forwards/backwards project evolution

Gaby Contardo (Flatiron) and I have been working on time asymmetry in NASA Kepler light curves. Our first attempts on this have been about prediction: Is it easier to predict a point in a light curve using its past or its future? It turns out that, for very deep mathematical reasons, there is a lot of symmetry here, even when the light curve is obviously time asymmetric in seemingly relevant ways. So deep, I think we might have some kind of definition of “stationary”. So we are re-tooling around just observable asymmetries. We discussed many things, including dictionary methods. It also occurred to us that in addition to time-reversal questions, there are also flux-reversal questions (like if you flip a light-curve event upside down).

working on a paper on the selection function

One research highlight today was working on the writing and organization of a paper on the defintion and use of the selection function in population studies (with, say, a catalog of observed sources). The paper is led by Hans-Walter Rix (MPIA), is aimed at the ESA Gaia community, and uses the white-dwarf luminosity and temperature distribution as its test case.

red dwarf population bifurcation

My loyal reader knows that Hans-Walter Rix (MPIA) and I have been looking at the population of white dwarfs as observed by ESA Gaia. This is demonstration project; it is somewhat adjacent to our usual science. However, today he ran our white-dwarf code for the much redder stars at the bottom of the main sequence (late M and brown dwarfs) and what did he find? It looks like the main sequence splits into two branches at the lowest-mass (coldest) end. Is that a discovery or known? And who could tell us?

CPM

I spent some weekend time working through the paper on NASA TESS detrending by So Hattori (NYUAD). It's beautiful, and pedagogical. I'm pleased.

comparing Bayesian and frequentist estimates of prediction error

I had an interesting conversation with Soledad Villar (JHU) about the difference between frequentist and Bayesian descriptions or analysis of the expected wrongness (out-of-sample prediction error) for a regression or interpolation. The different statistical philosophies lead to different kinds of operations you naturally do (frequentists naturally integrate over all possible data sets; Bayesians naturally also integrate over all possible (latent) parameter values consistent with the data). These differences in turn lead to different meanings for the eventual estimates of prediction error. I'm not sure I have it all right yet, but I'd like to figure it out and write something about all this. I'm generally a pragmatist, but statistical philosophy matters sometimes!

CPM rises from the ashes

I had a great call today with So Hattori (NYUAD) and Dan Foreman-Mackey (Flatiron), about Hattori's reboot of the causal pixel model by Dun Wang (that we used in NASA Kepler data) for new use on NASA TESS data. Importantly, Hattori has generalized the model so it can be used in way more science cases than we have looked at previously, including supernovae and tidal disruption events. And his paper is super-pedagogical, so it will invite and support (we hope) new users. Very excited to help finish this up!

an error model for APOGEE RV data

I worked with Adrian Price-Whelan (Flatiron) this morning on an empirical noise model for SDSS-IV APOGEE radial-velocity data. We fit a mixture of quiet and noisy stars plus additive Gaussian noise to empirical radial-velocity data, and started to figure out how the noise must depend on temperature, metallicity, and signal-to-noise. It looks like we can learn the noise model! And thus be less dependent on the assumptions in the pipelines.

more data, less good answer

I brought up the following issue at group meeting: When Lily Zhao (Yale) looks at how well spectral shape changes predict radial-velocity offsets (in simulated spectroscopic data from a rotating star with time-dependent star spots), she finds that there are small segments of data that predict the radial velocity offsets better than the whole data set does. That is, if you start with a good, small segment, and add data, your predictions get worse. Add data, do worse! This shouldn't be.

