#tellurics, day 3

On the third day of Telluric Line Hack Week, I had many great conversations, especially with co-organizer Cullen Blake (Penn), who has had many astronomical interests in his career and is currently building the CCD part of the near-future NEID spectrograph. In many ways my most productive conversation of the day was with Mathias Zechmeister (Göttingen) about how we combine the individual-order radial-velocity measurements with wobble into one combined RV measurement. He asked me detailed questions about our assumptions, which got me thinking about the more general question. Two comments: The first is that we officially don't believe that there is an observable absolute RV. Only relative RVs exist (okay, that's a bit strong, but it's my official position). The second is that once you realize that you will be inconsistent (slightly) from order to order, you realize that you might be inconsistent (slightly) on all sorts of different axes. Thus, the RV combination is really self-calibration of radial-velocity measurements. If we re-cast it in that form, we can do all sorts of new things, including accept data from other spectrographs, account for biases that are a function of weather, airmass, JD, or barycentric correction, and so on. Good idea!

Despite the workshop, we still held the weekly Stars Meeting at Flatiron, and I am sure glad we did! Sharon X Wang (DTM) gave a summary of what we are doing at #tellurics, Dan Tamayo (Princeton) told us about super-principled numerical integrations that are custom-built for reproducibility (which is crazy hard when you are doing problems that are strongly chaotic), and Simon J Murphy (Sydney) told us about a crazy binary star system with hot, spotty stars. The conversation in the meeting pleased me: These meetings are discussions, not seminars. The crowd loves the engineering, computing, and data-analysis aspects to the matters that arise and we are detail-oriented!

expected future-discounted discovery rate

My tiny bit of research today was on observation scheduling: I read a new paper by Bellm et al about scheduling wide-field imaging observations for ZTF and LSST. It does a good job of talking about the issues but it doesn't meet my (particular, constrained) needs, in part because Bellm et al are (sensibly) scheduling full nights of observations (that is, not going just-in-time with the scheduling), and they have separate optimizations for volume searched and slew overheads. However, it is highly relevant to what I have been doing. It also had lots of great references that I didn't know about! They also make a strong case for optimizing full nights rather than going just-in-time. I agree that this is better, provided that your conditions aren't changing under you. If they are changing under you, you can't really plan ahead. Interesting set of issues, and something that differentiates imaging-survey scheduling from spectroscopic follow-up scheduling.

I also did some work comparing expected information gain to expected discovery rate. One issue with information gain is that if it isn't information gain in this exposure (and it isn't, because we have to look ahead), then it is hard to write down the information gain, because it depends strongly on future decisions (for example, if we decide to stop observing the source entirely!). So I am leaning towards making my first contribution on this subject be about discovery rate.

Expected future-discounted discovery rate, that is.

stellar ages and fish-eye cameras

Ness and I are getting red-giant ages from APOGEE spectroscopy using The Cannon and a training set from Kepler. We worked on making plots that would help us understand where this age information is coming from. Options include: Chromospheric activity (which decreases with time in stars as the magnetic field decays), dredge-up of C, N, O from the nucleosynthetic core (which pollutes the surface abundances over time), trace element abundances (which might indicate birth place and time beyond the information in gross indicators like metallicity and alpha-enhancement), and non-LTE effects (which might be different in different stars since convection patterns and scale are a function of mass).

Tom Herbst (MPIA) showed me his fish-eye all-sky camera and data acquisition system, and we discussed science projects for it with Markus Pössel (MPIA). The whole system is on the roof, so its computer and controller and everything are isolated from all the building systems to protect the building and its IT infrastructure from lightning strikes!

Rix and I asked Yuan-Sen Ting (Harvard) to compute derivatives with respect to elements for the stellar models being used to analyze the APOGEE data. The idea is that we want to ask how well we can linearize the models around fiducial points and then model (and therefore make measurements from) the spectra.

The Climate Corporation

I spent a bit of today at The Climate Corporation, hosted by former astronomer Todd Small. He told me about things they work on, which include field-level predictions for farmers about rainfall and sunlight, precise prediction and instructions for irrigation and fertilization, and advice about crop density (distances between seeds). He said they get data feeds from Earth observing but also from soil testing and even the farm machinery itself (who knew: combine harvesters produce a data feed!). A modern piece of large-scale farm equipment is precisely located on its path by GPS, makes real-time decisions about planting or whatever it is doing, and produces valuable data. They even have a tractor simulator in the house for testing systems.

We talked for a bit about the challenging task of communicating with clients (farmers, in this case) about probabilistic information, such as measurement uncertainties and distributions over predictions. This is another motivation for developing an undergraduate data-science educational program: It doesn't matter what industry you are in, you will need to be able to understand and communicate about likelihoods and posterior probabilities. I gave an informal seminar to a part of the climate modeling team about our uses of Gaussian Processes in exoplanet discovery.

cross validation and Bayes

Jake VanderPlas (UW) had the misfortune to be staying in a NYU guest apartment when Hurricane Sandy hit on Monday, taking out subway and then power and then water. In between fulfilling basic human needs for ourselves and our neighbors, we worked on the relationship between cross-validation and Bayes integrals. I think we have something to say here. It might not be original, but it is useful in understanding the relationships of methods. We both wrote some equations and then tried to develop a concordance today. We started a document. While we sheltered in the only location in lower Manhattan with power and internet, I also spoke by Skype with Lang about flexible sky models for The Tractor. Today ended with a discussion on long-term future discounted free-cash flow, about which I really must write an essay sometime very soon.