{"paper":{"title":"An application of machine learning techniques to galaxy cluster mass estimation using the MACSIS simulations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.GA"],"primary_cat":"astro-ph.CO","authors_text":"David J. Barnes, Scott T. Kay, Thomas J. Armitage","submitted_at":"2018-10-19T10:16:46Z","abstract_excerpt":"Machine learning (ML) techniques, in particular supervised regression algorithms, are a promising new way to use multiple observables to predict a cluster's mass or other key features. To investigate this approach we use the \\textsc{MACSIS} sample of simulated hydrodynamical galaxy clusters to train a variety of ML models, mimicking different datasets. We find that compared to predicting the cluster mass from the $\\sigma -M$ relation, the scatter in the predicted-to-true mass ratio is reduced by a factor of 4, from $0.130\\pm0.004$ dex (${\\simeq} 35$ per cent) to $0.031 \\pm 0.001$ dex (${\\simeq"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.08430","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}