Combining astrophysical datasets with CRUMB

2023/11/17 by Fiona Porter, Fiona A. M. Porter, Porter, Fiona A. M. +2 · 1 voice
Computer Science · Physics and Astronomy · #Algorithms and Data Compression #Data Stream Mining Techniques #Machine Learning and Data Classification #astro-ph.GA #astro-ph.IM

paper · pdf · doi:10.48550/arxiv.2311.10507

Abstract

At present, the field of astronomical machine learning lacks widely-used benchmarking datasets; most research employs custom-made datasets which are often not publicly released, making comparisons between models difficult. In this paper we present CRUMB, a publicly-available image dataset of Fanaroff-Riley galaxies constructed from four "parent" datasets extant in the literature. In addition to providing the largest image dataset of these galaxies, CRUMB uses a two-tier labelling system: a "basic" label for classification and a "complete" label which provides the original class labels used in the four parent datasets, allowing for disagreements in an image's class between different datasets to be preserved and selective access to sources from any desired combination of the parent datasets.

Discussions

• A day late, but my NeurIPS paper for the ML and the Physical Sciences workshop went up on arxiv yesterday! arxiv.org/abs/2311.10507 And as for why I'm late advertising it, I have a good excuse... [bsky, 3 points, 2 comments]

Related

• Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy
• FIRST radio galaxy data set containing curated labels of classes FRI, FRII, compact and bent
• MiraBest: A Dataset of Morphologically Classified Radio Galaxies for Machine Learning
• The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data
• Machine Learning Classification of COSMOS2020 Galaxies: Quiescent vs. Star-Forming