arXiv subjects

P. Sánchez-Sáez

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

35 records · Page 2Linked to original sources

arXiv2024

SDSS1335+0728: The awakening of a $\sim 10^6 M_{\odot}$ black hole

The galaxy SDSS1335+0728, which had exhibited no prior optical variations during the preceding two decades, began showing significant nuclear variability in the Zwicky Transient Facility (ZTF) alert stream from December 2019 (as ZTF19acnskyy). Its behaviour suggests that SDSS1335+0728 hosts a $\sim 10^6 M_{\odot}$ black hole (BH) that is currently in the process of `turning on'. We present a multi-wavelength photometric analysis and spectroscopic follow-up performed with the aim of better understanding the origin of the nuclear variations detected in SDSS1335+0728. We used archival photometry and spectroscopic data to study the state of SDSS1335+0728 prior to December 2019, and new observations from Swift, SOAR/Goodman, VLT/X-shooter, and Keck/LRIS taken after its turn-on to characterise its current state. We find that: (a) since 2021, the UV flux is four times brighter than the flux reported by GALEX in 2004; (b) since June 2022, the mid-infrared flux has risen more than two times, and the W1-W2 WISE colour has become redder; (c) since February 2024, the source has begun showing X-ray emission; (d) the narrow emission line ratios are now consistent with a more energetic ionising continuum; (e) broad emission lines are not detected; and (f) the [OIII] line increased its flux $\sim 3.6$ years after the first ZTF alert, which implies a relatively compact narrow-line-emitting region. We conclude that the variations observed in SDSS1335+0728 could be either explained by an AGN that is just turning on or by an exotic tidal disruption event (TDE). If the former is true, SDSS1335+0728 is one of the strongest cases of an AGN observed in the process of activating. If the latter, it would correspond to the longest and faintest TDE ever observed (or another class of still unknown nuclear transient). Future observations of SDSS1335+0728 are crucial to further understand its behaviour.

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arXiv2024

Physical Properties of Type II Supernovae Inferred from ZTF and ATLAS Photometric Data

We report an analysis of a sample of 186 spectroscopically confirmed Type II supernova (SN) light curves (LCs) obtained from a combination of Zwicky Transient Facility (ZTF) and Asteroid Terrestrial-impact Last Alert System (ATLAS) observations. We implement a method to infer physical parameters from these LCs using hydrodynamic models that take into account the progenitor mass, the explosion energy, and the presence of circumstellar matter (CSM). The CSM is modelled via the mass loss rate, wind acceleration at the surface of the progenitor star with a $β$ velocity law, and the CSM radius. We also infer the time of explosion, attenuation (A$_V$), and the redshift for each SN. Our results favor low-mass progenitor stars (M$_{ZAMS}$\,$<$14\,$M_\odot$) with a dense CSM ($\dot{M}$ $>$ 10$^{-3}$ [M$_\odot$ yr$^{-1}$], a CSM radius of $\sim$ 10$^{15}$ cm, and $β$ $>$ 2). Additionally, we find that the redshift inferred from the supernova LCs is significantly more accurate than that inferred using the host galaxy photometric redshift, suggesting that this method could be used to infer more accurate host galaxy redshifts from large samples of SNe II in the LSST era. Lastly, we compare our results with similar works from the literature.

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arXiv2024

ATAT: Astronomical Transformer for time series And Tabular data

The advent of next-generation survey instruments, such as the Vera C. Rubin Observatory and its Legacy Survey of Space and Time (LSST), is opening a window for new research in time-domain astronomy. The Extended LSST Astronomical Time-Series Classification Challenge (ELAsTiCC) was created to test the capacity of brokers to deal with a simulated LSST stream. We describe ATAT, the Astronomical Transformer for time series And Tabular data, a classification model conceived by the ALeRCE alert broker to classify light-curves from next-generation alert streams. ATAT was tested in production during the first round of the ELAsTiCC campaigns. ATAT consists of two Transformer models that encode light curves and features using novel time modulation and quantile feature tokenizer mechanisms, respectively. ATAT was trained on different combinations of light curves, metadata, and features calculated over the light curves. We compare ATAT against the current ALeRCE classifier, a Balanced Hierarchical Random Forest (BHRF) trained on human-engineered features derived from light curves and metadata. When trained on light curves and metadata, ATAT achieves a macro F1-score of 82.9 +- 0.4 in 20 classes, outperforming the BHRF model trained on 429 features, which achieves a macro F1-score of 79.4 +- 0.1. The use of Transformer multimodal architectures, combining light curves and tabular data, opens new possibilities for classifying alerts from a new generation of large etendue telescopes, such as the Vera C. Rubin Observatory, in real-world brokering scenarios.

