arXiv subjects

P. Sánchez-Sáez

Publications and source records attributed to P. Sánchez-Sáez.

At least 19 recordsLinked to original sources

arXiv2026

CLAS+: A large catalog of Changing-Look AGN candidates selected through S-PLUS narrow-band photometry

Changing-look active galactic nuclei (CLAGN) exhibit rapid spectral transitions on timescales of months to years, challenging standard AGN unification models and providing unique insight into accretion-disk variability and obscuration processes. Despite their scientific importance, systematic searches for CLAGN remain limited by the scarcity of multi-epoch spectroscopy and the difficulty of constructing large samples efficiently. We introduce CLAS+, a catalog constructed by comparing synthetic photometry derived from DESI DR1 and SDSS DR17 spectra with contemporaneous narrow-band imaging from the S-PLUS survey. Applying our pipeline to a parent sample of 98,139 quasars, we select high-confidence CLAGN candidates above a threshold of reduced chi-squared $χ_r^2 > 15$. We identify 3,554 strong CLAGN candidates, corresponding to 3.6\% of the parent sample. This selected fraction is conditional on the adopted criterion and should not be interpreted as an intrinsic CLAGN occurrence rate. Cross-matching with existing CLAGN compilations shows that CLAS+ identifies a large population of previously unreported high-priority candidates (3,481). Among the spectroscopic--photometric comparisons with reliable temporal classification, 40\% are classified as photometric turn-on, 41\% as intermediate, and 19\% as turn-off. We also find an apparent redshift dependence in the relative fractions, with photometric turn-on candidates becoming more common at higher redshifts within the CLAS+ selected sample. Therefore, narrow-band photometry provides a powerful and efficient alternative to purely spectroscopic searches for identifying CLAGN candidates at scale. CLAS+ substantially expands the known CLAGN candidate population and provides a valuable target list for future spectroscopic follow-up and time-domain studies with facilities such as Rubin/LSST.

astro-ph.GA↗

arXiv2026

The X-ray-Weak Tail of the Quasar Population: Physical Properties and Redshift Evolution of Variability-Selected AGN in eFEDS

(abridged) Aims: To characterize variability-selected AGN candidates within the eROSITA Final Equatorial-Depth Survey (eFEDS), leveraging the synergy between optical variability and deep X-ray observations by SRG (Spectrum Roentgen Gamma)/eROSITA, to compare the physical properties of AGN populations selected through complementary methods. Methods: We used a sample of AGN candidates selected via optical variability by Arévalo et al. and cross-match it to the X-ray selected eFEDS AGN sample. Spectroscopic characterizations are obtained for the matched samples from the Dark Energy Spectroscopic Instrument public data products. Results: We find a high classification purity with 98% candidates confirmed as broad-line AGN/QSOs. We identify a significant population (25%) that lack X-ray counterparts (X-U) in the eFEDS survey. We find that the X-D and X-U samples exhibit distinct physical properties and redshift distributions. The X-U sample peaks at $z = 2$ while the X-D sample at $z =1$, with nearly half of the variability-selected AGN at $z > 2.5$ remaining undetected in X-rays. This missing fraction can be attributed to K-correction effects and SED evolution with luminosity, mainly resulting in high accretion rate objects falling out of the X-D sample at higher redshifts. X-ray variability accounts for a part of the missing fraction but its dependence with redshift is less clear. Ensemble spectral analysis reveals that X-D sources have steeper optical continua and stronger broad emission lines than their X-U counterparts, while the forbidden and semi-forbidden lines have equal EWs in both samples. Finally, broad absorption line (BAL) features are found almost exclusively in the X-U population. This "missing" X-ray population must be accounted for when extrapolating X-ray-selected AGN to the broader Type I population and could impact cosmic estimates of massive black hole growth.

