Fast and efficient identification of anomalous galaxy spectra with neural density estimation

Journal Article · Tue Sep 12 00:00:00 EDT 2023 · Monthly Notices of the Royal Astronomical Society

DOI:https://doi.org/10.1093/mnras/stad2773· OSTI ID:2008397

Böhm, Vanessa; Kim, Alex G.; Juneau, Stéphanie

ABSTRACT

Current large-scale astrophysical experiments produce unprecedented amounts of rich and diverse data. This creates a growing need for fast and flexible automated data inspection methods. Deep learning algorithms can capture and pick up subtle variations in rich data sets and are fast to apply once trained. Here, we study the applicability of an unsupervised and probabilistic deep learning framework, the probabilistic auto-encoder, to the detection of peculiar objects in galaxy spectra from the SDSS survey. Different to supervised algorithms, this algorithm is not trained to detect a specific feature or type of anomaly, instead it learns the complex and diverse distribution of galaxy spectra from training data and identifies outliers with respect to the learned distribution. We find that the algorithm assigns consistently lower probabilities (higher anomaly score) to spectra that exhibit unusual features. For example, the majority of outliers among quiescent galaxies are E+A galaxies, whose spectra combine features from old and young stellar population. Other identified outliers include LINERs, supernovae, and overlapping objects. Conditional modelling further allows us to incorporate additional information. Namely, we evaluate the probability of an object being anomalous given a certain spectral class, but other information such as metrics of data quality or estimated redshift could be incorporated as well. We make our code publicly available.

• Sponsoring Organization: USDOE
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 2008397
• Journal Information: Monthly Notices of the Royal Astronomical Society, Journal Name: Monthly Notices of the Royal Astronomical Society Journal Issue: 2 Vol. 526; ISSN 0035-8711
• Publisher: Oxford University Press
• Country of Publication: United Kingdom
• Language: English