Autoencoding Galaxy Spectra II: Redshift Invariance and Outlier Detection

TL;DR

This paper introduces an improved autoencoder-based outlier detection method for galaxy spectra that achieves redshift invariance and effectively identifies various types of spectral anomalies, aiding astronomical data analysis.

Contribution

The authors develop a novel loss function to ensure redshift invariance in the autoencoder's latent space, enhancing outlier detection accuracy in galaxy spectra.

Findings

The method reliably detects outliers such as blended stars and galaxies.

The latent space becomes more interpretable with clear separation of anomalies.

The trained model and probability scores are publicly released for SDSS galaxy data.

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, non-degenerate 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.