astro-ph.GA↗

arXiv2026

VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features

Photometric redshift estimation for active galactic nuclei (AGNs) remains a fundamental challenge for current and upcoming large-scale photometric surveys. Traditional spectral energy distribution (SED) fitting suffers from color-redshift degeneracies, particularly for AGNs whose power-law continua hide the strong spectral features required to anchor redshift estimates. While AGN variability provides additional constraining power, existing frameworks require multi-band light curves that are not always available. This work presents VAR-PZnn, a fully connected mixture density network that integrates 26 variability features extracted from ZTF g-band light curves with optical photometry from Pan-STARRS1, mid-infrared (MIR) photometry from CatWISE, and, for a subsample, NIR photometry from UKIDSS. The model is trained and tested on 72,728 spectroscopically confirmed AGNs/QSOs spanning 0.01 < z < 4.5 and g-band magnitudes from 17 to 21.5. For the main sample, we achieve σ_{NMAD} = 0.058 and an outlier fraction of η= 8.2%, which reduces to 5.4% when the 10% of sources with the highest predicted uncertainty are excluded. An ablation study demonstrates that MIR photometry provides the dominant constraint for photo-z accuracy, while variability features serve as a secondary refiner. Using UKIDSS NIR data as a proxy for future synergies between LSST and space-based missions like Euclid and Roman, we obtain η= 13.3% without MIR data and η= 4.6% when MIR is available. We benchmark against Low-Resolution Templates (LRT) SED fitting (η= 28.7%) and the VAR-PZ framework; applying single-band VAR-PZ priors worsens LRT performance to η= 39.4% due to single-band light-curve degeneracies, confirmed via simulations (η= 27.6% to 28.1%). This framework provides a scalable approach for the Legacy Survey of Space and Time (LSST).

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arXiv2026

Spatially resolved optical and mid-infrared spectroscopy of SDSS1335+0728: implications for the origin of the Ansky event

The galaxy SDSS1335+0728 brightened abruptly in December 2019 (the Ansky event) and has since been confirmed as the host of extreme X-ray quasi-periodic eruptions (QPEs) of debated origin. We constrain the origin of its transient activity by characterising the galaxy properties and nuclear accretion history with spatially resolved VLT/MUSE and JWST MIRI/MRS spectroscopy. We extract stellar and gas kinematics and emission-line fluxes, construct emission-line ionisation diagnostic maps, reconstruct the nuclear ionisation history via a Balmer-line light-echo analysis, and measure the mid-infrared silicate feature strength. The stellar kinematics reveal two counter-rotating stellar regions and kinematically cold gas ($σ_{\rm gas} \lesssim 60$ km s$^{-1}$), consistent with a past minor merger. Stellar populations show an old host with ongoing star formation confined to a ring at intermediate radii. Ionisation diagnostics reveal a three-zone structure: a central region powered by SMBH accretion, where high-ionisation coronal lines ([NeVI]$\lambda7.65μ$m, [NeV]$\lambda14.32μ$m, [OIV]$\lambda25.89μ$m) are confined, a star-forming ring, and a LINER-like outer region. A Balmer-line light-echo analysis yields a minimum ionising luminosity $\log L_{\rm ion,min} \approx 40.5$ erg s$^{-1}$ sustained over at least $\sim 1\,500$ yr. Broad silicate emission at 9.7 and 18$μ$m indicates optically thin dust, inconsistent with a classical active galactic nucleus (AGN) dusty torus. The data are consistent with two scenarios for the pre-2019 accretion: a persisting or gradually fading low-luminosity AGN, or a long-lived tidal disruption event (TDE) remnant disc. In both, Ansky corresponds to a slow, faint transient in a $\sim\!10^6\,M_{\odot}$ SMBH with already ongoing accretion, challenging the "faded AGN" interpretation proposed for some QPE hosts.

