arXiv subjects

L. Raul Abramo

Publications and source records attributed to L. Raul Abramo.

At least 19 recordsLinked to original sources

arXiv2026

Where the Forest Goes Dark: Halo-centered Characterization of High Column Density Systems with IllustrisTNG

High column density systems of neutral hydrogen (HCDs) --- Lyman limit systems (LLSs), sub-damped and damped Ly$α$ absorbers (DLAs) --- contaminate both the 3D and the 1D statistics of the Ly$α$ forest. Most DLAs are masked out from the analyses, but cleaning algorithms are not perfect, and weaker LLSs are individually undetectable, so the residual contamination is absorbed into nuisance parameters that dilute the cosmological constraining power. Simulations provide the ideal setting to study this effect, allowing absorption features to be directly traced back to the gas producing them. We identify high-column-density structures on the gas cells of the TNG50 hydrodynamical simulation, deblend them in velocity space, and split every sightline into HCD-only and forest-only spectra. Applied to halos at $z \simeq 3$ out to 50 virial radii, it yields each class's covering fraction against impact parameter $b$ and halo mass. The impact parameter defines the type of absorption appearing in the spectrum: DLAs give way to sub-DLAs at $b \approx 0.11\,R_{200c}$, sub-DLAs to LLSs at $0.3$, and LLSs to the Ly$α$-forest at $0.5$, which covers $\simeq 78\%$ of sightlines at $R_{200c}$. In units of $R_{200c}$ the sequence is nearly mass-independent over three decades in mass, so the virial radius sets the scale of the neutral gas distribution. A single Voigt component recovers the column density of essentially every damped system, but not of LLSs, whose absorption features often arise from several separate contributions from gas structures along the sightline. Our detailed characterization of HCDs gives the first step to the construction of advanced techniques to directly forward-model their contribution to Ly$α$ spectra.

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arXiv2026

OJALÁ: Optimizing J-PAS Astronomy for Large-scale Analysis. A foundation model for the SED of galaxies, QSOs and stars

The advent of large-scale surveys requires efficient ML techniques to exploit the information of massive datasets. We present OJALA, a transformer-based autoregressive foundation model designed to simultaneously classify astronomical objects and infer their physical parameters using 54 narrow bands from J-PAS, combined with broad bands from the DESI Legacy Imaging Surveys and WISE. The model is trained on $\sim20$ million synthetic SEDs generated from DESI DR1 spectra. We validate OJALA using a cross-matched sample of $\sim121,000$ objects between J-PAS and DESI. The model achieves a weighted F1-score of approximately 0.9 for spectral classification (stars, galaxies, and QSOs) at $i < 21$. For galaxies, we recover photo-z with a precision of $σ_{\rm NMAD} < 0.01$, while for QSOs, the precision improves significantly at $z > 1.5$, reaching $σ_{\rm NMAD} \approx 0.006$ at $z \approx 3.5$. We demonstrate robust estimation of physical properties for galaxies, recovering stellar masses and SFR with a scatter of approximately 0.11 dex and 0.22 dex, respectively. Furthermore, the model accurately predicts EWs for major optical emission lines, allowing for the derivation of extinction-corrected H$α$ luminosities with a scatter of 0.29 dex. OJALA successfully reproduces the BPT and WHAN diagnostic diagrams, classifying SF, AGN, and passive galaxies with F1-scores typically ranging from 70% to 90% depending on the diagnostic class. For stars, the model reliably infers effective temperature and metallicity, though surface gravity remains challenging. Finally, we show the modularity of the architecture by fine-tuning the pre-trained embeddings to predict BH masses, a property not included in the primary training, recovering spectroscopic virial estimates with a precision of approximately 0.5 dex. We release the code, model weights, and a comprehensive VAC for the J-PAS EDR.

