arXiv subjects

Vincenzo Petrecca

Publications and source records attributed to Vincenzo Petrecca.

8 recordsLinked to original sources

arXiv2026

Scavenger hunt: Selection of obscured active galactic nuclei combining multiband optical variability and colors

As wide-field optical surveys such as Vera Rubin Observatory's Legacy Survey of Space and Time (LSST) begin operations, time-domain astronomy is facing a data revolution, paving the road for new, expanded variability studies. This work leverages the complementary power of optical variability and color selection to identify active galactic nuclei (AGN), focusing on optimizing the identification of obscured AGN, typically more challenging to distinguish from inactive galaxies based on optical variability alone. The analysis is designed to provide valuable insights in the context of performance preview for the LSST, albeit using a scaled-down version of the LSST dataset. We present the first combined AGN selection based on g+r+i band light curves from the VST-COSMOS survey, spanning 3.3 yr. We identify AGN candidates independently in each band using a random forest (RF) classifier trained on features mainly related to optical variability, along with six optical/infrared colors and a morphology indicator. We subsequently merge the three band-specific samples in order to enhance selection purity and reliability. We then focus on defining a subset of features that significantly improve the identification of obscured AGN. The RF classifiers yield a consistent performance across the three bands, highlighting the critical role of contamination. Using the combined three-band plus color selection we successfully recover $58^{+9}_{-8}\%$ of all AGN and $69^{+10}_{-8}\%$ of the known obscured AGN that have been independently confirmed in all three bands. When requiring confirmation in two out of the three bands, these fractions increase to $69^{+10}_{-8}\%$ and $80^{+10}_{-9}\%$, respectively. We also demonstrate that, while combining variability features with colors is crucial to improve obscured AGN selection, relying solely on color features returns a markedly higher contamination rate.

astro-ph.GA↗

arXiv2025

Continuous Wide-Field Optical Monitoring for Very Early-Phase Transient Discovery

The study of transient phenomena in a multimessenger context is expected to remain a major pillar of astrophysical discovery in the decades ahead. Supernovae, Kilonovae, Black-Hole formation, Novae, GRBs, and tidal disruption events are prime examples, as their earliest phases link electromagnetic radiation to gravitational waves, neutrinos, and high-energy emission. Yet, the physics connecting these messengers unfolds within minutes to hours, while traditional surveys revisit the same region of the sky on the scale of days/weeks, missing when the event begins. Current survey facilities excel at answering what happened and how often, but essentially fail in addressing how it happened and how it couples to gravitational waves, neutrinos, or high-energy emission. Continuous wide-area optical monitoring, as proposed here, removes this limitation. The traditional approach, where a GW or neutrino alert triggers electromagnetic follow-up, is now complemented, and sometimes reversed: early electromagnetic discoveries can prompt searches for weaker gravitational waves or neutrino signals that would otherwise be missed. In the Einstein Telescope era, wide-field optical monitoring will allow us to find the optical counterparts of gravitational-wave events and understand their physics. At the same time, a telescope capable of continuous monitoring provides immediate scientific value for planetary defense, space-debris tracking, stellar variability, exoplanets transit monitoring, accretion-driven activity, and when we step into a new observational territory, the true discoveries are often the ones we did not expect. In this vision, continuous time-domain astronomy does not replace classical surveys: it completes them by supplying the missing temporal dimension. Follow-up observations remain essential, but they now begin at the physical onset of the event rather than after its evolution is underway.

astro-ph.IM↗

arXiv2025

The ensemble broad-frequency power spectrum of Stripe-82 quasars from multiple surveys