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arXiv2024

A Newborn AGN in a Starforming Galaxy

We report on the finding of a newborn AGN, i.e. current AGN activity in a galaxy previously classified as non-active, and characterize its evolution. Black hole ignition event candidates were selected from a parent sample of spectrally classified non-active galaxies (2.394.312 objects), that currently show optical flux variability indicative of a type I AGN, according to the ALeRCE light curve classifier. A second epoch spectrum for a sample of candidate newborn AGN were obtained with the SOAR telescope to search for new AGN features. We present spectral results for the most convincing case of new AGN activity, for a galaxy with a previous star-forming optical classification, where the second epoch spectrum shows the appearance of prominent, broad Balmer lines without significant changes in the narrow line flux ratios. Long term optical lightcurves show a steady increase in luminosity starting 1.5 years after the SDSS spectrum was taken and continuing for at least 7 years. MIR colors from the WISE catalog have also evolved from typical non active galaxy colors to AGN-like colors and recent X-ray flux detections confirm its AGN nature.

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arXiv2023

Selection of powerful radio galaxies with machine learning

We developed and trained a pipeline of three machine learning (ML) models than can predict which sources are more likely to be an AGN and to be detected in specific radio surveys. Also, it can estimate redshift values for predicted radio-detectable AGNs. These models, which combine predictions from tree-based and gradient-boosting algorithms, have been trained with multi-wavelength data from near-infrared-selected sources in the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX) Spring field. Training, testing, calibration, and validation were carried out in the HETDEX field. Further validation was performed on near-infrared-selected sources in the Stripe 82 field. In the HETDEX validation subset, our pipeline recovers 96% of the initially labelled AGNs and, from AGNs candidates, we recover 50% of previously detected radio sources. For Stripe 82, these numbers are 94% and 55%. Compared to random selection, these rates are two and four times better for HETDEX, and 1.2 and 12 times better for Stripe 82. The pipeline can also recover the redshift distribution of these sources with $σ_{\mathrm{NMAD}}$ = 0.07 for HETDEX ($σ_{\mathrm{NMAD}}$ = 0.09 for Stripe 82) and an outlier fraction of 19% (25% for Stripe 82), compatible with previous results based on broad-band photometry. Feature importance analysis stresses the relevance of near- and mid-infrared colours to select AGNs and identify their radio and redshift nature. Combining different algorithms in ML models shows an improvement in the prediction power of our pipeline over a random selection of sources. Tree-based ML models (in contrast to deep learning techniques) facilitate the analysis of the impact that features have on the predictions. This prediction can give insight into the potential physical interplay between the properties of radio AGNs (e.g. mass of black hole and accretion rate).

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arXiv2023

Improving the selection of changing-look AGNs through multi-wavelength photometric variability

We present second epoch optical spectra for 30 changing-look (CL) candidates found by searching for Type-1 optical variability in a sample of active galactic nuclei (AGNs) spectroscopically classified as Type 2. We use a random-forest-based light curve classifier and spectroscopic follow-up, confirming 50 per cent of candidates as turning-on CLs. In order to improve this selection method and to better understand the nature of the not-confirmed CL candidates, we perform a multi-wavelength variability analysis including optical, mid-infrared (MIR) and X-ray data, and compare the results from the confirmed and not-confirmed CLs identified in this work. We find that most of the not-confirmed CLs are consistent with weak Type 1s dominated by host-galaxy contributions, showing weaker optical and MIR variability. On the contrary, the confirmed CLs present stronger optical fluctuations and experience a long (from five to ten years) increase in their MIR fluxes and the colour W1-W2 over time. In the 0.2-2.3 keV band, at least four out of 11 CLs with available SRG/eROSITA detections have increased their flux in comparison with archival upper limits. These common features allow us to select the most promising CLs from our list of candidates, leading to nine sources with similar multi-wavelength photometric properties to our CL sample. The use of machine learning algorithms with optical and MIR light curves will be very useful to identify CLs in future large-scale surveys.