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arXiv2026

J-PAS: Semi-Supervised Sim-to-Obs Transfer for Robust Star--Galaxy--Quasar Classification

Modern studies in astrophysics and cosmology increasingly rely on simulations and cross-survey analyses, yet differences in data generation, instrumentation, calibration, and unmodeled physics introduce distribution mismatches between datasets (``domain shift''). In machine-learning pipelines, this occurs when the joint distribution of inputs and labels differs between the training (source) and application (target) domains, causing source-trained models to underperform on the target. Transfer learning and domain adaptation provide principled ways to mitigate this effect. We study a concrete simulation-to-observation case: semi-supervised domain adaptation (SSDA) to transfer a four-class spectral classifier -- high-redshift quasars, low-redshift quasars, galaxies, and stars -- from J-PAS mock catalogs based on DESI spectra to real J-PAS observations. Our pipeline pretrains on abundant labeled DESI$\rightarrow$J-PAS mocks and adapts to the target domain using a small labeled J-PAS subset. We benchmark SSDA against two baselines: a J-PAS--only supervised model trained with the same target-label budget, and a mocks-only model evaluated on held-out J-PAS data. On this held-out J-PAS data, SSDA achieves a macro-F1 score (balancing precision and recall) of $0.82$ and an overall true positive rate of $0.89$, compared to $0.79/0.85$ for the J-PAS--only baseline and $0.73/0.87$ for the mocks-only model. The gains are driven primarily by improved quasar classification, especially in the high-redshift subclass ($\mathrm{F1}=0.66$ vs.\ $0.55/0.37$), yielding better-calibrated candidate lists for spectroscopic targeting (e.g., WEAVE-QSO) and AGN searches. This study shows how modest target supervision enables robust, data-efficient simulation-to-observation transfer when simulations are plentiful but target labels are scarce.

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arXiv2025

The miniJPAS survey quasar selection V: combined algorithm

Aims. Quasar catalogues from narrow-band photometric data are used in a variety of applications, including targeting for spectroscopic follow-up, measurements of supermassive black hole masses, or Baryon Acoustic Oscillations. Here, we present the final quasar catalogue, including redshift estimates, from the miniJPAS Data Release constructed using several flavours of machine-learning algorithms. Methods. In this work, we use a machine learning algorithm to classify quasars, optimally combining the output of 8 individual algorithms. We assess the relative importance of the different classifiers. We include results from 3 different redshift estimators to also provide improved photometric redshifts. We compare our final catalogue against both simulated data and real spectroscopic data. Our main comparison metric is the $f_1$ score, which balances the catalogue purity and completeness. Results. We evaluate the performance of the combined algorithm using synthetic data. In this scenario, the combined algorithm outperforms the rest of the codes, reaching $f_1=0.88$ and $f_1=0.79$ for high- and low-z quasars (with $z\geq2.1$ and $z<2.1$, respectively) down to magnitude $r=23.5$. We further evaluate its performance against real spectroscopic data, finding different performances. We conclude that our simulated data is not realistic enough and that a new version of the mocks would improve the performance. Our redshift estimates on mocks suggest a typical uncertainty of $σ_{\rm NMAD} =0.11$, which, according to our results with real data, could be significantly smaller (as low as $σ_{\rm NMAD}=0.02$). We note that the data sample is still not large enough for a full statistical consideration.

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arXiv2025

A robust cosmic standard ruler from the cross-correlations of galaxies and dark sirens

Observations of gravitational waves (GWs) from dark sirens allow us to infer their locations and distances. Galaxies, on the other hand, have precise angular positions but no direct measurement of their distances -- only redshifts. The cross-correlation of GWs, which we limit here to binary black hole mergers (BBH), in spherical shells of luminosity distance $D_L$, with galaxies in shells of redshift $z$, leads to a direct measurement of the Hubble diagram $D_L(z)$. Since this standard ruler relies only on the statistical proximity of the dark sirens and galaxies (a general property of large-scale structures), it is essentially model-independent: the correlation is maximal when both redshift and $D_L$ shells coincide. We forecast the constraining power of this technique, which we call {\it{Peak Sirens}}, for run 5 (O5) of LIGO-Virgo-KAGRA (LVK), as well as for the third-generation observatories Einstein Telescope and Cosmic Explorer. We employ thousands of full-sky light cone simulations with realistic numbers for the tracers, and include masking by the Milky Way, lensing and inhomogeneous GW sky coverage. We find that the method is not expected to suffer from some of the issues present in other dark siren methods, such as biased constraints due to incompleteness of galaxy catalogs or dependence on priors for the merger rates of BBH. We show that with Peak Sirens, given the projected O5 sensitivity, LVK can measure $H_0$ with $7\%$ precision by itself, assuming $Λ$CDM, and $4\%$ precision using external datasets to constrain $Ω_m$. We also show that future third-generation GW detectors can achieve, without external data, sub-percent uncertainties in $H_0$ assuming $Λ$CDM, and 3\% in a more flexible $w_0w_a$CDM model. The method also shows remarkable robustness against systematic effects such as the modeling of non-linear structure formation.

