2015/03/31 by Ben Hoyle, B. Hoyle, Markus Michael Rau +7 · 1 citation
Computer Science · Mathematics · Physics and Astronomy · #Advanced Statistical Methods and Models #Anomaly (physics) #Anomaly Detection Techniques and Applications #Astronomy #Astrophysics #Galaxies: Formation, Evolution, Phenomena #Galaxy #Photometric redshift #Physics #Redshift #Redshift survey #Sample (material) #astro-ph.CO
paper · pdf · doi:10.1093/mnras/stv1551
13 pages, 8 figures, 1 table, minor text updates to macth MNRAS accepted version
openalex publication_date 2015/08/11 · arxiv created 2016/06/15 · arxiv updated 2016/06/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We present an analysis of anomaly detection for machine learning redshift estimation. Anomaly detection allows the removal of poor training examples, which can adversely influence redshift estimates. Anomalous training examples may be photometric galaxies with incorrect spectroscopic redshifts, or galaxies with one or more poorly measured photometric quantity. We select 2.5 million ‘clean’ SDSS DR12 galaxies with reliable spectroscopic redshifts, and 6730 ‘anomalous’ galaxies with spectroscopic redshift measurements which are flagged as unreliable. We contaminate the clean base galaxy sample with galaxies with unreliable redshifts and attempt to recover the contaminating galaxies using the Elliptical Envelope technique. We then train four machine learning architectures for redshift analysis on both the contaminated sample and on the preprocessed ‘anomaly-removed’ sample and measure redshift statistics on a clean validation sample generated without any preprocessing. We find an improvement on all measured statistics of up to 80 per cent when training on the anomaly removed sample as compared with training on the contaminated sample for each of the machine learning routines explored. We further describe a method to estimate the contamination fraction of a base data sample.