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arXiv2023

Persistent and occasional: searching for the variable population of the ZTF/4MOST sky using ZTF data release 11

We present a variability, color and morphology based classifier, designed to identify transients, persistently variable, and non-variable sources, from the Zwicky Transient Facility (ZTF) Data Release 11 (DR11) light curves of extended and point sources. The main motivation to develop this model was to identify active galactic nuclei (AGN) at different redshift ranges to be observed by the 4MOST ChANGES project. Still, it serves as a more general time-domain astronomy study. The model uses nine colors computed from CatWISE and PS1, a morphology score from PS1, and 61 single-band variability features computed from the ZTF DR11 g and r light curves. We trained two versions of the model, one for each ZTF band. We used a hierarchical local classifier per parent node approach, where each node was composed of a balanced random forest model. We adopted a 17-class taxonomy, including non-variable stars and galaxies, three transient classes, five classes of stochastic variables, and seven classes of periodic variables. The macro averaged precision, recall and F1-score are 0.61, 0.75, and 0.62 for the g-band model, and 0.60, 0.74, and 0.61, for the r-band model. When grouping the four AGN classes into one single class, its precision, recall, and F1-score are 1.00, 0.95, and 0.97, respectively, for both the g and r bands. We applied the model to all the sources in the ZTF/4MOST overlapping sky, avoiding ZTF fields covering the Galactic bulge, including 86,576,577 light curves in the g-band and 140,409,824 in the r-band. Only 0.73\% of the g-band light curves and 2.62\% of the r-band light curves were classified as stochastic, periodic, or transient with high probability ($P_{init}\geq0.9$). We found that, in general, more reliable results are obtained when using the g-band model. Using the latter, we identified 384,242 AGN candidates, 287,156 of which have $P_{init}\geq0.9$.

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arXiv2023

Multiwavelength monitoring of the nucleus in PBC J2333.9-2343: the giant radio galaxy with a blazar-like core

PBC J2333.9-2343 is a giant radio galaxy at z = 0.047 with a bright central core associated to a blazar nucleus. If the nuclear blazar jet is a new phase of the jet activity, then the small orientation angle suggest a dramatic change of the jet direction. We present observations obtained between September 2018 and January 2019 (cadence larger than three days) with Effeslberg, SMARTS-1.3m, ZTF, ATLAS, Swift, and Fermi-LAT, and between April-July 2019 (daily cadence) with SMARTS-1.3m and ATLAS. Large (>2x) flux increases are observed on timescales shorter than a month, which are interpreted as flaring events. The cross correlation between the SMARTS-1.3m monitoring in the NIR and optical shows that these data do not show significant time lag within the measured errors. A comparison of the optical variability properties between non-blazars and blazars AGN shows that PBC J2333.9-2343 has properties more comparable to the latter. The SED of the nucleus shows two peaks, that were fitted with a one zone leptonic model. Our data and modelling shows that the high energy peak is dominated by External Compton from the dusty torus with mild contribution from Inverse Compton from the jet. The derived jet angle of 3 degrees is also typical of a blazar. Therefore, we confirm the presence of a blazar-like core in the center of this giant radio galaxy, likely a Flat Spectrum Radio Quasar with peculiar properties.