Of course whenever this happens it means there is something wrong with the model. But what to do to diagnose this and fix it? Most of the crowd was in support of what I might call “feature engineering”, in which we identify the best spectral regions and just use those. I don't like that solution, but it's easier to implement than a full shake-down of the model assumptions.

our forwards-backwards results are fading

Gaby Contardo (Flatiron) and I have been working on predicting light-curve data points from their pasts and their futures, to see if there is a time asymmetry. And we have been finding one! But today we discussed results in which Contardo was much more aggressive in removing data at or near spacecraft issues (this is NASA Kepler data). And most of our results go away! So we have to decide where we go from here. Obviously we should publish our results even if they are negative! But how to spin it all...?

reimplementing The Cannon

One of the things I say over and over in my group is: We build software, but every piece of software itself is not that valuable: Our software is valuable because it encodes good ideas and good practices for data analysis. In that spirit, I re-wrote The Cannon (Ness et al 2015) in an hour today in a Google (tm) Colab notebook. It's only ten-ish lines of active code! And ten more of comments. The Cannon is not a software package; it is a set of ideas. And my reimplementation has way more stable linear algebra than any previous version I've seen (because I've learned so much about this in the last few years, with help from Soledad Villar). I did the Cannon reimplementation for Teresa Huang (JHU), who is finding adversarial attacks against it.

emission-line ratios

I had a nice conversation today with Renbin Yan (Kentucky) and Xihan Ji (Kentucky) about work they have been doing with emission-line ratios. Typically these ratios are plotted on a “BPT” diagram (yes, named after people, unfortunately). Ji has been looking for more informative two-dimensional diagrams, by considering linear combinations of a larger set of ratios. He has beautiful visualizations! And he can also clearly show how the models of the line ratios depend on assumptions and parameters, which develop intuitions about what the ratios tell us, physically. We briefly discussed the possibility that we might actually be able to constrain nucleosynthesis parameters using emission-line spectra of nebulae!

not much

Today was a low-ish research day. In my research time, I discussed improvements to radial-velocity measurements with Adrian Price-Whelan (Flatiron) and gauge-invariant machine learning with Soledad Villar.

diagnosing data-analysis issues

I had a useful meeting with Lily Zhao (Yale), Megan Bedell (Flatiron), and Matt Daunt (NYU) to discuss Zhao and Daunt's various data-analysis projects in precision spectroscopy. In both cases, we spent a lot of time looking at figures (and, in Zhao's case, interactively making figures in the meeting). This is generic: We spend way more time looking at visualization of issues than we do reading the code that generates them. I think it's important too; code has to make sensible figures; reading code can lead to all sorts of confusions. And, besides, debugging follows the scientific method: You hypothesize things the code could be doing wrong, you design figures to make that would demonstrate the bug, you predict what those figures should and shouldn't show, you make the figures, and you conclude and create new hypotheses. It's funny, I currently don't think that Science (tm) follows the scientific method, but I think debugging scientific code does. Hmmm.

scoping papers about technical work

While sitting in a freezing-cold car (not mine!), I pair-coded with (well really watched code being written by) Adrian Price-Whelan (Flatiron) on the SDSS-IV APOGEE visit sub-frames; the idea is to get higher time-resolution radial-velocity information. In the conversation while code was being hacked, we set scope for a couple of possible papers: In one, we could show that we measure short-period binary orbital parameters more precisely (and more accurately?) with finer time-resolution measurements. In another, we could show that we can measure asteroseismic modes across the red-giant branch. We don't have either result yet, so I am just dreaming. But it's related to the point that it is sometimes hard to publish technical contributions to astronomy.

the 16th birthday of this blog

Today is the 16th birthday of this blog! Yes, this blog has been going for 16 years, and if I trust my platform, this post will be post number 3753. I had a great research day today. In Stars & Exoplanets meeting Rodrigo Luger (Flatiron) showed his nice information-theoretic results on what you can learn from stellar light curves about stellar surfaces, and Sam Grunblatt (AMNH) showed some planets that have—or should have—changing orbital periods as they inspiral into their host stars. I asked Grunblatt about the resonances that might be there, like the ones I just learned about in Saturn's rings: Are planet inspirals sensitive to asteroseismic resonances?

Before and after this meeting, Adrian Price-Whelan (Flatiron) and I continued working on measuring radial-velocities in SDSS-IV APOGEE sub-exposures. We find so many weird effects we are confused! We find sub-hour velocity trends but they seem to have the wrong slopes (accelerations) given what we know about the targets. It might have to do with badly masked bad pixels in the spectra...