Variability is a striking features of quasars, observed at all timescales wavelengths. Studying its properties and the correlations with the physical parameters (e.g. black hole mass and accretion rate) provides significant insights into accretion physics. However, the detailed picture and the exact interplay between different emitting regions are not yet clear. We combine data from Sloan Digital Sky Survey (SDSS), the Panoramic Survey Telescope and Rapid Response System 1 (Pan-STARRS1, PS1), the Zwicky Transient Facility (ZTF), and the Gaia space telescope to constrain the power spectrum of quasars in the Stripe-82 region over a broad frequency range, 10^{-1} to 10^{-3} day^{-1}(rest frame). Light curves are matched and cross-calibrated to reach \sim 20 years in the r-band for 4037 quasars. We split the sample into bins of the same black hole mass, accretion rate, and redshift, and measure the ensemble power spectral density (PSD) in each bin. The power spectra of SDSS, ZTF, and Gaia are measured independently. We do not measure it on PS1 data due to more erratic cadence, but we discuss the use of interpolation techniques, eventually allowing us to use the data together. We find significant evidence that the long-term UV/optical variability of quasars is stationary, as the ensemble PSD estimates from SDSS, Gaia and ZTF are consistent within the errors despite coming from different surveys and years. The PSD shape is consistent with a bending power law with spectral indices of -2.7 and -1 at high and low frequencies. A fit with the PSD associated with a damped random walk is significantly worse. The PSD amplitude below the break does not depend on black hole mass, but there is some evidence for anti-correlation with the accretion rate. The bending frequency, instead, scales with the black hole mass as $ν_b$ \propto M_{\mathrm{BH}}^{-0.6\pm0.1} and does not depend on the accretion rate.

astro-ph.GA↗

arXiv2025

Selection of optically variable active galactic nuclei via a random forest algorithm

Context. A defining characteristic of active galactic nuclei (AGN) that distinguishes them from other astronomical sources is their stochastic variability, which is observable across the entire electromagnetic spectrum. Upcoming optical wide-field surveys, such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time, are set to transform astronomy by delivering unprecedented volumes of data for time domain studies. This data influx will require the development of the expertise and methodologies necessary to manage and analyze it effectively. Aims. This project focuses on optimizing AGN selection through optical variability in wide-field surveys and aims to reduce the bias against obscured AGN. We tested a random forest (RF) algorithm trained on various feature sets to select AGN. The initial dataset consisted of 54 observations in the r-band and 25 in the g-band of the COSMOS field, captured with the VLT Survey Telescope over a 3.3-year baseline. Methods. Our analysis relies on feature sets derived separately from either band plus a set of features combining data from both bands, mostly characterizing AGN on the basis of their variability properties and obtained from their light curves. We trained multiple RF classifiers using different subsets of selected features and assessed their performance via targeted metrics. Results. Our tests provide valuable insights into the use of multiband and multivisit data for AGN identification. We compared our findings with previous studies and dedicated part of the analysis to potential enhancements in selecting obscured AGN. The expertise gained and the methodologies developed here are readily applicable to datasets from other ground- and space-based missions.

astro-ph.GA↗

arXiv2024

Ensemble power spectral density of SDSS quasars in UV/optical bands

Context: Quasar variability has proven to be a powerful tool to constrain the properties of their inner engine and the accretion process onto supermassive black holes. Correlations between UV/optical variability and physical properties have been long studied with a plethora of different approaches and time-domain surveys, although the detailed picture is not yet clear. Aims: We analysed archival data from the SDSS Stripe-82 region to study how the quasar power spectral density (PSD) depends on the black hole mass, bolometric luminosity, accretion rate, redshift, and rest-frame wavelength. We developed a model-independent analysis framework that could be easily applied to upcoming large surveys such as the Legacy Survey of Space and Time (LSST). Methods: We used light curves of 8042 spectroscopically confirmed quasars, observed in at least six yearly seasons in five filters ugriz. We split the sample into bins of similar physical properties containing at least 50 sources, and we measured the ensemble PSD in each of them. Results: We find that a simple power law is a good fit to the power spectra in the frequency range explored. Variability does not depend on redshift at a fixed wavelength. Instead, both PSD amplitude and slope depend on the black hole mass, accretion rate, and rest-frame wavelength. We provide scaling relations to model the observed variability as a function of the physical properties, and discuss the possibility of a universal PSD shape for all quasars, where frequencies scale with the black hole mass, while normalization and slope(s) are fixed (at any given wavelength and accretion rate).