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arXiv2022

The Type 1 and Type 2 AGN dichotomy according to their ZTF optical variability

The scarce optical variability studies in spectrally classified Type 2 active galactic nuclei (AGNs) have led to the discovery of anomalous objects that are incompatible with the simplest unified models (UM). This paper focuses on the exploration of different variability features that allows to separate between obscured, Type 2 AGNs, and the variable, unobscured Type 1s. We analyse systematically the Zwicky Transient Facility, 2.5 years long light curves of ~ 15000 AGNs from the Sloan Digital Sky Survey Data Release 16, which are generally considered Type 2s due to the absence of strong broad emission lines (BELs). Consistently with the expectations from the UM, the variability features are distributed differently for distinct populations, with spectrally classified weak Type 1s showing 1 order of magnitude larger variances than the Type 2s. We find that the parameters given by the damped random walk model leads to broader Hα equivalent width for objects with τ_g > 16 d and long term structure function SF{\infty},g> 0.07 mag. By limiting the variability features, we find that ~ 11 per cent of Type 2 sources show evidence for optical variations. A detailed spectral analysis of the most variable sources (~1 per cent of the Type 2 sample) leads to the discovery of misclassified Type 1s with weak BELs and changing-state candidates. This work presents one of the largest systematic investigations of Type 2 AGN optical variability to date, in preparation for future large photometric surveys.

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arXiv2022

A structure function analysis of VST-COSMOS AGN

We present our sixth work in a series dedicated to variability studies of active galactic nuclei (AGN) based on the survey of the COSMOS field by the VLT Survey Telescope (VST). Its 54 r-band visits over 3.3 yr and single-visit depth of 24.6 r-band mag make this dataset a valuable scaled-down version that can help forecast the performance of the Rubin Observatory Legacy Survey of Space and Time (LSST). This work is centered on the analysis of the structure function (SF) of VST-COSMOS AGN, investigating possible differences in its shape and slope related to how the AGN were selected, and explores possible connections between the ensemble variability of AGN and black-hole mass, accretion rate, bolometric luminosity, redshift, and obscuration of the source. Given its features, our dataset opens up the exploration of samples ~2 mag fainter than most of the literature to date. We identify several samples of AGN - 677 in total - obtained by a variety of selection techniques which partly overlap. Our analysis compares results for the various samples. We split each sample in two based on the median of the physical property of interest, and analyze differences in the shape and slope of the SF, and possible causes. While the shape of the SF does not change with depth, it is highly affected by the type of AGN (unobscured/obscured) included in the sample. Where a linear region can be identified, we find that the variability amplitude anticorrelates with accretion rate and bolometric luminosity, consistent with previous literature on the topic, while no dependence on black-hole mass emerges from this study. With its longer baseline and denser and more regular sampling, the LSST will allow an improved characterization of the SF and its dependencies on the mentioned physical properties over much larger AGN samples.

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arXiv2022

Confirming new changing--look AGNs discovered through optical variability using a random-forest based light curve classifier

Determining the frequency and duration of changing--look (CL) active galactic nuclei (AGNs) phenomena, where the optical broad emission lines appear or disappear, is crucial to understand the evolution of the accretion flow around supermassive black holes. We present a strategy to select new CL candidates starting from a spectroscopic type 2 AGNs sample and searching for current type 1 photometric variability. We use the publicly available Zwicky Transient Facility (ZTF) alert stream and the Automatic Learning for the Rapid Classification of Events (ALeRCE) light curve classifier to produce a list of CL candidates with a highly automated algorithm, resulting in 60 candidates. Visual inspection reduced the sample to 30. We performed new spectroscopic observations of six candidates of our clean sample, without further refinement, finding the appearance of clear broad Balmer lines in four of them and tentative evidence of type changes in the remaining two, which suggests a promising success rate of $\geq66$ per cent for this CL selection method.

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arXiv2021

Searching for changing-state AGNs in massive datasets -- I: applying deep learning and anomaly detection techniques to find AGNs with anomalous variability behaviours