astro-ph.GA↗

arXiv2026

ESOFinder: an LLM-powered tool to help users navigate ESO documentation

The large amount and diversity of documentation available for users of the European Southern Observatory (ESO), spanning the full observing lifecycle from proposal preparation and observation planning to data reduction and archival access, makes it increasingly challenging for the astronomical community to efficiently find relevant information. To address this, we have developed ESOFinder, an in-house chatbot powered by Large Language Models (LLMs) and Retrieval Augmented Generation (RAG). ESOFinder integrates public information from instrument manuals, phase 1/2/3 documentation, data reduction pipeline manuals, the ESO Knowledge Base, and key web resources (spanning more than 3500 links and over 100 manuals) to provide concise, context-aware, and reference-linked answers to user queries about proposal or observation preparation, data retrieval, and data processing. Built on open-source LLMs, running on a local server, ESOFinder ensures data privacy, transparency, and complete control over the knowledge base. Its multi-step architecture allows verification of retrieved documents and generated answers, reducing the risk of hallucinations and improving the reliability of responses compared to commercial tools. The current version of ESOFinder is being tested internally at ESO to evaluate its performance, assess its integration with internal workflows, and identify limitations in coverage and accuracy. These tests will guide further improvements, including the incorporation of additional documentation, and enhanced retrieval strategies. Ultimately, ESOFinder aims to become an interface for users to navigate ESO's complex documentation ecosystem and to support both staff and community astronomers in their daily tasks.

astro-ph.IM↗

arXiv2026

Seeing through the dust: Unraveling near-infrared variability in type 2 active galactic nuclei

Near-infrared (NIR) variability studies of active galactic nuclei (AGNs) are still limited, as long-term multiepoch monitoring in the NIR is observationally challenging. The depth, wavelength coverage, and 14-year temporal baseline of UltraVISTA make it one of the few surveys capable of providing a detailed characterization of AGN variability in this regime. We aim to quantify the NIR variability of known AGNs in the COSMOS field and to investigate the physical origin of variability in type 2 AGNs. In particular, we examine how NIR variability can help clarify the discrepancies between optical and X-ray classifications. Using the 14-year multiepoch UltraVISTA DR6 dataset in the YJHKs bands, we constructed calibrated NIR light curves and quantified their variability through a set of metrics. AGN-like stochastic variability was identified by modeling the light curves with a damped random walk (DRW) process. We find that about 7-17% of the 533 type 2 AGNs are variable in the NIR, with variability fractions increasing toward Ks, where the dusty torus dominates the emission. Based on the wavelength dependence of the DRW variability amplitude, we classify variable type 2 AGNs into disk-dominated, torus-dominated, and highly obscured groups. About one third of the X-ray unobscured (XR I) type 2 AGNs are variable in the NIR, consistent with misclassified weak type 1 or true type 2 AGNs. On the other hand, 21.4% (30/140) of the X-ray obscured (XR II) type 2 AGNs show detectable variability in the NIR, most of them only in H or Ks, consistent with obscuration of the bluer (accretion disk) bands. Type 2 AGNs without X-ray counterparts (165) show the smallest fraction (3.6%) of variable objects. NIR variability provides an effective and independent diagnostic for confirming optical classifications and for identifying weak or misclassified type 1 AGNs in deep extragalactic surveys.

astro-ph.GA↗

arXiv2026

Spectral Handling and Estimation of AGN Parameters (SHEAP), The first AGN fitting GPU-based code

In the coming years, the number of discovered active galactic nuclei (AGN) is expected to increase significantly due to upcoming spectroscopic surveys. This growth will challenge current analysis and modeling techniques, requiring scalable methods for large, heterogeneous datasets with diverse signal-to-noise ratios, spectral resolutions, and host-galaxy contamination. We present SHEAP (Spectral Handling and Estimation of AGN Parameters), a spectral-fitting framework designed to analyze large AGN samples efficiently while preserving physical interpretability, reproducibility, and robust uncertainty estimation. SHEAP uses JAX, a Python GPU-powered framework, to implement a flexible model with modular components, including continuum, host galaxy, FeII pseudo-continuum, and multi-component emission lines, together with parameter tying and physically motivated constraints. By combining gradient-based optimization with automatic differentiation, vectorization, and just-in-time compilation, SHEAP achieves stable convergence in blended regions, such as H$β$, while substantially reducing runtime. We compare SHEAP measurements with literature results and public fitting pipelines across four samples covering the CIV, MgII, H$β$, and H$α$ regions. We find good agreement for the main AGN spectral parameters, with $\sim85$--$100%$ of objects lying within the $\pm0.3$ dex band and reduced chi-square distributions close to unity. Relative to the runtime reported by \citet{2026Bernal} using \texttt{pPXF}, the fitting stage requires only $\sim1.7%$ of the computational time, corresponding to an improvement of approximately $100$ times. These results show that \texttt{SHEAP} delivers reliable AGN spectral decompositions at substantially lower computational cost, making it suitable for massive spectroscopic datasets.

