The LSST AGN Data Challenge: Selection Methods

Journal Article · Fri Aug 11 00:00:00 EDT 2023 · The Astrophysical Journal

DOI:https://doi.org/10.3847/1538-4357/ace31a· OSTI ID:1995039

Savić, Đorđe V. ; Jankov, Isidora ; Yu, Weixiang ; Petrecca, Vincenzo ; Temple, Matthew J. ; Ni, Qingling ; Shirley, Raphael ; Kovačević, Andjelka B. ; Nikolić, Mladen ; Ilić, Dragana ; Popović, Luka Č. ; Paolillo, Maurizio ; Panda, Swayamtrupta ; Ćiprijanović, Aleksandra ; Richards, Gordon T.

Abstract Development of the Rubin Observatory Legacy Survey of Space and Time (LSST) includes a series of Data Challenges (DCs) arranged by various LSST Scientific Collaborations that are taking place during the project's preoperational phase. The AGN Science Collaboration Data Challenge (AGNSC-DC) is a partial prototype of the expected LSST data on active galactic nuclei (AGNs), aimed at validating machine learning approaches for AGN selection and characterization in large surveys like LSST. The AGNSC-DC took place in 2021, focusing on accuracy, robustness, and scalability. The training and the blinded data sets were constructed to mimic the future LSST release catalogs using the data from the Sloan Digital Sky Survey Stripe 82 region and the XMM-Newton Large Scale Structure Survey region. Data features were divided into astrometry, photometry, color, morphology, redshift, and class label with the addition of variability features and images. We present the results of four submitted solutions to DCs using both classical and machine learning methods. We systematically test the performance of supervised models (support vector machine, random forest, extreme gradient boosting, artificial neural network, convolutional neural network) and unsupervised ones (deep embedding clustering) when applied to the problem of classifying/clustering sources as stars, galaxies, or AGNs. We obtained classification accuracy of 97.5% for supervised models and clustering accuracy of 96.0% for unsupervised ones and 95.0% with a classic approach for a blinded data set. We find that variability features significantly improve the accuracy of the trained models, and correlation analysis among different bands enables a fast and inexpensive first-order selection of quasar candidates.

• Research Organization: Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
• Sponsoring Organization: USDOE Office of Science (SC), High Energy Physics (HEP)
• Grant/Contract Number: AC02-07CH11359
• OSTI ID: 1995039
• Alternate ID(s): OSTI ID: 1989912
• Report Number(s): FERMILAB-PUB-22-735-SCD; arXiv:2307.04072
• Journal Information: The Astrophysical Journal, Journal Name: The Astrophysical Journal Vol. 953 Journal Issue: 2; ISSN 0004-637X
• Publisher: American Astronomical Society
• Country of Publication: United States
• Language: English

Related Subjects

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY
79 ASTRONOMY AND ASTROPHYSICS
active galactic nuclei
quasars
neural networks