan and I can cast our binary discovery project in terms of a ratio of tractable marginalized likelihoods. That would be fun, and it would constitute a (relatively) responsible use of the (noisy) parallax information. In the second, I was able to confirm (by experimental coding) the (annoyingly correct) intuition of Dan Foreman-Mackey (UW) that the linearized spectral shift is not precise enough for our extreme-precision radial-velocity needs. So I have to do full-up redshifting of everything.


group meetings

At my morning group meeting, Will Farr (Birmingham) told us about CARMA models and their use in stellar radial velocity analysis. His view is that they are a possible basis or method for looking (coarsely) at asteroseismology. That meshes well with things we have been talking about at NYU about doing Gaussian Processes with kernels that are non-trivial in the frequency domain to identify asteroseismic modes.

In the afternoon group meeting, we had a very wide-ranging conversation, about possible future work on CMB foregrounds, about using shrinkage priors to improve noisy measurements of SZ clusters and other low signal-to-noise objects, We also discussed the recent Dragonfly discovery of a very low surface-brightness galaxy, and whether it presents a challenge for cosmological models.


data-driven models of images and stars

Today was a low-research day! That said, I had two phone conversations of great value. The first was with Andy Casey (Cambridge), about possibly building a fully data-driven model of stars that goes way beyond The Cannon, using the Gaia data as labels, and de-noising the Gaia data themselves. I am trying to conceptualize a project for the upcoming #GaiaSprint.

I also had a great phone conference with Dun Wang (NYU), Dan Foreman-Mackey (UW), and Bernhard Schölkopf (MPI-IS) about image differencing, or Wang's new version of it, that has been so successful in Kepler data. We talked about the regimes in which it would fail, and vowed to test these in writing the paper. In traditional image differencing, you use the past images to make a reference image, and you use the present image to determine pointing, rotation, and PSF adjustments. In Wang's version, you use the past images to determine regression coefficients, and you use the present image to predict itself, using those regression coefficients. That's odd, but not all that different if you view it from far enough away. We have writing to do!


measuring and modeling radial velocities

Dan Foreman-Mackey (UW) appeared for a few days in New York City. I had various conversations with him, including one in which I sanity-checked my data-driven model for radial velocities. He was suspicious that I can take the first-order (linear) approximation on the velocities. I said that they are a thousandth of a pixel! He still was suspicious. I also discussed with him the point of—and the mathematical basis underlying—the project we have with Adrian Price-Whelan (Princeton) on inferring companion orbits from stellar radial-velocity data. He agrees with me that we have a point in doing this project despite its unbelievably limited scope! Remotely, I worked a bit more on the wide-separation binaries in Gaia DR1 with Price-Whelan.


data-driven radial velocities

In my weekend research time, I worked out a fully data-driven method for measuring radial velocities in an extreme-precision (or even normal-precision) spectroscopic survey. The idea is to simultaneously fit for the spectrum of the star and its radial-velocity offset; you need multiple epochs of observations to get both (at least at high signal-to-noise). Because the model is fully data-driven, it won't give absolute radial velocities; it will only give relative velocities. That's always the cost of being data driven—the loss of interpretability.

I also added to the model some flexibility to capture spectral variations with time, especially those that might project onto the radial-velocity direction or measurement. That would permit us to discover and characterize spectral changes that co-vary with surface radial-velocity perturbations or jitter. I am trying to write something down that would be practical to apply to HARPS data, but I'm all theory right now.


Gaia thinking

I continued to think about and write about Gaia DR1 projects today. In particular, I tried to write down a responsible way to measure the standardness of standard stars, given noisy parallaxes. I also tried to understand whether we have a scope and interesting-enough results on wide-separation binary stars to merit a paper.


cross-correlation, Disco, Gaia, and life

My day started with a discussion of determination of stellar radial velocities by cross-correlation with a weighted mask (as is done in the HARPS pipeline) with Megan Bedell (Chicago). We talked about the subtleties of doing this, when there are partial-pixel shifts and we want answers that are continuous.

There was a substantial phone call today organized by Jonathan Bird (Vanderbilt) to talk about the After-SDSS-IV proposal for use of the 2.5-m telescope and its instruments. We are working on a proposal called Disco to do dense sampling of the Milky Way disk (looking in the infrared through the dust).

By text message, Adrian Price-Whelan (Princeton) and I tentatively decided that we would pursue a paper about wide binaries with the Gaia DR1 T-GAS data that we were exploring yesterday. I really hope we have a paper to write, because it would be fun to be in the first set of papers. Of course Gaia DR1 papers appeared on the arXiv already tonight!

At the end of the day, Sean Solomon (Columbia) gave the Departmental Colloquium, about Mercury and the Messenger mission. It was a great talk, showing that there is water and volatiles on Mercury, as not expected in naive models. At the end, Jasna Brujic (NYU) asked him about life on Mercury and elsewhere in the Solar System. He described the evidence that rocks are thrown from planet to planet and expressed the view (also held by me) that it is quite likely that there is life elsewhere in the Solar System. That made me happy!


