Black hole virial masses from single-epoch photometry: the miniJPAS test case

Chaves-Montero, Jonás; Bonoli, Silvia; Trakhtenbrot, Benny; Fernández-Centeno, Alejandro; Queiroz, Carolina; Díaz-García, Luis A.; Delgado, Rosa María González; Hernán-Caballero, Antonio; Hernández-Monteagudo, Carlos; Lópen-Sanjuan, Carlos; Overzier, Roderik; Sobral, David; Abramo, L. Raul; Alcaniz, Jailson; Benitez, Narciso; Carneiro, Saulo; Cenarro, A. Javier; Cristóbal-Hornillos, David; Dupke, Renato A.; Ederoclite, Alessandro; Marín-Franch, Antonio; de Oliveira, Claudia Mendes; Moles, Mariano; Sodré, Laerte; Taylor, Keith; Varela, Jesús; Ramió, Héctor Vázquez; Civera, Tamara

doi:10.48550/arxiv.2111.01180

Cited by 2 publications

(1 citation statement)

References 117 publications

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“…These unique characteristics enable observation and analysis of galaxies and QSOs in continuous redshift ranges, 0 z 1 and 0.5 z 4, respectively (Bonoli et al 2021). Different studies have proved the capability of J-PAS to address several topics within the astrophysical field, for instance, the evolution of the stellar population properties of galaxies up to z ∼ 1 (González Delgado et al 2021), the properties of the nebular emission lines of galaxies down to z ≤ 0.35 (Martínez-Solaeche et al 2022), the measurement of black hole virial masses for the QSO population (Chaves-Montero et al 2021), or the study of galaxy properties within galaxy clusters (Rodríguez-Martín et al 2022) and groups (González . Unfortunately, the data available for spectroscopically confirmed sources in the miniJPAS area are not sufficient to train and test ML algorithms for our purpose.…”

Section: Introductionmentioning

confidence: 99%

J-PAS: Measuring emission lines with artificial neural networks

Martínez-Solaeche

Delgado

García-Benito

et al. 2021

A&A

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In the years to come, the Javalambre-Physics of the Accelerated Universe Astrophysical Survey (J-PAS) will observe 8000 deg2 of the northern sky with 56 photometric bands. J-PAS is ideal for the detection of nebular emission objects. This paper presents a new method based on artificial neural networks (ANNs) that is aimed at measuring and detecting emission lines in galaxies up to z = 0.35. These lines are essential diagnostics for understanding the evolution of galaxies through cosmic time. We trained and tested ANNs with synthetic J-PAS photometry from CALIFA, MaNGA, and SDSS spectra. To this aim, we carried out two tasks. First, we clustered galaxies in two groups according to the values of the equivalent width (EW) of Hα, Hβ, [N II], and [O III] lines measured in the spectra. Then we trained an ANN to assign a group to each galaxy. We were able to classify them with the uncertainties typical of the photometric redshift measurable in J-PAS. Second, we utilized another ANN to determine the values of those EWs. Subsequently, we obtained the [N II]/Hα, [O III]/Hβ, and O 3N 2 ratios, recovering the BPT diagram ([O III]/Hβ versus [N II]/Hα). We studied the performance of the ANN in two training samples: one is only composed of synthetic J-PAS photo-spectra (J-spectra) from MaNGA and CALIFA (CALMa set) and the other one is composed of SDSS galaxies. We were able to fully reproduce the main sequence of star-forming galaxies from the determination of the EWs. With the CALMa training set, we reached a precision of 0.092 and 0.078 dex for the [N II]/Hα and [O III]/Hβ ratios in the SDSS testing sample. Nevertheless, we find an underestimation of those ratios at high values in galaxies hosting an active galactic nuclei. We also show the importance of the dataset used for both training and testing the model. Such ANNs are extremely useful for overcoming the limitations previously expected concerning the detection and measurements of the emission lines in such surveys as J-PAS. Furthermore, we show the capability of the method to measure a EW of 10 Å in Hα, Hβ, [N II] and [O III] lines with a signal-to-noise ratio (S/N) of 5, 1.5, 3.5, and 10, respectively, in the photometry. Finally, we compare the properties of emission lines in galaxies observed with miniJPAS and SDSS. Despite the limitation of such a comparison, we find a remarkable correlation in their EWs.

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Section: Introductionmentioning

confidence: 99%

J-PAS: Measuring emission lines with artificial neural networks

Martínez-Solaeche

Delgado

García-Benito

et al. 2021

A&A

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The miniJPAS survey quasar selection

Solaeche¹,

Queiroz²,

Delgado³

2023

A&A

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This paper is part of large effort within the J-PAS collaboration that aims to classify point-like sources in miniJPAS, which were observed in 60 optical bands over ∼ 1 deg 2 in the AEGIS field. We developed two algorithms based on artificial neural networks (ANN) to classify objects into four categories: stars, galaxies, quasars at low redshift (z < 2.1), and quasars at high redshift (z ≥ 2.1). As inputs, we used miniJPAS fluxes for one of the classifiers (ANN 1 ) and colours for the other (ANN 2 ). The ANNs were trained and tested using mock data in the first place. We studied the effect of augmenting the training set by creating hybrid objects, which combines fluxes from stars, galaxies, and quasars. Nevertheless, the augmentation processing did not improve the score of the ANN. We also evaluated the performance of the classifiers in a small subset of the SDSS DR12Q superset observed by miniJPAS. In the mock test set, the f1-score for quasars at high redshift with the ANN 1 (ANN 2 ) are 0.99 (0.99), 0.93 (0.92), and 0.63 (0.57) for 17 < r ≤ 20, 20 < r ≤ 22.5, and 22.5 < r ≤ 23.6, respectively, where r is the J-PAS rSDSS band. In the case of low-redshift quasars, galaxies, and stars, we reached 0.97 (0.97), 0.82 (0.79), and 0.61 (0.58); 0.94 (0.94), 0.90 (0.89), and 0.81 (0.80); and 1.0 (1.0), 0.96 (0.94), and 0.70 (0.52) in the same r bins. In the SDSS DR12Q superset miniJPAS sample, the weighted f1-score reaches 0.87 (0.88) for objects that are mostly within 20 < r ≤ 22.5. We find that the most common confusion occurs between quasars at low redshift and galaxies in mocks and miniJPAS data. We discuss the origin of this confusion, and we show examples in which these objects present features that are shared by both classes. Finally, we estimate the number of point-like sources that are quasars, galaxies, and stars in miniJPAS.

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Black hole virial masses from single-epoch photometry: the miniJPAS test case

Cited by 2 publications

References 117 publications

J-PAS: Measuring emission lines with artificial neural networks

J-PAS: Measuring emission lines with artificial neural networks

The miniJPAS survey quasar selection

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