Autoencoding Galaxy Spectra. II. Redshift Invariance and Outlier Detection

Yan Liang, Peter Melchior, Sicong Lu, Andy Goulding, Charlotte Ward

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

We present an unsupervised outlier detection method for galaxy spectra based on the spectrum autoencoder architecture spender, which reliably captures spectral features and provides highly realistic reconstructions for SDSS galaxy spectra. We interpret the sample density in the autoencoder latent space as a probability distribution, and identify outliers as low-probability objects with a normalizing flow. However, we found that the latent-space position is not, as expected from the architecture, redshift invariant, which introduces stochasticity into the latent space and the outlier detection method. We solve this problem by adding two novel loss terms during training, which explicitly link latent-space distances to data-space distances, preserving locality in the autoencoding process. Minimizing the additional losses leads to a redshift-invariant, nondegenerate latent-space distribution with clear separations between common and anomalous data. We inspect the spectra with the lowest probability and find them to include blends with foreground stars, extremely reddened galaxies, galaxy pairs and triples, and stars that are misclassified as galaxies. We release the newly trained spender model and the latent-space probability for the entire SDSS-I galaxy sample to aid further investigations.

Original languageEnglish (US)
Article number75
JournalAstronomical Journal
Volume166
Issue number2
DOIs
StatePublished - Aug 1 2023

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science

Fingerprint

Dive into the research topics of 'Autoencoding Galaxy Spectra. II. Redshift Invariance and Outlier Detection'. Together they form a unique fingerprint.

Cite this