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arXiv2025

Field-level simulation-based inference with galaxy catalogs: the impact of systematic effects

It has been recently shown that a powerful way to constrain cosmological parameters from galaxy redshift surveys is to train graph neural networks to perform field-level likelihood-free inference without imposing cuts on scale. In particular, de Santi et al. (2023) developed models that could accurately infer the value of $Ω_{\rm m}$ from catalogs that only contain the positions and radial velocities of galaxies that are robust to uncertainties in astrophysics and subgrid models. However, observations are affected by many effects, including 1) masking, 2) uncertainties in peculiar velocities and radial distances, and 3) different galaxy selections. Moreover, observations only allow us to measure redshift, intertwining galaxies' radial positions and velocities. In this paper we train and test our models on galaxy catalogs, created from thousands of state-of-the-art hydrodynamic simulations run with different codes from the CAMELS project, that incorporate these observational effects. We find that, although the presence of these effects degrades the precision and accuracy of the models, and increases the fraction of catalogs where the model breaks down, the fraction of galaxy catalogs where the model performs well is over 90 %, demonstrating the potential of these models to constrain cosmological parameters even when applied to real data.

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arXiv2024

Exploring the halo-galaxy connection with probabilistic approaches

The connection between galaxies and dark matter halos encompasses a range of processes and play a pivotal role in our understanding of galaxy formation and evolution. Traditionally, this link has been established through physical or empirical models. Machine learning techniques are adaptable tools that handle high-dimensional data and grasp associations between numerous attributes. In particular, probabilistic models capture the stochasticity inherent to these complex relations. We compare different probabilistic machine learning methods to model the uncertainty in the halo-galaxy connection and efficiently generate galaxy catalogs that faithfully resemble the reference sample by predicting joint distributions of central galaxy properties conditioned to their host halo features. The analysis is based on the IllustrisTNG300 simulation. The methods model the distributions in different ways. We compare a multilayer perceptron that predicts the parameters of a multivariate Gaussian distribution, a multilayer perceptron classifier, and the method of normalizing flows. The classifier predicts the parameters of a Categorical distribution, which are defined in a high-dimensional parameter space through a Voronoi cell-based hierarchical scheme. We evaluate the model's performances under various sample selections based on halo properties. The three methods exhibit comparable results, with normalizing flows showing the best performance in most scenarios. The models reproduce the main features of galaxy properties distributions with high-fidelity and reproduce the results obtained with traditional, deterministic, estimators. Our results also indicate that different halos and galaxy populations are subject to varying degrees of stochasticity, which has relevant implications for studies of large-scale structure.

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arXiv2023

The miniJPAS survey quasar selection IV: Classification and redshift estimation with SQUEzE

We present a list of quasar candidates including photometric redshift estimates from the miniJPAS Data Release constructed using SQUEzE. This work is based on machine-learning classification of photometric data of quasar candidates using SQUEzE. It has the advantage that its classification procedure can be explained to some extent, making it less of a `black box' when compared with other classifiers. Another key advantage is that using user-defined metrics means the user has more control over the classification. While SQUEzE was designed for spectroscopic data, here we adapt it for multi-band photometric data, i.e. we treat multiple narrow-band filters as very low-resolution spectra. We train our models using specialized mocks from Queiroz et al. (2022). We estimate our redshift precision using the normalized median absolute deviation, $σ_{\rm NMAD}$ applied to our test sample. Our test sample returns an $f_1$ score (effectively the purity and completeness) of 0.49 for quasars down to magnitude $r=24.3$ with $z\geq2.1$ and 0.24 for quasars with $z<2.1$. For high-z quasars, this goes up to 0.9 for $r<21.0$. We present two catalogues of quasar candidates including redshift estimates: 301 from point-like sources and 1049 when also including extended sources. We discuss the impact of including extended sources in our predictions (they are not included in the mocks), as well as the impact of changing the noise model of the mocks. We also give an explanation of SQUEzE reasoning. Our estimates for the redshift precision using the test sample indicate a $σ_{NMAD}=0.92\%$ for the entire sample, reduced to 0.81\% for $r<22.5$ and 0.74\% for $r<21.3$. Spectroscopic follow-up of the candidates is required in order to confirm the validity of our findings.