astro-ph.GA↗

arXiv2026

VAR-PZ: Constraining the Photometric Redshifts of Quasars using Variability

The Vera C. Rubin Observatory LSST is expected to discover tens of millions of new Active Galactic Nuclei (AGNs). The survey's exceptional cadence and sensitivity will enable UV/optical/NIR monitoring of a significant fraction of these objects. The unprecedented number of sources makes spectroscopic follow-up for the vast majority of them unfeasible in the near future, so most studies will have to rely on photometric redshifts estimates which are traditionally much less reliable for AGN than for inactive galaxies. This work presents a novel methodology to constrain the photometric redshift of AGNs that leverages the effects of cosmological time dilation, and of the luminosity and wavelength dependence of AGN variability. Specifically, we assume that the variability can be modeled as a damped random walk (DRW) process, and adopt a parametric model to characterize the DRW timescale ($τ$) and asymptotic amplitude of the variability (SF$_\infty$) based on the redshift, the rest-frame wavelength, and the AGN luminosity. We construct variability-based photo-$z$ priors by modeling the observed variability using the expected DRW parameters at a given redshift. These variability-based photometric redshift (VAR-PZ) priors are then combined with traditional SED fitting to improve the redshift estimates from SED fitting. Validation is performed using observational data from the SDSS, demonstrating significant reduction in catastrophic outliers by more than 10% in comparison with SED fitting techniques and improvements in redshift precision. The simulated light curves with both SDSS and LSST-like cadences and baselines confirm that, VAR-PZ will be able to constrain the photometric redshifts of SDSS-like AGNs by bringing the outlier fractions down to below 7% from 32% (SED-alone) at the end of the survey.

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arXiv2025

Lost and Found - A gallery of overlooked optical nuclear transients from the ZTF archive

Tidal disruption events (TDEs) correspond to the destruction of a star by the tidal forces around a black hole, leading to outbursts which can last from months to years. These transients are rare, and increasing the current sample is paramount to understand them. As part of the Fink alert broker, we have developed an early detection system for TDEs for the Zwicky Transient Facility (ZTF) data. In this paper, we report on the optical transients we found either during the development of this tool, or when applying the classifier to the existing archive. We use this sample to anticipate what improvements to the TDE detection systems will need to be implemented for future surveys. For all the transients, we present optical and infrared archival photometry from ZTF, WISE, and Catalina, and assess the previous nuclear activity of the host. We fit the ZTF lightcurves with both a phenomenological and a physically-motivated model. We report on a total of 19 optical nuclear transients, out of which nine are in passive galaxies, eight in active galaxies, and two for which the activity of the host is uncertain. Two transients are newly discovered repeated TDE candidates, and we compare them to the current sample of repeated optical nuclear transients. One transient is exceptionally long-lived (over 5 years), in an until-now passive galaxy. Three of the TDE-like flares in active galaxies have absolute g-band magnitudes brighter than -24, making them new Extreme Nuclear Transient (ENT) candidates. One seemingly repeated object was revealed to be two independent supernovae in the same galaxy. This sample shows both the potential of our detection system for future discovery, and the relevance of archival searches to reveal overlooked transients. It also raises several points of concern and avenues of improvement for current and future classifiers.

astro-ph.HE↗

arXiv2025

Unlocking AGN Variability with Custom ZTF Photometry for High-Fidelity Light Curves and Robust Selection

