Fully Automated Approaches to Analyze Large-Scale Astronomy Survey Data

TL;DR

This paper discusses the shift towards fully automated data analysis pipelines in astronomy, highlighting an AI-driven approach for stellar astrophysics, specifically eclipsing binaries, and addressing the challenges of systematic errors.

Contribution

It introduces an AI-driven automated pipeline for analyzing large-scale astronomical survey data, focusing on stellar astrophysics and eclipsing binaries, and discusses associated challenges.

Findings

Prototype pipeline developed for eclipsing binaries

Initial results demonstrate feasibility of automation

Identifies key challenges in systematic error management

Abstract

Observational astronomy has changed drastically in the last decade: manually driven target-by-target instruments have been replaced by fully automated robotic telescopes. Data acquisition methods have advanced to the point that terabytes of data are flowing in and being stored on a daily basis. At the same time, the vast majority of analysis tools in stellar astrophysics still rely on manual expert interaction. To bridge this gap, we foresee that the next decade will witness a fundamental shift in the approaches to data analysis: case-by-case methods will be replaced by fully automated pipelines that will process the data from their reduction stage, through analysis, to storage. While major effort has been invested in data reduction automation, automated data analysis has mostly been neglected despite the urgent need. Scientific data mining will face serious challenges to identify, understand and eliminate the sources of systematic errors that will arise from this automation. As a special case, we present an artificial intelligence (AI) driven pipeline that is prototyped in the domain of stellar astrophysics (eclipsing binaries in particular), current results and the challenges still ahead.