arXiv subjects

F. Stoppa

Publications and source records attributed to F. Stoppa.

8 recordsLinked to original sources

arXiv2026

The flash-ionised SN Ibn 2025kzr: H-free CSM formed during a precursor outburst 55 days prior to explosion

Type Ibn supernovae (SNe) are a class of interacting SNe characterised by narrow helium lines in their spectra. We present an extensive observational dataset of the Type Ibn SN 2025kzr at 51 Mpc, including the discovery of a precursor outburst with a peak brightness of M_r~-13.6 mag beginning ~55 days before explosion. Our photometry indicates the SN was discovered within the first day of explosion, showing fast-rising, ultraviolet-bright emission peaking at M_r=-19.26+/-0.09 mag and a peak blackbody temperature of T~29000 K, consistent with shock breakout within a region of dense and confined circumstellar material (CSM). Our high-cadence spectroscopic dataset spanning 1.9-58.5 days post-explosion shows flash-ionised emission features during the first 10 days. In our SALT spectrum at 3.8 days we observe a pronounced blueshift of the He II lines by 460 km/s compared to the He I lines at zero velocity, while a Pickering-decrement analysis reveals a CSM that is fully hydrogen-free. The timing of the disappearance of the flash features combined with the CSM velocity of 1500 km/s imply a mass-loss event ~66 days before explosion, in close agreement with the timing of the precursor observed 55 days before explosion and strongly suggestive of a physical link. We derive a CSM mass of 0.03-1.7 M_sun and a corresponding high mass-loss rate >~10^{-1} M_sun/yr. The precursor timescale and energetics suggest an extreme mass-loss event that might be explained by wave-driven mass loss during the late stages of nuclear burning, in particular the oxygen-burning phase. Overall, we favour a single massive Wolf-Rayet progenitor with M_ZAMS~30-40 M_sun to explain SN 2025kzr, although a binary origin cannot be excluded.

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arXiv2025

Pan-STARRS follow-up of the gravitational-wave event S250818k and the lightcurve of SN 2025ulz

Kilonovae are the scientifically rich, but observationally elusive, optical transient phenomena associated with compact binary mergers. Only a handful of events have been discovered to date, all through multi-wavelength (gamma ray) and multi-messenger (gravitational wave) signals. Given their scarcity, it is important to maximise the discovery possibility of new kilonova events. To this end, we present our follow-up observations of the gravitational-wave signal, S250818k, a plausible binary neutron star merger at a distance of $237 \pm 62$ Mpc. Pan-STARRS tiled 286 and 318 square degrees (32% and 34% of the 90% sky localisation region) within 3 and 7 days of the GW signal, respectively. ATLAS covered 65% of the skymap within 3 days, but with lower sensitivity. These observations uncovered 47 new transients; however, none were deemed to be linked to S250818k. We undertook an expansive follow-up campaign of AT 2025ulz, the purported counterpart to S250818k. The griz-band lightcurve, combined with our redshift measurement ($z = 0.0849 \pm 0.0003$) all indicate that SN 2025ulz is a SN IIb, and thus not the counterpart to S250818k. We rule out the presence of a AT 2017gfo-like kilonova within $\approx 27$% of the distance posterior sampled by our Pan-STARRS pointings ($\approx 9.1$% across the total 90% three-dimensional sky localisation). We demonstrate that early observations are optimal for probing the distance posterior of the three-dimensional gravitational-wave skymap, and that SN 2025ulz was a plausible kilonova candidate for $\lesssim 5$ days, before ultimately being ruled out.

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arXiv2024

Automated Detection of Satellite Trails in Ground-Based Observations Using U-Net and Hough Transform

The expansion of satellite constellations poses a significant challenge to optical ground-based astronomical observations, as satellite trails degrade observational data and compromise research quality. Addressing these challenges requires developing robust detection methods to enhance data processing pipelines, creating a reliable approach for detecting and analyzing satellite trails that can be easily reproduced and applied by other observatories and data processing groups. Our method, called ASTA (Automated Satellite Tracking for Astronomy), combines deep learning and computer vision techniques for effective satellite trail detection. It employs a U-Net based deep learning network to initially detect trails, followed by a Probabilistic Hough Transform to refine the output. ASTA's U-Net model was trained on a dataset with manually labelled full-field MeerLICHT images prepared using the LABKIT annotation tool, ensuring high-quality and precise annotations. This annotation process was crucial for the model to learn and generalize the characteristics of satellite trails effectively. Furthermore, the user-friendly LABKIT tool facilitated quick and efficient data refinements, streamlining the overall model development process. ASTA's performance was evaluated on a test set of 20,000 image patches, both with and without satellite trails, to rigorously assess its precision and recall. Additionally, ASTA was applied to approximately 200,000 full-field MeerLICHT images, demonstrating its effectiveness in identifying and characterizing satellite trails. The software's results were validated by cross-referencing detected trails with known public satellite catalogs, confirming its reliability and showcasing its ability to uncover previously untracked objects.

