何がブラックホールを成長させ、新しい星を形成するのか?機械学習が謎を解く(What makes black holes grow and new stars form? Machine learning helps solve the mystery) | テック・アイ技術情報研究所

何がブラックホールを成長させ、新しい星を形成するのか?機械学習が謎を解く(What makes black holes grow and new stars form? Machine learning helps solve the mystery)

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2024-03-05 バース大学

超大質量ブラックホールは、銀河の進化において重要な役割を果たすが、その成長は従来、二つの銀河の激しい衝突と合併によるものと考えられていた。しかし、バース大学の新研究では、銀河の合併だけではブラックホールを燃料にするのに十分でないことが示唆された。この研究では、機械学習を用いて銀河の合併を分類し、その関係を調査し、ブラックホールの成長についての新しい理解を提供した。

<関連情報>

タイプ2セイファート銀河における合併後の星形成強化:ディープラーニングの見解 A post-merger enhancement only in star-forming Type 2 Seyfert galaxies: the deep learning view

M S Avirett-Mackenzie, C Villforth, M Huertas-Company, S Wuyts, D M Alexander, S Bonoli, A Lapi, I E Lopez, C Ramos Almeida, F Shankar
Monthly Notices of the Royal Astronomical Society  Published:22 February 2024
DOI:https://doi.org/10.1093/mnras/stae183

何がブラックホールを成長させ、新しい星を形成するのか?機械学習が謎を解く(What makes black holes grow and new stars form? Machine learning helps solve the mystery)

ABSTRACT

Supermassive black holes require a reservoir of cold gas at the centre of their host galaxy in order to accrete and shine as active galactic nuclei (AGN). Major mergers have the ability to drive gas rapidly inwards, but observations trying to link mergers with AGN have found mixed results due to the difficulty of consistently identifying galaxy mergers in surveys. This study applies deep learning to this problem, using convolutional neural networks trained to identify simulated post-merger galaxies from survey-realistic imaging. This provides a fast and repeatable alternative to human visual inspection. Using this tool, we examine a sample of ∼8500 Seyfert 2 galaxies (⁠L[OIII]∼1038.5−42 erg s−1) at z < 0.3 in the Sloan Digital Sky Survey and find a merger fraction of 2.19+0.21 −0.17per cent compared with inactive control galaxies, in which we find a merger fraction of 2.96+0.26 −0.20per cent, indicating an overall lack of mergers among AGN hosts compared with controls. However, matching the controls to the AGN hosts in stellar mass and star formation rate reveals that AGN hosts in the star-forming blue cloud exhibit a ∼2 × merger enhancement over controls, while those in the quiescent red sequence have significantly lower relative merger fractions, leading to the observed overall deficit due to the differing M*–SFR distributions. We conclude that while mergers are not the dominant trigger of all low-luminosity, obscured AGN activity in the nearby Universe, they are more important to AGN fuelling in galaxies with higher cold gas mass fractions as traced through star formation.

1701物理及び化学
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