A Machine-learning Approach to Assessing the Presence of Substructure in Quasar-host Galaxies Using the Hyper Suprime-cam Subaru Strategic Program

The conditions under which galactic nuclear regions become active are largely unknown, although it has been hypothesized that secular processes related to galaxy morphology could play a significant role. We investigate this question using optical i -band images of 3096 SDSS quasars and galaxies at 0...
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Autor*in:	Chris Nagele [verfasserIn] John D. Silverman [verfasserIn] Tilman Hartwig [verfasserIn] Junyao Li [verfasserIn] Connor Bottrell [verfasserIn] Xuheng Ding [verfasserIn] Yoshiki Toba [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	AGN host galaxies Convolutional neural networks

Übergeordnetes Werk:	In: The Astrophysical Journal - IOP Publishing, 2022, 947(2023), 1, p 30
Übergeordnetes Werk:	volume:947 ; year:2023 ; number:1, p 30

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3847/1538-4357/acbd4a

Katalog-ID:	DOAJ089150694

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source	In The Astrophysical Journal 947(2023), 1, p 30 volume:947 year:2023 number:1, p 30
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callnumber-first	Q - Science
author	Chris Nagele
spellingShingle	Chris Nagele misc QB460-466 misc AGN host galaxies misc Convolutional neural networks misc Astrophysics A Machine-learning Approach to Assessing the Presence of Substructure in Quasar-host Galaxies Using the Hyper Suprime-cam Subaru Strategic Program
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topic_title	QB460-466 A Machine-learning Approach to Assessing the Presence of Substructure in Quasar-host Galaxies Using the Hyper Suprime-cam Subaru Strategic Program AGN host galaxies Convolutional neural networks
topic	misc QB460-466 misc AGN host galaxies misc Convolutional neural networks misc Astrophysics
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title	A Machine-learning Approach to Assessing the Presence of Substructure in Quasar-host Galaxies Using the Hyper Suprime-cam Subaru Strategic Program
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title_full	A Machine-learning Approach to Assessing the Presence of Substructure in Quasar-host Galaxies Using the Hyper Suprime-cam Subaru Strategic Program
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title_sort	machine-learning approach to assessing the presence of substructure in quasar-host galaxies using the hyper suprime-cam subaru strategic program
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title_auth	A Machine-learning Approach to Assessing the Presence of Substructure in Quasar-host Galaxies Using the Hyper Suprime-cam Subaru Strategic Program
abstract	The conditions under which galactic nuclear regions become active are largely unknown, although it has been hypothesized that secular processes related to galaxy morphology could play a significant role. We investigate this question using optical i -band images of 3096 SDSS quasars and galaxies at 0.3 < z < 0.6 from the Hyper Suprime-Cam Subaru Strategic Program, which possesses a unique combination of area, depth, and resolution, allowing the use of residual images, after removal of the quasar and smooth galaxy model, to investigate internal structural features. We employ a variational auto-encoder, which is a generative model that acts as a form of dimensionality reduction. We analyze the lower-dimensional latent space in search of features that correlate with nuclear activity. We find that the latent space does separate images based on the presence of nuclear activity, which appears to be associated with more pronounced components (i.e., arcs, rings, and bars) as compared to a matched control sample of inactive galaxies. These results suggest the importance of secular processes and possibly mergers (by their remnant features) in activating or sustaining black hole growth. Our study highlights the breadth of information available in ground-based imaging taken under optimal seeing conditions and having an accurate characterization of the point-spread function (PSF), thus demonstrating future science to come from the Rubin Observatory.
abstractGer	The conditions under which galactic nuclear regions become active are largely unknown, although it has been hypothesized that secular processes related to galaxy morphology could play a significant role. We investigate this question using optical i -band images of 3096 SDSS quasars and galaxies at 0.3 < z < 0.6 from the Hyper Suprime-Cam Subaru Strategic Program, which possesses a unique combination of area, depth, and resolution, allowing the use of residual images, after removal of the quasar and smooth galaxy model, to investigate internal structural features. We employ a variational auto-encoder, which is a generative model that acts as a form of dimensionality reduction. We analyze the lower-dimensional latent space in search of features that correlate with nuclear activity. We find that the latent space does separate images based on the presence of nuclear activity, which appears to be associated with more pronounced components (i.e., arcs, rings, and bars) as compared to a matched control sample of inactive galaxies. These results suggest the importance of secular processes and possibly mergers (by their remnant features) in activating or sustaining black hole growth. Our study highlights the breadth of information available in ground-based imaging taken under optimal seeing conditions and having an accurate characterization of the point-spread function (PSF), thus demonstrating future science to come from the Rubin Observatory.
abstract_unstemmed	The conditions under which galactic nuclear regions become active are largely unknown, although it has been hypothesized that secular processes related to galaxy morphology could play a significant role. We investigate this question using optical i -band images of 3096 SDSS quasars and galaxies at 0.3 < z < 0.6 from the Hyper Suprime-Cam Subaru Strategic Program, which possesses a unique combination of area, depth, and resolution, allowing the use of residual images, after removal of the quasar and smooth galaxy model, to investigate internal structural features. We employ a variational auto-encoder, which is a generative model that acts as a form of dimensionality reduction. We analyze the lower-dimensional latent space in search of features that correlate with nuclear activity. We find that the latent space does separate images based on the presence of nuclear activity, which appears to be associated with more pronounced components (i.e., arcs, rings, and bars) as compared to a matched control sample of inactive galaxies. These results suggest the importance of secular processes and possibly mergers (by their remnant features) in activating or sustaining black hole growth. Our study highlights the breadth of information available in ground-based imaging taken under optimal seeing conditions and having an accurate characterization of the point-spread function (PSF), thus demonstrating future science to come from the Rubin Observatory.
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title_short	A Machine-learning Approach to Assessing the Presence of Substructure in Quasar-host Galaxies Using the Hyper Suprime-cam Subaru Strategic Program
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