Issue |
A&A
Volume 694, February 2025
|
|
---|---|---|
Article Number | A127 | |
Number of page(s) | 25 | |
Section | Extragalactic astronomy | |
DOI | https://doi.org/10.1051/0004-6361/202451870 | |
Published online | 11 February 2025 |
The success of optical variability in uncovering active galactic nuclei in low stellar mass galaxies
1
Instituto de Física y Astronomía, Universidad de Valparaíso, Gran Bretaña, 1111 Valparaíso, Chile
2
Millennium Nucleus on Transversal Research and Technology to Explore Supermassive Black Holes (TITANS), Chile
3
European Southern Observatory, Karl-Schwarzschild-Str. 2, 85748 Garching, Germany
4
Instituto de Astrofísica and Centro de Astroingeniería, Facultad de Física, Pontificia Universidad Católica de Chile, Campus San Joaquín, Av. Vicuña Mackenna 4860, Macul, 7820436 Santiago, Chile
5
Millennium Institute of Astrophysics (MAS), Nuncio Monseñor Sótero Sanz 100, Providencia, Santiago, Chile
6
Space Science Institute, 4750 Walnut Street, Suite 205, Boulder, CO 80301, USA
7
Departamento de Astronomía, Universidad de Chile, Camino el Observatorio, 1515 Santiago, Chile
⋆ Corresponding author; santiago.bernal@postgrado.uv.cl
Received:
12
August
2024
Accepted:
10
December
2024
Context. The origins of supermassive black holes (SMBHs) at the centers of massive galaxies are a topic of intense investigation. One way to address this subject is to identify the seeds of SMBHs as intermediate-mass black holes (IMBHs; 100 M⊙ < MBH < 106 M⊙). IMBHs are expected to be found at the centers of low stellar mass galaxies (LSMGs).
Aims. Our goal is to complete the census of SMBHs in LSMGs. In this work our aim is to establish the purity of active galactic nucleus (AGN) selection by algorithms based on optical variability and to characterize the black hole population found through this method.
Methods. We used random forest algorithms to classify all objects in a large portion of the sky, using optical light curves obtained from, or built from images provided by, the Zwicky Transient Facility (ZTF). We compared different selection sets based on alerts (flux changes with at least 5σ significance) or complete light curves derived from different photometric selection algorithms. The AGN candidates thus selected were cross-matched with objects in the NASA-Sloan Atlas (NSA) of local galaxies, with M* < 2 × 1010 M⊙. The AGN nature of these candidates was verified and characterized using archival optical spectra from SDSS. We further established the fraction of candidates with counterparts in the eROSITA Data Release 1 catalog of X-ray sources.
Results. From an initial sample of 506 candidates, 415 have good-quality spectra. Among these 415 objects we found significant broad Balmer lines in the spectra for 86% (357) of the candidates. When considering BPT classifications, five additional candidates were confirmed, resulting in 87% (362) confirmed candidates. Specifically, broad Balmer lines were detected in 94%–98% of the AGN candidates selected from complete light curves and in 80% of those selected from the less frequent ZTF alerts. The black hole masses estimated from the spectra range from 2.2 × 106 M⊙ to 4.2 × 107 M⊙, reaching lower values for the candidates selected using the more sensitive light curves. The black hole masses obtained cluster around 0.1% of the stellar mass of the host from the NSA catalog. Two-thirds of the AGN candidates are classified as Seyfert or composite by their narrow emission line ratios (BPT diagnostics), while the rest are star-forming. Almost all the candidates classified as Seyfert and over 50% of those classified as star-forming have significant broad emission lines (BELs). We found X-ray counterparts for 67% of the candidates that fall in the footprint of the eROSITA-DE DR1. Considering only the candidates with significant BELs, the matches increase to 75%, regardless of where they appear in the BPT diagnostics diagrams.
Key words: galaxies: active / quasars: general / galaxies: Seyfert
© The Authors 2025
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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