The classic classification scheme for Active Galactic Nuclei (AGNs) was recently challenged by the discovery of the so-called changing-state (changing-look) AGNs (CSAGNs). The physical mechanism behind this phenomenon is still a matter of open debate and the samples are too small and of serendipitous nature to provide robust answers. In order to tackle this problem, we need to design methods that are able to detect AGN right in the act of changing-state. Here we present an anomaly detection (AD) technique designed to identify AGN light curves with anomalous behaviors in massive datasets. The main aim of this technique is to identify CSAGN at different stages of the transition, but it can also be used for more general purposes, such as cleaning massive datasets for AGN variability analyses. We used light curves from the Zwicky Transient Facility data release 5 (ZTF DR5), containing a sample of 230,451 AGNs of different classes. The ZTF DR5 light curves were modeled with a Variational Recurrent Autoencoder (VRAE) architecture, that allowed us to obtain a set of attributes from the VRAE latent space that describes the general behaviour of our sample. These attributes were then used as features for an Isolation Forest (IF) algorithm, that is an anomaly detector for a "one class" kind of problem. We used the VRAE reconstruction errors and the IF anomaly score to select a sample of 8,809 anomalies. These anomalies are dominated by bogus candidates, but we were able to identify 75 promising CSAGN candidates.

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arXiv2020

Alert Classification for the ALeRCE Broker System: The Light Curve Classifier

We present the first version of the ALeRCE (Automatic Learning for the Rapid Classification of Events) broker light curve classifier. ALeRCE is currently processing the Zwicky Transient Facility (ZTF) alert stream, in preparation for the Vera C. Rubin Observatory. The ALeRCE light curve classifier uses variability features computed from the ZTF alert stream, and colors obtained from AllWISE and ZTF photometry. We apply a Balanced Random Forest algorithm with a two-level scheme, where the top level classifies each source as periodic, stochastic, or transient, and the bottom level further resolves each of these hierarchical classes, amongst 15 total classes. This classifier corresponds to the first attempt to classify multiple classes of stochastic variables (including core- and host-dominated active galactic nuclei, blazars, young stellar objects, and cataclysmic variables) in addition to different classes of periodic and transient sources, using real data. We created a labeled set using various public catalogs (such as the Catalina Surveys and {\em Gaia} DR2 variable stars catalogs, and the Million Quasars catalog), and we classify all objects with $\geq6$ $g$-band or $\geq6$ $r$-band detections in ZTF (868,371 sources as of 2020/06/09), providing updated classifications for sources with new alerts every day. For the top level we obtain macro-averaged precision and recall scores of 0.96 and 0.99, respectively, and for the bottom level we obtain macro-averaged precision and recall scores of 0.57 and 0.76, respectively. Updated classifications from the light curve classifier can be found at the \href{http://alerce.online}{ALeRCE Explorer website}.

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arXiv2020

A random forest-based selection of optically variable AGN in the VST-COSMOS field

The survey of the COSMOS field by the VLT Survey Telescope is an appealing testing ground for variability studies of active galactic nuclei (AGN). With 54 r-band visits over 3.3 yr and a single-visit depth of 24.6 r-band mag, the dataset is also particularly interesting in the context of performance forecasting for the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). This work is the fifth in a series dedicated to the development of an automated, robust, and efficient methodology to identify optically variable AGN, aimed at deploying it on future LSST data. We test the performance of a random forest (RF) algorithm in selecting optically variable AGN candidates, investigating how the use of different AGN labeled sets (LSs) and features sets affects this performance. We define a heterogeneous AGN LS and choose a set of variability features and optical and near-infrared colors based on what can be extracted from LSST data. We find that an AGN LS that includes only Type I sources allows for the selection of a highly pure (91%) sample of AGN candidates, obtaining a completeness with respect to spectroscopically confirmed AGN of 69% (vs. 59% in our previous work). The addition of colors to variability features mildly improves the performance of the RF classifier, while colors alone prove less effective than variability in selecting AGN as they return contaminated samples of candidates and fail to identify most host-dominated AGN. We observe that a bright (r < 21 mag) AGN LS is able to retrieve candidate samples not affected by the magnitude cut, which is of great importance as faint AGN LSs for LSST-related studies will be hard to find and likely imbalanced. We estimate a sky density of 6.2 million AGN for the LSST main survey down to our current magnitude limit.

