Pix2Prof: fast extraction of sequential information from galaxy imagery via a deep natural language 'captioning' model

TL;DR

Pix2Prof is a deep learning model that rapidly extracts galaxy surface brightness profiles from images by treating the profiles as natural language captions, significantly outperforming manual methods in speed and scalability.

Contribution

The paper introduces Pix2Prof, a novel image captioning-inspired deep learning model that automates galaxy profile extraction, eliminating manual steps and enabling high-throughput analysis of large sky surveys.

Findings

Processes ~1 image/sec on CPU, over 100x faster than manual methods.

Capable of inferring profiles for 10^5 galaxies in under an hour on a single GPU.

Requires no manual interaction, facilitating large-scale astronomical data analysis.

Abstract

We present 'Pix2Prof', a deep learning model that can eliminate any manual steps taken when extracting galaxy profiles. We argue that a galaxy profile of any sort is conceptually similar to a natural language 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 galaxy profile inference. We demonstrate the technique by approximating a galaxy surface brightness (SB) profile fitting method that contains several manual steps. Pix2Prof processes $\sim$1 image per second on an Intel Xeon E5 2650 v3 CPU, 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 -- will be impractical.