Pix2Prof: fast extraction of sequential information from galaxy imagery via deep learning

We present "Pix2Prof", a deep learning model that eliminates manual steps in the measurement of galaxy surface brightness (SB) profiles. We argue that a galaxy "profile" of any sort is conceptually similar to an image caption. This idea allows us to leverage image captioning methods from the field of natural language processing, and so we design Pix2Prof as a float sequence "captioning" model suitable for SB profile inferral. We demonstrate the technique by approximating the galaxy SB fitting method described by Courteau (1996), an algorithm with several manual steps. We use g, r, and i-band images from the Sloan Digital Sky Survey (SDSS) Data Release 10 (DR10) to train Pix2Prof on 5367 image--SB profile pairs. We test Pix2Prof on 300 SDSS DR10 galaxy image--SB profile pairs in each of the g, r, and i bands to calibrate the mean SB deviation between interactive manual measurements and automated extractions, and demonstrate the effectiveness of Pix2Prof in mirroring the manual method. Pix2Prof processes $\sim1$ image per second on an Intel Xeon E5-2650 v3 and $\sim2$ images per second on a NVIDIA TESLA V100, improving on the speed of the manual interactive method by more than two orders of magnitude. Crucially, Pix2Prof requires no manual interaction, and since galaxy profile estimation is an embarrassingly parallel problem, we can further increase the throughput by running many Pix2Prof instances simultaneously. In perspective, Pix2Prof would take under an hour to infer profiles for $10^5$ galaxies on a single NVIDIA DGX-2 system. A single human expert would take approximately two years to complete the same task. Automated methodology such as this will accelerate the analysis of the next generation of large area sky surveys expected to yield hundreds of millions of targets. In such instances, all manual approaches -- even those involving a large number of experts -- would be impractical.