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arXiv2020

The Automatic Learning for the Rapid Classification of Events (ALeRCE) Alert Broker

We introduce the Automatic Learning for the Rapid Classification of Events (ALeRCE) broker, an astronomical alert broker designed to provide a rapid and self--consistent classification of large etendue telescope alert streams, such as that provided by the Zwicky Transient Facility (ZTF) and, in the future, the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). ALeRCE is a Chilean--led broker run by an interdisciplinary team of astronomers and engineers, working to become intermediaries between survey and follow--up facilities. ALeRCE uses a pipeline which includes the real--time ingestion, aggregation, cross--matching, machine learning (ML) classification, and visualization of the ZTF alert stream. We use two classifiers: a stamp--based classifier, designed for rapid classification, and a light--curve--based classifier, which uses the multi--band flux evolution to achieve a more refined classification. We describe in detail our pipeline, data products, tools and services, which are made public for the community (see \url{https://alerce.science}). Since we began operating our real--time ML classification of the ZTF alert stream in early 2019, we have grown a large community of active users around the globe. We describe our results to date, including the real--time processing of $9.7\times10^7$ alerts, the stamp classification of $1.9\times10^7$ objects, the light curve classification of $8.5\times10^5$ objects, the report of 3088 supernova candidates, and different experiments using LSST-like alert streams. Finally, we discuss the challenges ahead to go from a single-stream of alerts such as ZTF to a multi--stream ecosystem dominated by LSST.

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arXiv2019

The QUEST-La Silla AGN Variability Survey: selection of AGN candidates through optical variability

We used data from the QUEST-La Silla Active Galactic Nuclei (AGN) variability survey to construct light curves for 208,583 sources over $\sim 70$ deg$^2$, with a a limiting magnitude $r \sim 21$. Each light curve has at least 40 epochs and a length of $\geq 200$ days. We implemented a Random Forest algorithm to classify our objects as either AGN or non-AGN according to their variability features and optical colors, excluding morphology cuts. We tested three classifiers, one that only includes variability features (RF1), one that includes variability features and also $r-i$ and $i-z$ colors (RF2), and one that includes variability features and also $g-r$, $r-i$, and $i-z$ colors (RF3). We obtained a sample of high probability candidates (hp-AGN) for each classifier, with 5,941 candidates for RF1, 5,252 candidates for RF2, and 4,482 candidates for RF3. We divided each sample according to their $g-r$ colors, defining blue ($g-r\leq 0.6$) and red sub-samples ($g-r>0.6$). We find that most of the candidates known from the literature belong to the blue sub-samples, which is not necessarily surprising given that, unlike for many literature studies, we do not cut our sample to point-like objects. This means that we can select AGN that have a significant contribution from redshifted starlight in their host galaxies. In order to test the efficiency of our technique we performed spectroscopic follow-up, confirming the AGN nature of 44 among 54 observed sources (81.5\% of efficiency). From the campaign we concluded that RF2 provides the purest sample of AGN candidates.

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arXiv2018

The QUEST-La Silla AGN variability survey: connection between AGN variability and black hole physical properties

We present our statistical analysis of the connection between active galactic nuclei (AGN) variability and physical properties of the central supermassive black hole (SMBH). We constructed optical light curves using data from the QUEST-La Silla AGN variability survey. To model the variability, we used the structure function, among the excess variance and the amplitude from Damp Random Walk (DRW) modeling. For the measurement of SMBH physical properties, we used public spectra from the Sloan Digital Sky Survey (SDSS). Our analysis is based on an original sample of 2345 sources detected in both SDSS and QUEST-La Silla. For 1473 of these sources we could perform a proper measurement of the spectral and variability properties, and 1348 of these sources were classified as variable ($91.5\%$). We found that the amplitude of the variability ($A$) depends solely on the rest frame emission wavelength and the Eddington ratio, where $A$ anti-correlates with both $λ_{rest}$ and $L/L_{\text{Edd}}$. This suggests that AGN variability does not evolve over cosmic time, and its amplitude is inversely related to the accretion rate. We found that the logarithmic gradient of the variability ($γ$) does not correlate significantly with any SMBH physical parameter, since there is no statistically significant linear regression model with an absolute value of the slope higher than 0.1. Finally, we found that the general distribution of $γ$ measured for our sample differs from the distribution of $γ$ obtained for light curves simulated from a DRW process. For 20.6\% of the variable sources in our sample, a DRW model is not appropriate to describe the variability, since $γ$ differs considerably from the expected value of 0.5.

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