astro-ph.GA↗

arXiv2024

Recovered SN Ia rate from simulated LSST images

The Legacy Survey of Space and Time (LSST) will revolutionize Time Domain Astronomy by detecting millions of transients. In particular, it is expected to increment the number of type Ia supernovae (SNIa) of a factor of 100 compared to existing samples up to z~1.2. Such a high number of events will dramatically reduce statistical uncertainties in the analysis of SNIa properties and rates. However, the impact of all other sources of uncertainty on the measurement must still be evaluated. The comprehension and reduction of such uncertainties will be fundamental both for cosmology and stellar evolution studies, as measuring the SNIa rate can put constraints on the evolutionary scenarios of different SNIa progenitors. We use simulated data from the DESC Data Challenge 2 (DC2) and LSST Data Preview 0 (DP0) to measure the SNIa rate on a 15 deg2 region of the Wide-Fast-Deep area. We select a sample of SN candidates detected on difference images, associate them to the host galaxy, and retrieve their photometric redshifts (z-phot). Then, we test different light curves classification methods, with and without redshift priors. We discuss how the distribution in redshift measured for the SN candidates changes according to the selected host galaxy and redshift estimate. We measure the SNIa rate analyzing the impact of uncertainties due to z-phot, host galaxy association and classification on the distribution in redshift of the starting sample. We found a 17% average lost fraction of SNIa with respect to the simulated sample. As 10% of the bias is due to the uncertainty on the z-phot alone (which also affects classification when used as a prior), it results to be the major source of uncertainty. We discuss possible reduction of the errors in the measurement of the SNIa rate, including synergies with other surveys, which may help using the rate to discriminate different progenitor models.

astro-ph.CO↗

arXiv2023

The LSST AGN Data Challenge: Selection methods

Development of the Rubin Observatory Legacy Survey of Space and Time (LSST) includes a series of Data Challenges (DC) arranged by various LSST Scientific Collaborations (SC) that are taking place during the projects preoperational phase. The AGN Science Collaboration Data Challenge (AGNSCDC) is a partial prototype of the expected LSST AGN data, aimed at validating machine learning approaches for AGN selection and characterization in large surveys like LSST. The AGNSC-DC took part in 2021 focusing on accuracy, robustness, and scalability. The training and the blinded datasets were constructed to mimic the future LSST release catalogs using the data from the Sloan Digital Sky Survey Stripe 82 region and the XMM-Newton Large Scale Structure Survey region. Data features were divided into astrometry, photometry, color, morphology, redshift and class label with the addition of variability features and images. We present the results of four DC submitted solutions using both classical and machine learning methods. We systematically test the performance of supervised (support vector machine, random forest, extreme gradient boosting, artificial neural network, convolutional neural network) and unsupervised (deep embedding clustering) models when applied to the problem of classifying/clustering sources as stars, galaxies or AGNs. We obtained classification accuracy 97.5% for supervised and clustering accuracy 96.0% for unsupervised models and 95.0% with a classic approach for a blinded dataset. We find that variability features significantly improve the accuracy of the trained models and correlation analysis among different bands enables a fast and inexpensive first order selection of quasar candidates

astro-ph.GA↗

arXiv2022

Rubin Observatory LSST Transients and Variable Stars Roadmap

The Vera C. Rubin Legacy Survey of Space and Time holds the potential to revolutionize time domain astrophysics, reaching completely unexplored areas of the Universe and mapping variability time scales from minutes to a decade. To prepare to maximize the potential of the Rubin LSST data for the exploration of the transient and variable Universe, one of the four pillars of Rubin LSST science, the Transient and Variable Stars Science Collaboration, one of the eight Rubin LSST Science Collaborations, has identified research areas of interest and requirements, and paths to enable them. While our roadmap is ever-evolving, this document represents a snapshot of our plans and preparatory work in the final years and months leading up to the survey's first light.

astro-ph.IM↗