English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

With the increasing volume of astronomical data generated by modern surveytelescopes, automated pipelines and machine learning techniques have becomecrucial for analyzing and extracting knowledge from these datasets. Anomalydetection, i.e. the task of identifying irregular or unexpected patterns in thedata, is a complex challenge in astronomy. In this paper, we proposeMulti-Class Deep Support Vector Data Description (MCDSVDD), an extension of thestate-of-the-art anomaly detection algorithm One-Class Deep SVDD, specificallydesigned to handle different inlier categories with distinct datadistributions. MCDSVDD uses a neural network to map the data into hyperspheres,where each hypersphere represents a specific inlier category. The distance ofeach sample from the centers of these hyperspheres determines the anomalyscore. We evaluate the effectiveness of MCDSVDD by comparing its performancewith several anomaly detection algorithms on a large dataset of astronomicallight-curves obtained from the Zwicky Transient Facility. Our resultsdemonstrate the efficacy of MCDSVDD in detecting anomalous sources whileleveraging the presence of different inlier categories. The code and the dataneeded to reproduce our results are publicly available athttps://github.com/mperezcarrasco/AnomalyALeRCE.

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