climate on hot jupiters

A no-research day (Thursdays are always bad) was ended on a great note with a Colloquium by Ian Dobbs-Dixon (NYUAD), who spoke about the atmospheres of hot-jupiter-like exoplanets. He has a great set of equipment that connects the global climate model built for Earth climate modeling with lots of planet-relevant physics (like strong, anisotropic insolation and internal heat flows) to figure out what must be happening on these planets. He showed some nice predictions and also some nice explanations of the observed property (yes observed property) that these planets do not have their hottest point at the sub-stellar point. It's so exciting when we think forward to what might be possible with NASA JWST.

hierarchical calibration

My main research contribution today was to write some notes for myself and Lily Zhao (Yale) about how we might start to produce a low-dimensional, hierarchical, non-parametric calibration model for the EXPRES spectrograph.

how does my watch even work?

At the end of a long faculty meeting at NYU Physics, my colleague Shura Grosberg came to me to discuss a subject we have been discussing at a low rate for many months: How is it possible that my watch (my wristwatch) is powered purely by stochastic motions of my arm, when thermal ratchets are impossible? He presented to me a very simple model, in which my watch is seen a set of three coupled systems. One is the winder, which is a low-Q oscillator that works at long periods. The next is the escapement and spring, which is a high-Q oscillator that has a period of 0.2 seconds. The next is the thermal bath of noise to which the watch dissipates energy. If my arm delivers power only on long periods (or mainly on long periods), then it only couples well to the first of these. And then power can flow to the other two systems. Ah, I love physicists!

milli-charged dark matter

As my loyal reader knows, I love the Brown-Bag talks at the Center for Cosmology and Particle Physics. Today was a great example! Hongwan Liu (NYU) talking about milli-charged dark matter. Putting a charge in the dark sector is a little risky, because the whole point of dark matter is that it is invisible, electromagnetically! But it turns out that if you include enough particle complexity in the dark sector, you can milli-charge the dark matter and move thermal energy from the light sector into the dark sector and vice versa.

Liu was motivated by some issues with 21-cm intensity mapping, but he has some very general ideas and results in his work. I was impressed by the point that his work involves the heat capacity of the dark sector. That's an observable, in principle! And it depends on the particle mass, because a dark sector with smaller particle mass has more particles and therefore more degrees of freedom and more heat capacity! It's interesting to think about the possible consequences of this. Can we rule out very small masses somehow?

using phase to interpolate between images

Continuing on stuff I got distracted into yesterday (when I should be working on NSF proposals!) I did some work on phase manipulation to interpolate between images. This was: Fourier transform both images, and interpolate in amplitude and phase independently, rather than just interpolate the complex numbers in a vector sense. It works in some respects and not in others. And it works much better on a localized image patch than in a whole image. I made this tweet to demonstrate. This is related to the idea that people who do this professionally use wavelet-like methods to get local phase information in the image instead of manipulating global phase. So the trivial thing doesn't work; I need to learn more!

substructure, phases, EPRV

Nora Shipp (Chicago) has been in town this week, working with Adrian Price-Whelan to find halo substructures and stellar streams around the Milky Way. The two of them made beautiful animations, paging through distance slices, showing halo stellar density (as measured by a color-magnitude matched filter). There are lots of things visible in those animations! We discussed the point that what makes overdensities appear to the human eye is their coherence through slices.

That made me think of things that Bill Freeman (MIT) and his lab does with amplifying small signals in video: Should we be looking for small overdensities with similar tricks? Freeman's lab uses phase transforms (like Fourier transforms and more localized versions of those) to detect and amplify small motions. Maybe we should use phase transforms here too. That led Price-Whelan and me to hack a little bit on this image pair by Judy Schmidt, which was fun but useless!

Late in the day, Megan Bedell (Flatiron), Lily Zhao (Yale), Debra Fischer (Yale), and I all met to discuss EXPRES data. It turns out that what the EXPRES team has in terms of data, and what they need in terms of technology, is incredibly well aligned with what Bedell and I want to do in the EPRV space. For example, EXPRES has been used to resolve the asteroseismic p-modes in a star. For another, it has made excellent observations of a spotty star. For another, it has a calibration program that wants to go hierarchical. I left work at the end of the day extremely excited about the opportunities here.

particle phenomenology

Today Josh Ruderman (NYU) gave a great Physics Colloquium, about particle physics phenomenology, from measuring important standard-model parameters with colliders to finding new particles in cosmology experiments. It was very wide-ranging and filled with nice insights about (among other things) thermal-relic dark matter and intuitions about (among other things) observability of different kinds of dark-sector activity. One theme of the dark-matter talks I have seen recently is that most sensible, zeroth-order bounds (like on mass and cross section for a thermal-relic WIMP) can be modified by slightly complexifying the problem (like by adding a dark photon or another dark state). Ruderman navigated a bunch of that for us nicely, and convinced us that there is lots to do in particle theory, even if the LHC remains in a standard-model desert.

more brokers

Our LSST broker discussions from yesterday continued at the Cosmology X Machine Learning group meeting at Flatiron. The group helped us think a little bit about the supervised and unsupervised options in the time-domain space.

