writing

My only accomplishment today was a bit of writing in the method section of the write-up of my project with Kapala and Lang.

linear classifier for projections, Herschel

In the morning, Joakim Andén (Princeton) spoke at the SCDA about classifying noisy cryo-EM projected molecules into different categories coming from different (but similar) conformations of the same basic structure. These are subtle differences, and each data point (which is a tiny, noisy image) is only a randomly oriented projection of the molecule in question. He develops the best linear discriminant by finding the eigenvalues of the data matrix in the three-dimensional space, which is clever because he only has the data in (many) two-dimensional projections. His method works well and is fast. This is very relevant to the galaxy deprojection project I have going with Baron. The only significant issue with the method is that it assumes that the angles for the projections are known. They aren't really; it is interesting to think about the generalization to the case of unknown projection angles.

In the afternoon, Kapala (Cape Town), Lang, and I assigned short-term tasks for our Herschel dust-mapping project: Kapala will get the data in order and comment and understand the (legacy) code. Dustin will get the code working and delivering results, and I will write the abstract and outline the paper. We worked a bit on the title late in the day.

image modeling and optimization

The morning began with a talk by Oguz Semerci (Schlumberger-Doll) about optimization and inverse problems in imaging where the data are non trivially related to the image of interest and the problem is under-determined (requiring regularization). He showed examples from medical imaging, airport security, and oil exploration. Unlike many talks I see in these areas, he wasn't restricting to convex problems but still had very good performance. My only complaint would be that he was custom-building optimizers for each problem; I would be surprised if he beats the best industrial optimizers out there. Of course Lang and I are guilty of the same thing in The Tractor! One beautiful idea in his talk is the use of level sets to define the shapes of complex, opaque objects (or image segments).

Mid-day, Maria Kapala (Cape Town), Dustin Lang, and I had a chat about how to proceed on modeling Herschel imaging. Lang agreed to help Kapala get our old code from 2012 working again, and consult on optimization. I promised to start writing the paper (my favorite) and Kapala agreed to gather the relevant data for paper 1. (Somehow the two paragraphs of this research-blog post are more similar than I expected.)

high-resolution dust maps, data-driven SED templates

Maria Kapala (Cape Town) showed up today, to discuss analyses of multi-wavelength imaging. Our idea, which was born years ago in work with Lang, is to build a dust map that constrains dust density and temperature and emission properties using all the Herschel bands, but works at the angular resolution of the best band. The usual practice is to smooth everything to the worst band!

Also had a long conversation with Boris Leistedt (NYU) about learning the templates simultaneously with the redshifts in a template-based photometric-redshift system. This is the right thing to do: It captures the causal model that is inherent in the template-based systems, but also captures the data-driven advantages of a machine-learning method. I am interested to know how accurately and at what resolution we could recover templates in realistic fake-data tests. We scoped a first paper on the subject.

Dr Wu and Dr Duffell

Ronin Wu (CEA and Tokyo) came through NYU today, and told us about Herschel spectroscopy of M83. She finds that the spatially resolved and integrated spectra of star-forming galaxies seem to be saying slightly different things. She also made us think that the nucleus of M83 might be like a little mini-ULIRG.

At the end of the day, Paul Duffell (NYU) defended his PhD thesis. He had a huge crowd for the seminar and it was a great show, just as his thesis was a great read. He has moved supersonic numerical fluid simulation into some new regimes, with adaptive, moving meshes. His most exciting results (to me) are on the phenomenology of binary black holes, and (separately) gap-opening in protoplanetary disks. In both cases, he can make novel predictions for new observations. Gruzinov (NYU) and I encouraged him to make those predictions! Congratulations to Dr Paul Duffell on a great piece of work and welcome to the community of scholars (as it were).

dark matter

I spent part of the day preparing (and an hour giving) a public lecture on dark matter at the University of Toledo. I pitched it at undergrads, and got in some philosophical comments about how we know things. After the talk I had a great discusion with a bunch of physics majors at Toledo, who seemed extremely engaged with research. I also chatted with the locals about Herschel data, star formation, and galaxy evolution.

analytic derivatives

On a packing-and-travel-saturated day, research accomplishment was thin, although on the plane I took the first steps towards putting analytic derivatives into our high-resolution Herschel dust emissivity model. The derivatives should enormously speed optimization, if Fergus and Krishnan are to be believed.

goodbye MPIA

Today was my last day at MPIA for 2012. I spoke at length with Karin Sandstrom (MPIA) about fitting combined HI, CO, and dust-emission data, and with Sandstrom and Kapala about Kapala's comparison of ultraviolet stars with Herschel dust. I also had a final conversation with Tsalmantza about all the lensing-related work she has been doing; she is still finding more candidate gravitational lenses and has prepared a long document for publication or follow-up, filled with candidates. Expect them to hit my ideas blog any day now! I am not getting any traction on my Atlas, which is what I have to finish in the next two months! Don't distract me, people!

