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.