LSST broker dreams

My day ended with a long conversation with Sjoert van Velzen (NYU), Tyler Pritchard (NYU), and Maryam Modjaz (NYU), about possible things we could be doing in the LSST time-domain and broker space. Our general interest is in finding interesting and unusual and outlier events that are interesting either because they are unprecedented, or because they are unusual within some subclass, or because they imply odd physical parameters or strange conditions. But we don't have much beyond that! We need to get serious in the next few months because there will be proposal calls.

complexifying optimal extraction

As my loyal reader knows, I have opinions about spectroscopic extraction—the inference of the one-dimensional spectrum of an object as a function of wavelength, given the two-dimensional image of the spectrum in the spectrograph detector plane. The EXPRES team (I happen to know) and others have the issue with their spectrographs that the cross-dispersion direction (the direction precisely orthogonal to the wavelength direction) is not always perfectly aligned with the y direction on the detector. This is a problem because if it is aligned, there are very simple extraction methods available.

I spent parts of the day writing down not the general solution to this problem (which might possibly be Bolton & Schlegel's SpectroPerfectonism, although I have issues with that too), but rather with an expansion around the perfectly-aligned case, that leads to an iterative solution, but preserving the solutions that work at perfect alignment. It's so beautiful! As expansions usually are.

What to call this? I am building on Zechmeister et al's “flat-relative optimal extraction”. But I'm allowing tilts. So Froet? Is that a rude word in some language?

combining data; learning rates

Marla Geha (Yale) crashed Flatiron today and we spent some time talking about a nice problem in spectroscopic data analysis: Imagine that you have a pipeline that works on each spectrum (or each exposure or each plate or whatever) separately, but that the same star has been observed multiple times. How do you post-process your individual-exposure results so that you get combined results that are the same as you would have if you had processed them all simultaneously. You want the calibration to be independent for each exposure, but he stellar template to be the same, for example. This is very related to the questions that Adrian Price-Whelan (Flatiron) and I have been solving in the last few weeks. You have to carry forward enough marginalized likelihood information to combine later. This involves marginalizing out the individual-exposure parameters but not the shared parameters. (And maybe making some additional approximations!)

As is not uncommon on a Friday, Astronomical Data Group meeting was great! So many things. One highlight for me was that Lily Zhao (Yale) has diagnosed—and figured out strategies related to—problems we had in wobble with the learning rate on our gradient descent. I hate optimization! But I love it when very good people diagnose and fix the problems in our optimization code!

intuitions about marginal likelihoods

Thursdays are low research days. I did almost nothing reportable here according to The Rules. I did have a valuable conversation with Price-Whelan (Flatiron) about marginalized likelihoods, and I started to get an intuition about why our factorization of Gaussian products has the form that it has. It has to do with the fact that the marginalized likelihood (the probability of the data, fully marginalizing out all linear parameters) permits or has variance for the data that is a sum in quadrature of the noise variance and the model variance. Ish!

code from the deep past

I had an amusing email from out of the blue, asking me to dig up the IDL (yes, IDL) code that I (and Blanton and Bovy and Johnston and Roweis and others) wrote to analyze the local velocity field using the ESA Hipparcos data. Being a huge supporter of open science, I had to say yes to this request. I dug through old cvs repositories (not svn, not git, but cvs) and found the code, and moved it to Github (tm) here. I didn't truly convert the cvs repo to git, so I erased history, which is bad. But time is precious, and I could always fix that later. I hereby apologize to my co-authors!

All this illustrates to me that it is very good to put your code out in the open. One reason is that then you don't have to go digging like this; a simple google search would have found it! Another is that when you know your code will be out in the open, you are (at least slightly) more likely to make it readable and useable by others. I dug up and threw to the world this code, but will anyone other than the authors ever be able to make any use of it? Or even understand it? I don't know.

calibrating a fiber spectrograph

I had my weekly call with Ana Bonaca (Harvard) this morning, where she updated me on our look at systematic effects in the radial-velocity measurements we are getting out of Hectochelle. We see very small velocity shifts in stellar radial velocities across the field of view that seem unlikely to be truly in the observed astrophysical stellar systems we are observing. At this point, Bonaca can show that these velocity shifts do not appear in the sky lines; that is, the calibration (with arc lamps) of the wavelengths on the detector is good.

All I have left at this point is that maybe the stars illuminate the fibers differently from the sky (and arc lamps) and this difference in illumination is transmitted to the spectrograph. I know how to test that, but it requires observing time; we can't do it in the data we have in hand right now. This is an important thing for me to figure out though, because it is related to how we commission and calibrate the fiber robot for SDSS-V. Next question: Will anyone give us observing time to check this?

nothing

Today was almost all admin and teaching. But I did get to the Astronomical Data Group meeting at Flatiron, where we had good discussions of representation learning, light curves generated by spotted stars, the population of planets around slightly evolved stars, and accreted stellar systems in the Milky Way halo!