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arXiv2023

Robust Field-level Likelihood-free Inference with Galaxies

We train graph neural networks to perform field-level likelihood-free inference using galaxy catalogs from state-of-the-art hydrodynamic simulations of the CAMELS project. Our models are rotational, translational, and permutation invariant and do not impose any cut on scale. From galaxy catalogs that only contain $3$D positions and radial velocities of $\sim 1, 000$ galaxies in tiny $(25~h^{-1}{\rm Mpc})^3$ volumes our models can infer the value of $Ω_{\rm m}$ with approximately $12$ % precision. More importantly, by testing the models on galaxy catalogs from thousands of hydrodynamic simulations, each having a different efficiency of supernova and AGN feedback, run with five different codes and subgrid models - IllustrisTNG, SIMBA, Astrid, Magneticum, SWIFT-EAGLE -, we find that our models are robust to changes in astrophysics, subgrid physics, and subhalo/galaxy finder. Furthermore, we test our models on $1,024$ simulations that cover a vast region in parameter space - variations in $5$ cosmological and $23$ astrophysical parameters - finding that the model extrapolates really well. Our results indicate that the key to building a robust model is the use of both galaxy positions and velocities, suggesting that the network have likely learned an underlying physical relation that does not depend on galaxy formation and is valid on scales larger than $\sim10~h^{-1}{\rm kpc}$.

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arXiv2023

The miniJPAS survey quasar selection III: Classification with artificial neural networks and hybridisation

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 $\sim$ 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 ($z \geq 2.1$). 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 $17 < r \leq 20$, $20 < r \leq 22.5$, and $22.5 < r \leq 23.6$, 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 $20 < r \leq 22.5$. Finally, we estimate the number of point-like sources that are quasars, galaxies, and stars in miniJPAS.

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arXiv2023

The miniJPAS survey quasar selection II: Machine learning classification with photometric measurements and uncertainties

Astrophysical surveys rely heavily on the classification of sources as stars, galaxies or quasars from multi-band photometry. Surveys in narrow-band filters allow for greater discriminatory power, but the variety of different types and redshifts of the objects present a challenge to standard template-based methods. In this work, which is part of larger effort that aims at building a catalogue of quasars from the miniJPAS survey, we present a Machine Learning-based method that employs Convolutional Neural Networks (CNNs) to classify point-like sources including the information in the measurement errors. We validate our methods using data from the miniJPAS survey, a proof-of-concept project of the J-PAS collaboration covering $\sim$ 1 deg$^2$ of the northern sky using the 56 narrow-band filters of the J-PAS survey. Due to the scarcity of real data, we trained our algorithms using mocks that were purpose-built to reproduce the distributions of different types of objects that we expect to find in the miniJPAS survey, as well as the properties of the real observations in terms of signal and noise. We compare the performance of the CNNs with other well-established Machine Learning classification methods based on decision trees, finding that the CNNs improve the classification when the measurement errors are provided as inputs. The predicted distribution of objects in miniJPAS is consistent with the putative luminosity functions of stars, quasars and unresolved galaxies. Our results are a proof-of-concept for the idea that the J-PAS survey will be able to detect unprecedented numbers of quasars with high confidence.

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arXiv2023

High-fidelity reproduction of central galaxy joint distributions with Neural Networks

The relationship between galaxies and haloes is central to the description of galaxy formation, and a fundamental step towards extracting precise cosmological information from galaxy maps. However, this connection involves several complex processes that are interconnected. Machine Learning methods are flexible tools that can learn complex correlations between a large number of features, but are traditionally designed as deterministic estimators. In this work, we use the IllustrisTNG300-1 simulation and apply neural networks in a binning classification scheme to predict probability distributions of central galaxy properties, namely stellar mass, colour, specific star formation rate, and radius, using as input features the halo mass, concentration, spin, age, and the overdensity on a scale of 3 $h^{-1}$ Mpc. The model captures the intrinsic scatter in the relation between halo and galaxy properties, and can thus be used to quantify the uncertainties related to the stochasticity of the galaxy properties with respect to the halo properties. In particular, with our proposed method, one can define and accurately reproduce the properties of the different galaxy populations in great detail. We demonstrate the power of this tool by directly comparing traditional single-point estimators and the predicted joint probability distributions, and also by computing the power spectrum of a large number of tracers defined on the basis of the predicted colour-stellar mass diagram. We show that the neural networks reproduce clustering statistics of the individual galaxy populations with excellent precision and accuracy.