(Abridged)We explore the potential of optical variability selection methods to identify AGN, including those challenging to detect with conventional techniques. Using the unprecedented combination of depth, sky coverage, and cadence of the ZTF survey, we target even starlight-dominated AGN, known for their redder colours, weaker variability signals, and difficult nuclear photometry due to their resolved hosts. We perform aperture photometry on ZTF reference-subtracted images for 40 million sources across 8,000 deg^2, assemble light curves and classify objects employing an RF algorithm into 14 classes, including 341,938 candidate AGN. We compare variability metrics derived from our photometry to those obtained from ZTF Data Release light curves (DR11-psf), to assess the impact of our analysis. We find that the fraction of low-z quiescent galaxies exhibiting significant variability drops dramatically (from 98\% of the sample to 7\%) when replacing the DR11-psf light curves with our difference image, aperture photometry (DI-Ap) version. The overall number of variable low-z AGN remains high (99\% when using DR11-psf lightcurves, 83\% when using DI-Ap), however, implying that our photometry can detect the fainter variability in host dominated AGN. The classifier effectively distinguishes between AGN and other sources, demonstrating high recovery rates even for AGN in resolved nearby galaxies. AGN candidates in eROSITA's eFEDS field, detected in X-rays and bright enough for ZTF optical observations, were classified as AGN (79\%) and non-variable galaxies (20\%). These groups show a 2 dex difference in X-ray luminosity but not in X-ray flux. A significant fraction of X-ray AGN are optically too faint for ZTF, and conversely, a quarter of ZTF AGN in the eFEDS area lack X-ray detections, highlighting a wide range of X-ray-to-optical flux ratios in AGN.

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arXiv2025

NICER observations reveal doubled timescales in Ansky's quasi-periodic eruptions (QPEs)

Quasi-periodic eruptions (QPEs) are recurring X-ray bursts originating from the vicinity of supermassive black holes, but their driving mechanisms remain under debate. This study analyzes new NICER observations of QPEs in Ansky (a transient event in the nucleus of the galaxy SDSS J1335+0728), taken between January and June 2025. By examining flare durations, peak-to-peak recurrence times, and profiles, we compare the 2025 data with those from 2024 to investigate changes in energy, timescales, and flare shapes. The 2025 QPEs are found to be four times more energetic, with recurrence times of approximately 10 days and flare durations ranging from 2.5 to 4 days, making them both about twice as long as in 2024. Additionally, the flare profiles have become more asymmetric, showing longer decays. We explore different theoretical scenarios to explain the observed properties of the QPEs in Ansky, including evolving stream-disk interactions in an extreme mass-ratio inspiral (EMRI) system as a potential mechanism behind the observed changes in recurrence time and energetics, while also considering alternative models based on mass transfer and accretion disk instabilities. Continued observational efforts will be crucial for unveiling the nature of Ansky.

astro-ph.HE↗

arXiv2025

Astrophotometric search for massive stars in the Milky way. Confronting Random Forest predictions with available spectroscopy

Massive stars play a significant role in different branches of astronomy, from shaping the processes of star and planet formation to influencing the evolution and chemical enrichment of the distant universe. Despite their high astrophysical significance, these objects are rare and difficult to detect. With Gaia's advent, we now possess extensive kinematic and photometric data for a significant portion of the Galaxy that can unveil, among others, new populations of massive star candidates. In order to produce bonafide bright (G magnitude $<$ 12) massive star candidate lists (threshold set to spectral type B2 or earlier but slight changes in this threshold also explored) in the Milky Way subject to be followed up by future massive spectroscopic surveys, we have developed a Gaia DR3 plus literature data based methodology. We trained a Balanced Random Forest (BRF) with the spectral types from the compilation by Skiff et al. (2014) as labels. Our approach yields a completeness of $\sim80\%$ and a purity ranging from $0.51 \pm 0.02$ for probabilities between 0.6 and 0.7, up to $0.85 \pm 0.05$ for the 0.9-1.0 range. To externally validate our methodology, we searched for and analyzed archival spectra of moderate to high probability (p $>$ 0.6) candidates that are not contained in our catalog of labels. Our independent spectral validation confirms the expected performance of the BRF, spectroscopically classifying 300 stars as B3 or earlier (due to observational constraints imposed in the B0-3 range), including 107 new stars. Based on the most conservative yields of our methodology, our candidate list could increase the number of bright massive stars by $\sim$50\%. As a byproduct, we developed an automatic methodology for spectral typing optimized for LAMOST spectra, based on line detection and characterization guiding a decision path.

