I am working on a simulation to predict the number of dwarf galaxies in the Milky Way that will be detected by LSST. We will ultimately use this to estimate the impact of LSST’s observations on dark matter indirect detection limits from the Fermi-LAT.
Our simulation is a Monte Carlo (right now with ~10k realizations) that we have selected to be representative of the Milky Way. This was accomplished by simulating the observations from SDSS, DES, and Pan STARRS1. Each realization thus has a set of mock dwarf galaxies (~few hundred) with properties such as: distance, location on sky, absolute magnitude, mass, surface brightness, half-light radius, etc.
The next task is to determine the probability that each mock dwarf galaxy in each of the realizations will be detected by LSST. I am very new to working with this sort of data and the LSST tools, etc., so any advice anyone could provide would be most appreciated.
Thanks for the post! I happened to run across your student’s poster on this work during the Fermilab virtual poster session yesterday, and I’d be happy to talk about it more. @Kabelo is a student at UChicago who has been working on the LSST data analysis side of this to come up with a selection function for Milky Way satellite detectability (a la what we did for DES and PS1 a few years back). I think it would be great to find a time to chat. Are you still around the Chicagoland area?
As someone who is most definitely “not an expert” on dwarf galaxies, I’m not entirely certain if this is the same thing, but if it is, I wanted to add a pointer to this notebook on Local Volume Dwarf detection estimates using MAF, contributed by Jeff Carlin.
Thanks @ljones, the notebook is definitely relevant. I am interested as well about this topic, as a matter of fact. One possible route, that we discussed in the past with @kadrlica is to imprint dwarf mock realization on DC2 image, using the source injection capabilities of the Rubin DM software. I was not in Chicago, unfortunately.