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arXiv2022

The miniJPAS Survey: Detection of double-core Lyα morphology of two high-redshift (z>3) QSOs

The Ly$α$ emission is an important tracer of neutral gas in a circum-galactic medium (CGM) around high-z QSOs. The origin of Lya emission around QSOs is still under debate which has significant implications for galaxy formation and evolution. In this paper, we study Ly$α$ nebulae around two high redshift QSOs, SDSS J141935.58+525710.7 at $z=3.218$ (hereafter QSO1) and SDSS J141813.40+525240.4 at $z=3.287$ (hereafter QSO2), from the miniJPAS survey within the AEGIS field. Using the contiguous narrow-band (NB) images from the miniJPAS survey and SDSS spectra, we analyzed their morphology, nature, and origin. We report the serendipitous detection of double-core Ly\al\ morphology around two QSOs which is rarely seen among other QSOs. The separations of the two Ly\al~cores are 11.07 $\pm$ 2.26 kpcs (1.47 $\pm$ 0.3$^{\prime\prime}$) and 9.73 $\pm$ 1.55 kpcs (1.31 $\pm$ 0.21$^{\prime\prime}$) with Ly$α$~line luminosities of $\sim$ 3.35 $\times 10^{44}$ erg s $^{-1} $ and $\sim$ 6.99 $\times$ 10$^{44}$ erg s $^{-1}$ for QSO1 and QSO2, respectively. The miniJPAS NB images show evidence of extended Ly$α$ and CIV morphology for both QSOs and extended HeII morphology for QSO1. These two QSOs may be potential candidates for the new enormous Lyman alpha nebula (ELAN) found from the miniJPAS survey due to their extended morphology in the shallow depth and relatively high Ly$α$ luminosities. We suggest that galactic outflows are the major powering mechanism for the double-core Ly$α$ morphology. Considering the relatively shallow exposures of miniJPAS, the objects found here could be the tip of the iceberg of a promising number of such objects that will be uncovered in the upcoming full J-PAS survey and deep IFU observations with 8-10m telescopes will be essential for constraining the underlying physical mechanism that is responsible for the double-cored morphology.

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arXiv2022

Fisher matrix for the angular power spectrum of multi-tracer galaxy surveys

Redshift evolution and peculiar velocities break the isotropy of cosmological surveys with respect to the directions parallel and transverse to the line of sight, limiting the accuracy of the Fourier representation to small areas and redshift ranges. In contrast to the Fourier space power spectrum, the full information about the two-point function of tracers of large-scale structure is encapsulated in the redshift-dependent angular power spectrum $C_\ell^{ij} (z_i,z_j)$ for the tracer species $i$ and $j$ at the redshift slices $z_i$ and $z_j$, expressed in harmonic space. In this paper we derive semi-analytical expressions for the multi-tracer Fisher matrix of angular power spectra, in real and in redshift space, which are exact in the linear regime of structure formation. Our expressions can be used to forecast the constraining power of galaxy surveys with many tracers and a large number of redshift slices, for which the derivation of the Fisher matrix from numerically evaluated covariance matrices may not be feasible or practical.

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arXiv2022

Improving cosmological covariance matrices with machine learning

Cosmological covariance matrices are fundamental for parameter inference, since they are responsible for propagating uncertainties from the data down to the model parameters. However, when data vectors are large, in order to estimate accurate and precise matrices we need huge numbers of observations, or rather costly simulations - neither of which may be viable. In this work we propose a machine learning approach to alleviate this problem in the context of the matrices used in the study of large-scale structure. With only a small amount of data (matrices built with samples of 50-200 halo power spectra) we are able to provide significantly improved matrices, which are almost indistinguishable from the ones built from much larger samples (thousands of spectra). In order to perform this task we trained convolutional neural networks to denoise the matrices, using in the training process a data set made up entirely of spectra extracted from simple, inexpensive halo simulations (mocks). We then show that the method not only removes the noise in the matrices of the cheap simulation, but it is also able to successfully denoise the matrices of halo power spectra from N-body simulations. We compare the denoised to the other matrices using several metrics, and in all of them they score better, without any signs of spurious artifacts. With the help of the Wishart distribution we derive an analytical extrapolation for the effective sample augmentation allowed by the denoiser. Finally, we show that, by using the denoised matrices, the cosmological parameters can be recovered with nearly the same accuracy as when using matrices built with a sample of 30,000 spectra in the case of the cheap simulations, and with 15,000 spectra in the case of the N-body simulations. Of particular interest is the bias in the Hubble parameter $H_0$, which was significantly reduced after applying the denoiser.