astro-ph.SR↗

arXiv2025

Image-Based Multi-Survey Classification of Light Curves with a Pre-Trained Vision Transformer

We explore the use of Swin Transformer V2, a pre-trained vision Transformer, for photometric classification in a multi-survey setting by leveraging light curves from the Zwicky Transient Facility (ZTF) and the Asteroid Terrestrial-impact Last Alert System (ATLAS). We evaluate different strategies for integrating data from these surveys and find that a multi-survey architecture which processes them jointly achieves the best performance. These results highlight the importance of modeling survey-specific characteristics and cross-survey interactions, and provide guidance for building scalable classifiers for future time-domain astronomy.

astro-ph.IM↗

arXiv2025

Tuning into spatial frequency space: Satellite and space debris detection in the ZTF alert stream

A significant challenge in the study of transient astrophysical phenomena is the identification of bogus events, among which human-made satellites and debris in Earth orbit remain major contaminants. Existing pipelines effectively identify satellite trails but can miss more complex signatures, such as collections of satellite glints. In the Rubin Observatory era, the scale of operations will increase tenfold compared to its precursor, the Zwicky Transient Facility (ZTF), requiring improvements in classification purity, data compression for informative alerts, and pipeline speed. We explore the use of the 2D Fast Fourier Transform (FFT) on difference images as a tool to enhance machine learning models for satellite detection. Using the ALeRCE single-stamp classifier as a baseline, we adapt its architecture to incorporate a cutout of the FFT of the difference image alongside the standard ZTF image triplet (science, reference, and difference stamps). We evaluate several stamp sizes and resolutions, focusing on regimes where data compression is critical due to alert size limits and real-time constraints. Incorporating the FFT significantly improves satellite classification, especially in the smallest field-of-view model (16 arcsec), where accuracy increases from 72.0\pm2.9% to 87.8\pm1.3%. This demonstrates the FFT's value in compressing and capturing extended satellite features. However, the FFT alone does not match the full-context performance of the 63 arcsec (95.9\pm1.3%) or multiscale (90.6\pm0.8%) models, highlighting the complementary role of spatial context. We show how FFTs can be leveraged to cull satellite and debris signatures from alert streams in current and future time-domain surveys.

astro-ph.IM↗

arXiv2025

ALeRCE light curve classifier: Tidal disruption event expansion pack

ALeRCE (Automatic Learning for the Rapid Classification of Events) processes the Zwicky Transient Facility (ZTF) alert stream in preparation for the Vera C. Rubin Observatory, classifying objects using a broad taxonomy. The ALeRCE light curve classifier is a balanced random forest (BRF) algorithm with a two-level scheme that uses variability features from the ZTF alert stream and colors from AllWISE and ZTF photometry. This work presents an updated version of the ALeRCE broker light curve classifier that includes tidal disruption events (TDEs) as a new subclass. We incorporated 24 new features, including the distance to the nearest source detected in ZTF science images and a parametric model of the power-law decay for transients. The labeled set was expanded to 219792 spectroscopically classified sources, including 60 TDEs. To integrate TDEs into ALeRCE's taxonomy, we identified specific characteristics that distinguish them from other transients: their central position in a galaxy, their typical decay pattern when fully disrupted, and their lack of color variability after disruption. We developed features to separate TDEs from other transient events. The updated classifier improves performance across all classes and integrates the TDE class with 91 percent recall. It also identifies a large number of potential TDE candidates in the ZTF alert stream's unlabeled data.

astro-ph.IM↗

arXiv2024

The Success of Optical Variability in Uncovering AGNs in Low-stellar Mass Galaxies