Valchava workshop, day four

Rix arrived in Valchava today, and (after a morning hike) proceeded to talk about making high-resolution models of multi-resolution (or heterogeneous) multi-spectral data, with the example being our dust maps made from Herschel data. One thing I learned in the discussion is that we maybe should be using L-BFGS-B optimization instead of the hacky Levenberg-Marquardt-like thing we are doing now. I was surprised, since Lev-Mar-like methods know that the problem is least-squares-like, whereas L-BFGS-B won't, but the experts (especially Dilip Krishnan) advised strongly.

more galaxies and more dust

In between talks by KG Lee (MPIA), Bovy, and Farrar (NYU), Mykytyn and I continued to work on the Atlas measurements, and Kapala and I looked some more into the relationship between Herschel dust maps and PHAT extinction measurements on luminous stars. The latter is quite a mess and we can't figure out what parts of the mess are problems with the star fitting or real variance in the extinction, and what parts of the mess are problems with the star fitting or real correlations between star and dust properties. We discussed with Groves.

Herschel emission predicts visible extinction

It's true, folks: The dust that radiates in the infrared is the same as the dust that absorbs in the visible and ultraviolet! Some may say this has been known for decades, but I successfully confirmed it today by comparing the dust models I can make for multi-band Herschel data on the M31 disk with extinction maps made from HST-imaging visible and UV stellar colors by Karl Gordon and Julianne Dalcanton of the PHAT Collaboration. My comparison is purely qualitative at present but I hope in the next week or two to make it quantitative. I want to find the combination of emission-inferred dust column, temperature, and emissivity parameter that best predicts the visible extinction; I then hope to find that it is only the column that is heavily involved in the prediction. That would be a great success of my (completely trivial, null) dust absorber and emitter model. I discussed all this today in one of the MPIA lounges with Ben Weiner (Arizona), Brent Groves (MPIA), Karin Sandstrom (MPIA), and Rix, with cameos by Thomas Henning (MPIA) and Tom Herbst (MPIA).

modeling multi-resolution data sets

I worked today on our Herschel side project, in which Lang and I attempt to model the dust emission in the M31 disk at the resolution of the Herschel 100-micron images, but using information from all the bands out to 500 microns, which are much lower in resolution. It all seems to work! But we need ways to test the results. Ideas that have come up include: Check that we reproduce the extinction map. Check that we predict well 70-micron and 1-mm data. Check that we don't find strong A_V vs temperature variations. Any other ideas? We get good residuals internally to Herschel, but obviously external tests are much more valuable.

not the h-word!

In the morning, Sarah Ballard (Harvard) gave a nice talk about finding exoplanets with Kepler plus follow-up. She effectively noted that the candidates most in need of follow-up are Jupiter-mass and Earth-mass, the former because of confusion from blended eclipsing binaries (which look very similar in time history) and the latter because of confusion from more massive planets, which (either from blending or just bad luck) at low signal-to-noise can look like Earth-mass planets. She said that there are very few false positives among the Kepler KOIs at other masses, which is what I have been hearing from other sources (think Johnson and Kjeldsen, recently). She showed very nice models of some transit-timing variations that can be explained by multiple planets in resonance. She also said a lot about habitability while only barely mentioning that word, which was appropriate, given that what is called by the h-word is often just about mass, radius, and mean intercepted radiation from the parent star.

In the rest of the day I discussed probabilistic photometric distances with Rix and Foreman-Mackey, discussed the intergalactic medium and its detectability in different regimes with Hennawi's group, discussed Herschel data with Lang, and watched Foreman-Mackey have an epiphany about the relationships between hierarchical inference, graphical models, and the expectation-maximization algorithm.

non-parametric Tractor

Lang and I pair-coded (him in Princeton, me in an office in Heidelberg with Foreman-Mackey, Marshall, and Price-Whelan forced to listen in) a version of the Tractor to model Herschel maps of warm dust in M31. Our model is a thermal emission model on a grid of point sources, smoothed with the (very variable with wavelength) point-spread function. We started by trying to do this by just making a huge grid of Tractor point sources with fixed positions and leaving everything up to the Tractor's excellent optimizer, but no way. So in the evening, Lang made a new Tractor model which is a non-parametric (think: lots of parameters) thermally emitting dust blanket. It worked! Now we just have to figure out how to make it fast enough that we can run it on large amounts of sky in practice. The idea of all of this is to get a Herschel-inferred dust map in M31 at the resolution of Herschel's best data, not smoothed to the resolution of Herschel's worst data.

At the other desks in the Camp Hogg office, Price-Whelan worked on object detection, photometry, and astrometry in multi-epoch data with variable conditions, Marshall worked on modeling lenses in PanSTARRS, and Foreman-Mackey worked on making The Thresher run on full-frame images, with and without drifting centroids, and on PanSTARRS data (to give something to our hosts here at MPIA).

interstellar medium

In transit to Paris, I worked on a problem given to me by Rix and Brent Groves (MPIA): Model the dust emission in M31 at the resolution of the highest-resolution Herschel maps, but constrained by all the maps. This is a perfect job for the Tractor but it requires creating new objects (think unresolved, emitting dust blobs). I didn't get very far, because it is a substantial generalization of what we have. What we have doesn't currently constrain the spectral energy distribution of any source very much a priori. That is, the Tractor doesn't yet have strong priors on emission mechanisms.