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arXiv2022

The Effective Field Theory of Large-Scale Structure and Multi-tracer

We study the performance of the perturbative bias expansion when combined with the multi-tracer technique, and their impact on the extraction of cosmological parameters. We consider two populations of tracers of large-scale structure and perform a series of Markov chain Monte Carlo analysis for those two tracers separately. The constraints in $ω_{\rm cdm}$ and $h$ using multi-tracer are less biased and approximately $60\%$ better than those obtained for a single tracer. The multi-tracer approach also provides stronger constraints on the bias expansion parameters, breaking degeneracies between them and with their error being typically half of the single-tracer case. Finally, we studied the impacts caused in parameter extraction when including a correlation between the stochastic field of distinct tracers. We also include a study with galaxies showing that multi-tracer still lead to substantial gains in the cosmological parameters.

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arXiv2022

Mimicking the halo-galaxy connection using machine learning

Elucidating the connection between the properties of galaxies and the properties of their hosting haloes is a key element in galaxy formation. When the spatial distribution of objects is also taken under consideration, it becomes very relevant for cosmological measurements. In this paper, we use machine learning techniques to analyse these intricate relations in the IllustrisTNG300 magnetohydrodynamical simulation, predicting baryonic properties from halo properties. We employ four different algorithms: extremely randomized trees, K-nearest neighbours, light gradient boosting machine, and neural networks, along with a unique and powerful combination of the results from all four approaches. Overall, the different algorithms produce consistent results in terms of predicting galaxy properties from a set of input halo properties that include halo mass, concentration, spin, and halo overdensity. For stellar mass, the Pearson correlation coefficient is 0.98, dropping down to 0.7-0.8 for specific star formation rate (sSFR), colour, and size. In addition, we apply, for the first time in this context, an existing data augmentation method, synthetic minority over-sampling technique for regression with Gaussian noise (SMOGN), designed to alleviate the problem of imbalanced data sets, showing that it improves the overall shape of the predicted distributions and the scatter in the halo-galaxy relations. We also demonstrate that our predictions are good enough to reproduce the power spectra of multiple galaxy populations, defined in terms of stellar mass, sSFR, colour, and size with high accuracy. Our results align with previous reports suggesting that certain galaxy properties cannot be reproduced using halo features alone.

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arXiv2022

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

Precise measurements of black hole masses are essential to understanding the coevolution of these sources and their host galaxies. We develop a novel approach for computing black hole virial masses using measurements of continuum luminosities and emission line widths from partially overlapping, narrow-band observations of quasars; we refer to this technique as single-epoch photometry. This novel method relies on forward-modelling quasar observations for estimating emission line widths, which enables unbiased measurements even for lines coarsely resolved by narrow-band data. We assess the performance of this technique using quasars from the Sloan Digital Sky Survey (SDSS) observed by the miniJPAS survey, a proof-of-concept project of the Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) collaboration covering $\simeq1\,\mathrm{deg}^2$ of the northern sky using the 56 J-PAS narrow-band filters. We find remarkable agreement between black hole masses from single-epoch SDSS spectra and single-epoch miniJPAS photometry, with no systematic difference between these and a scatter ranging from 0.4 to 0.07 dex for masses from $\log(M_\mathrm{BH})\simeq8$ to 9.75, respectively. Reverberation mapping studies show that single-epoch masses present approximately 0.4 dex precision, letting us conclude that our novel technique delivers black hole masses with only mildly lower precision than single-epoch spectroscopy. The J-PAS survey will soon start observing thousands of square degrees without any source preselection other than the photometric depth in the detection band, and thus single-epoch photometry has the potential to provide details on the physical properties of quasar populations that do not satisfy the preselection criteria of previous spectroscopic surveys.

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