We used random forest algorithms to classify all objects in a large portion of the sky, using optical light curves obtained, or built from images provided, by the Zwicky Transient Facility (ZTF). We compare different selection sets based on alerts or complete light curves derived from different photometric selection algorithms. The AGN candidates thus selected are cross-matched with objects in the NASA-Sloan Atlas (NSA) of local galaxies, with $M_*<2\times10^{10}M_\odot$. The AGN nature of these candidates is verified and characterized using archival optical spectra from SDSS. We further establish the fraction of candidates with counterparts in the eROSITA data release 1 catalog of X-ray sources. 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, an additional 5 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 $2.2\times10^6M_\odot$ to $4.2\times10^7M_\odot$, 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 BELs. We found X-ray counterparts for $67\%$ of the candidates that fall in the footprint of the eROSITA-DE DR1.

astro-ph.GA↗

arXiv2024

AT 2021hdr: A candidate tidal disruption of a gas cloud by a binary super massive black hole system

With a growing number of facilities able to monitor the entire sky and produce light curves with a cadence of days, in recent years there has been an increased rate of detection of sources whose variability deviates from standard behavior, revealing a variety of exotic nuclear transients. The aim of the present study is to disentangle the nature of the transient AT 2021hdr, whose optical light curve used to be consistent with a classic Seyfert 1 nucleus, which was also confirmed by its optical spectrum and high-energy properties. From late 2021, AT 2021hdr started to present sudden brightening episodes in the form of oscillating peaks in the Zwicky Transient Facility (ZTF) alert stream, and the same shape is observed in X-rays and UV from Swift data. The oscillations occur every about 60-90 days with amplitudes of around 0.2 mag in the g and r bands. Very Long Baseline Array (VLBA) observations show no radio emission at milliarcseconds scale. It is argued that these findings are inconsistent with a standard tidal disruption event (TDE), a binary supermassive black hole (BSMBH), or a changing-look active galactic nucleus (AGN); neither does this object resemble previous observed AGN flares, and disk or jet instabilities are an unlikely scenario. Here, we propose that the behavior of AT 2021hdr might be due to the tidal disruption of a gas cloud by a BSMBH. In this scenario, we estimate that the putative binary has a separation of about 0.83 mpc and would merge in about 70000 years. This galaxy is located at 9 kpc from a companion galaxy, and in this work we report this merger for the first time. The oscillations are not related to the companion galaxy.

astro-ph.GA↗

arXiv2024

Application of Convolutional Neural Networks to time domain astrophysics. 2D image analysis of OGLE light curves

In recent years the amount of publicly available astronomical data has increased exponentially, with a remarkable example being large scale multiepoch photometric surveys. This wealth of data poses challenges to the classical methodologies commonly employed in the study of variable objects. As a response, deep learning techniques are increasingly being explored to effectively classify, analyze, and interpret these large datasets. In this paper we use two-dimensional histograms to represent Optical Gravitational Lensing Experiment (OGLE) phasefolded light curves as images. We use a Convolutional Neural Network (CNN) to classify variable objects within eight different categories (from now on labels): Classical Cepheid (CEP), RR Lyrae (RR), Long Period Variable (LPV), Miras (M), Ellipsoidal Binary (ELL), Delta Scuti (DST), Eclipsing Binary (E), and spurious class with Incorrect Periods (Rndm). We set up different training sets to train the same CNN architecture in order to characterize the impact of the training. The training sets were built from the same source of labels but different filters and balancing techniques were applied. Namely: Undersampling (U), Data Augmentation (DA), and Batch Balancing (BB). The best performance was achieved with the BB approach and a training sample size of $\sim$370000 stars. Regarding computational performance, the image representation production rate is of $\sim$76 images per core per second, and the time to predict is $\sim$ 60$\, μ\text{s}$ per star. The accuracy of the classification improves from $\sim$ 92%, when based only on the CNN, to $\sim$ 98% when the results of the CNN are combined with the period and amplitude features in a two step approach. This methodology achieves comparable results with previous studies but with two main advantages: the identification of miscalculated periods and the improvement in computational time cost.

astro-ph.IM↗