Accretion disks, quasars and cosmology: meandering towards understanding

Cao, Shui; Jaiswal, Vikram Kumar; Karas, Vladimír; Khadka, Narayan; Martínez-Aldama, Mary Loli; Naddaf, Mohammad-Hassan; Panda, Swayamtrupta; Núñez, F. Pozo; Prince, Raj; Ratra, Bharat; Śniegowska, Marzena; Zhang, Yu

doi:10.48550/arxiv.2209.06563

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Emission from AGNs is produced by an accretion disk and ionized clouds surrounding a central supermassive black hole (Salpeter 1964;Zel'dovich & Novikov 1964;Antonucci 1993;Netzer 2015). AGNs emit across the whole electromagnetic spectrum (Padovani et al 2017) and are readily observed at large distances due to their high luminosity, with potential to be used as probes of cosmology (Panda et al 2019;Risaliti & Lusso 2019;Czerny et al 2022). AGNs have profound effects on the life and evolution of their entire host galaxy (Ferrarese & Merritt 2000;Gebhardt et al 2000;Kormendy & Ho 2013).…”

Section: Introductionmentioning

confidence: 99%

The LSST AGN Data Challenge: Selection Methods

Savić

Jankov

et al. 2023

ApJ

View full text Add to dashboard Cite

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.

show abstract