Combining astrophysical datasets with CRUMB

Autor:	Porter, Fiona A. M., Scaife, Anna M. M.
Rok vydání:	2023
Předmět:	Astrophysics - Instrumentation and Methods for Astrophysics Astrophysics - Astrophysics of Galaxies
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted in Machine Learning and the Physical Sciences Workshop at NeurIPS 2023; 6 pages, 1 figure, 1 table
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2311.10507 Zobrazit plný text záznamu View this record from Arxiv