#GaiaDR1 zero-day

Today (at 06:30 New York time) Gaia released it's DR1 data, and in particular the T-GAS sample of stars with five-parameter solutions and photometry. What a great day it was! I assembled with Kathryn Johnston (Columbia), David Spergel (Princeton), Adrian Price-Whelan (Princeton), Ruth Angus (Columbia), Keith Hawkins (Columbia), and others to get, play with, and make figures from the new data. Many amusing things happened, and this blog post will not capture them all.

Hawkins immediately plotted the velocity distribution of disk stars in the U-V plane, using the overlap between T-GAS and RAVE. He confirms the velocity structure Bovy, Roweis, and I predicted based on (clever, if I say so myself) de-projections of the Hipparcos data. Right as we were looking at this, Bovy tweeted the same thing. Hawkins has access to our RAVE-on data with detailed abundances, so he can show that the velocity structures are chemically inhomogeneous; the questions that are easy to ask are: Are they all inhomogeneous in the same ways, or are there differences? And can we see any spatial dependence within T-GAS of the velocity structure? He moved on to looking at the candle-standardness of the red clump.

Andy Casey (Cambridge), working remotely, made temperature-magnitude diagrams for the RAVE-on sample. I asked him to show what happens as you harden the cut on the parallax signal-to-noise (parallax over parallax uncertainty). He tweeted the answer. It really looks like we might be able to use Gaia to build a completely data-driven model of all aspects of stars.

Price-Whelan and I looked at various things. We started by trying to see if there is vertical velocity structure in the nearby disk that might show evidence for disk warping, or horizontal velocity structure that might look like spiral arm perturbations. The figures are confusing! There seems to be a very cold bubble around the Sun in the Galactocentric U velocity, which is odd. After spending lots of time confused about that, we looked for very wide separation binary stars, and we see lots! Indeed, it looks like we have evidence for binaries with separations larger than 1 pc! That's worth following up, especially if we have any overlapping spectra. Finally, Price-Whelan also showed that the Kepler-identified transiting exoplanet host stars are all on disk orbits; that is, we don't have (yet) any halo exoplanets. But these are early days!

That's just a tiny slice of the things we started to think about and play with. It is the beginning of a new era. Thank you to the Gaia Mission and all the people who gave years of their professional and scientific lives to this project.


a tiny bit of Gaia

Today was taken out by teaching and the job season, but in my tiny bit of research time, I worked on what I am going to do tomorrow with the Gaia DR1 data. That got me on the phone to Adrian Price-Whelan. We talked about Gaia and also our binary-star sampler.


nearly ready for Gaia DR1

I got obsessed on the weekend, and even more today, with the problem of getting the Gaia DR1 data in the form of a complete flat file, on Wednesday morning (that is, in two days) when the data release happens. Various sources have told us that we can't get a flat file, we have to do thousands to hundreds of thousands of remote database queries! In my fury about this, I tweeted (yes, twittered) at Jos de Bruijne (ESTEC), who promised me that the flat file would be available at the ESA Archive right away. Whew!

I decided that everyone on my team who is thinking about Gaia needs to figure out, today or tomorrow, what plot are they going to make? with the new data. That is, it is too big to think What science question am I going to ask?, so we should ask the simpler question about the plot / figure. I will send email to everyone about this tomorrow. I also noted the release in an email out to the #GaiaSprint participants.

In unrelated news, Andy Casey (Cambridge) and I discussed what we need to do to get ready for SDSS-IV APOGEE2 DR14. As my loyal reader knows, we are providing value-added content to DR14 using The Cannon. We worked out what our minimum deliverable is, what inputs and outputs that entails, what script writing that entails, and left it with Casey to communicate all that. We might deliver more than the minimum, but we want to only promise the minimum.


writing about radial velocity inference

I spent work on the weekend writing in Adrian Price-Whelan (Princeton) and my paper on exact sampling for the exoplanet or binary-star radial velocity problem. In addition to the writing, we spent a lot of time talking out the experiments that we want to show in this paper that are demonstrative of the power of the method. But my main contribution today was to blow out a discussion section as fast as possible. In our paper template, the discussion section talks about the implications of the work, but more importantly what has to change if the assumptions turn out to be wrong (and they do!). I wrote fast, obeying the motto “write drunk, edit sober.”


finding spectral twins in pixel space

As my loyal reader knows, I am a big believer in trying to use the immense data sets we have on stars (in particular spectral data sets) to build data-driven models that have some kind of interpretability. The problem is interpretability, because purely data-driven models are uninterpretable by construction. My biggest success along these lines is The Cannon, first built by Melissa Ness (MPIA) and followed up by Anna Ho (Caltech) and Andy Casey (Cambridge). An amusing and mostly true summary of machine learning is that all supervised methods are, fundamentally, nearest-neighbor methods. This suggests that we might be able to make massive progress if we just started to look at stars with identical or near-identical spectra. And of course I mean in the space of spectral pixels, not in the space of labels derived from those pixels. I pitched this project to Marc Williamson (NYU) today, and sent him off with some reading. We are going to look for twins, but accounting for variations in radial velocity and line-spread function, so it won't be completely trivial.

