Multi-Class Deep SVDD: Anomaly Detection Approach in Astronomy with Distinct Inlier Categories

TL;DR

This paper introduces Multi-Class Deep SVDD, a novel anomaly detection method tailored for astronomy that effectively distinguishes anomalies across multiple inlier categories using neural network hyperspheres.

Contribution

It extends Deep SVDD to handle multiple inlier categories with distinct distributions, improving anomaly detection in astronomical datasets.

Findings

MCDSVDD outperforms existing anomaly detection algorithms on astronomical light-curve data.

The method effectively identifies anomalies across different inlier categories.

Results are validated on data from the Zwicky Transient Facility.

Abstract

With the increasing volume of astronomical data generated by modern survey telescopes, automated pipelines and machine learning techniques have become crucial for analyzing and extracting knowledge from these datasets. Anomaly detection, i.e. the task of identifying irregular or unexpected patterns in the data, is a complex challenge in astronomy. In this paper, we propose Multi-Class Deep Support Vector Data Description (MCDSVDD), an extension of the state-of-the-art anomaly detection algorithm One-Class Deep SVDD, specifically designed to handle different inlier categories with distinct data distributions. MCDSVDD uses a neural network to map the data into hyperspheres, where each hypersphere represents a specific inlier category. The distance of each sample from the centers of these hyperspheres determines the anomaly score. We evaluate the effectiveness of MCDSVDD by comparing its performance with several anomaly detection algorithms on a large dataset of astronomical light-curves obtained from the Zwicky Transient Facility. Our results demonstrate the efficacy of MCDSVDD in detecting anomalous sources while leveraging the presence of different inlier categories. The code and the data needed to reproduce our results are publicly available at https://github.com/mperezcarrasco/AnomalyALeRCE.