Megan Bedell (Chicago) pointed out to me today that the binary masks used by the HARPS pipeline appeared on arXiv today. That's big for our radial-velocity projects.


Simple Monte Carlo; a noise model

After the successes of yesterday on our custom radial-velocity sampler, currently called The Joker (but not pronounced how you might think), I put some time into writing the method section. One complex point of the sampling, which is fundamentally not Markov but instead just Simple Monte Carlo, is that if the SMC doesn't lead to many surviving samples, we either do more SMC or else switch over to a standard MCMC, initialized by the output of the SMC. That took some design thought; it capitalizes on an important point of problem structure, which is that—given a finite time window of observations—there is a finite resolution to likelihood peaks in the period direction. It remains to be seen if what we have designed will work.

Early in the day, I spoke with Andy Casey (Cambridge) about a possible noise model for the label outputs from The Cannon acting on the RAVE data. As my loyal reader knows, we consider the formal uncertainties coming from The Cannon to be under-estimates. It sounds like Casey has good evidence for a noise floor, which can be added in quadrature and make repeat visits to spectra more-or-less consistent. It's do-or-die because he needs to submit this paper today or tomorrow!


first group meeting at the SCCA

The single best thing today was the first meeting of the new joint group meeting of observational astrophysicists at the brand-spanking-new Simons Center for Computational Astrophysics. In addition to the union of Mike Blanton (NYU), Anthony Pullen (NYU), and my groups, the Director of the Center, David Spergel (SCCA) was there. We had great attendance; introductions alone took more than 90 minutes. Highlights for me included the following: A brief argument broke out about binary stars: Do we really know that both pairs of binaries have the same chemical abundances in detail? Spergel pointed out that the high-velocity stars that Keith Hawkins (Columbia) is finding could have implications for the early universe and the escape fraction of ultraviolet photons. Pullen talked about finding the SZ effect in filaments, Spergel mentioned work on identifying filaments using good machine learning by Shirley Ho (CMU), and MJ Vakili (NYU) talked about the work he has done on halo occupation, which could (in principle) take filament-environment as inputs. Hawkins also talked about a Gaia DR1 zero-day project; Adrian Price-Whelan (Columbia) and I promised to start off next week's group meeting with a visualization of the DR1 data, which at that point will be hours old!

Before and after group meeting, Price-Whelan and I worked on our binary-star (and exoplanet) sampler, building and executing experiments, and writing in the document. One amusing thing is that Megan Bedell (Chicago) gave us some (proprietary) exoplanet radial-velocity data to fit, where she finds one period but thinks there might be more. We found only her one period; confirming her results, but we found that we could get other possible periods if we drop her first data point or blow up her error bars (uncertainties).

One thing that came up in group meeting and I discussed afterwards with Hawkins is the project by Andy Casey (Cambridge), Hawkins, me, and many others to release a reanalysis of the RAVE data that overlap the Gaia DR1 T-GAS sample. Casey and Hawkins have detailed abundances for the red-giant stars, but not the main-sequence stars. I asked Hawkins why the main sequence is so much harder? He stunned me by saying that in fact he thinks the main sequence ought to be easier; we just have red-giant abundances now because people have worked harder on them. There's some medium-hanging (I won't say low-hanging!) fruit right there!


the best exoplanet spectrographs are much better than m/s

Today was the first day of the new academic year, so much of my day was obliterated by the most fun part of my job, which is teaching! That said, I still got in time for conversations with Megan Bedell, Dun Wang, and Boris Leistedt. I presented to Bedell my causal argument that instruments like HARPS are actually delivering much better than one meter per second precision and that the substantial scatter seen is because of the stars, not the instruments (and not the software pipelines). The argument is about the lack of covariance between measured radial velocities and (say) wavelength calibration parameters: Even if the noise contributing to wavelength calibration jitter is uncorrelated with the noise contributing to stellar jitter, it should show up as a covariance between calibration parameters and stellar radial velocity measurements. The argument is subtle, causal, and uncertain (because I am bad at this kind of reasoning). But if I am right, we don't need better instruments, and we don't need better pipelines. We need better models of stars!

Dun Wang and I discussed near-term and medium-term publishing plans. The top priority is to finish his paper on image differencing. I asked him to work hard on explaining how it is totally different from all other image differencing methods, because it uses the past images to learn regression coefficients, but builds the model of the present (target) image from other pixels in that image itself. That is, it is more general image modeling, really. And that's why it performs so well! Of course it requires a great data set for training.

Leistedt is using Gaussian Processes inside a physical model for galaxy observations given Doppler shifts. This is a completely flexible data-driven model, but constrained to obey the redshift physics implied by special relativity. That makes for a very powerful method for predicting galaxy colors at other redshifts, given an observation at a single (training) redshift. He can make photometric redshift predictions, make k corrections (my favorite), simulate future data, and train the photometric redshifts in survey A from a training set that exists only in survey B. All awesome! We discussed the scope of paper zero.