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arXiv2024

The BlackGEM telescope array I: Overview

The main science aim of the BlackGEM array is to detect optical counterparts to gravitational wave mergers. Additionally, the array will perform a set of synoptic surveys to detect Local Universe transients and short time-scale variability in stars and binaries, as well as a six-filter all-sky survey down to ~22nd mag. The BlackGEM Phase-I array consists of three optical wide-field unit telescopes. Each unit uses an f/5.5 modified Dall-Kirkham (Harmer-Wynne) design with a triplet corrector lens, and a 65cm primary mirror, coupled with a 110Mpix CCD detector, that provides an instantaneous field-of-view of 2.7~square degrees, sampled at 0.564\arcsec/pixel. The total field-of-view for the array is 8.2 square degrees. Each telescope is equipped with a six-slot filter wheel containing an optimised Sloan set (BG-u, BG-g, BG-r, BG-i, BG-z) and a wider-band 440-720 nm (BG-q) filter. Each unit telescope is independent from the others. Cloud-based data processing is done in real time, and includes a transient-detection routine as well as a full-source optimal-photometry module. BlackGEM has been installed at the ESO La Silla observatory as of October 2019. After a prolonged COVID-19 hiatus, science operations started on April 1, 2023 and will run for five years. Aside from its core scientific program, BlackGEM will give rise to a multitude of additional science cases in multi-colour time-domain astronomy, to the benefit of a variety of topics in astrophysics, such as infant supernovae, luminous red novae, asteroseismology of post-main-sequence objects, (ultracompact) binary stars, and the relation between gravitational wave counterparts and other classes of transients

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arXiv2023

XMM-Newton-discovered Fast X-ray Transients: Host galaxies and limits on contemporaneous detections of optical counterparts

Extragalactic fast X-ray transients (FXTs) are a class of soft (0.3-10 keV) X-ray transients lasting a few hundred seconds to several hours. Several progenitor mechanisms have been suggested to produce FXTs, including supernova shock breakouts, binary neutron star mergers, or tidal disruptions involving an intermediate-mass black hole and a white dwarf. We present detailed host studies, including spectroscopic observations of the host galaxies of 7 XMM-Newton-discovered FXTs. The candidate hosts lie at redshifts 0.0928 $< z <$ 0.645 implying peak X-ray luminosities of 10$^{43}$ erg s$^{-1}$ $< L_X <$ 10$^{45}$ erg s$^{-1}$,and physical offsets of 1 kpc < $r_\mathrm{proj}$ < 22 kpc. These observations increase the number of FXTs with a spectroscopic redshift measurement by a factor of 2, although we note that one event is re-identified as a Galactic flare star. We infer host star formation rates and stellar masses by fitting the combined spectroscopic and archival photometric data. We also report on a contemporaneous optical counterpart search to the FXTs in Pan-STARRS and ATLAS by performing forced photometry at the position of the FXTs. We do not find any counterpart in our search. Given our constraints, including peak X-ray luminosities, optical limits, and host properties, we find that XRT 110621 is consistent with a SN SBO event. Spectroscopic redshifts of likely host galaxies for four events imply peak X-ray luminosities that are too high to be consistent with SN SBOs, but we are unable to discard either the BNS or WD-IMBH TDE scenarios for these FXTs.

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arXiv2023

FINKER: Frequency Identification through Nonparametric KErnel Regression in astronomical time series

Optimal frequency identification in astronomical datasets is crucial for variable star studies, exoplanet detection, and asteroseismology. Traditional period-finding methods often rely on specific parametric assumptions, employ binning procedures, or overlook the regression nature of the problem, limiting their applicability and precision. We aim to introduce a universal, nonparametric kernel regression method for optimal frequency determination that is generalizable, efficient, and robust across various astronomical data types. FINKER uses nonparametric kernel regression on folded datasets at different frequencies, selecting the optimal frequency by minimizing squared residuals. This technique inherently incorporates a weighting system that accounts for measurement uncertainties and facilitates multiband data analysis. We evaluate our method's performance across a range of frequencies pertinent to diverse data types and compare it with an established period-finding algorithm, conditional entropy. The method demonstrates superior performance in accuracy and robustness compared to existing algorithms, requiring fewer observations to identify significant frequencies reliably. It exhibits resilience against noise and adapts well to datasets with varying complexity.

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arXiv2023

AutoSourceID-Classifier. Star-Galaxy Classification using a Convolutional Neural Network with Spatial Information

Aims. Traditional star-galaxy classification techniques often rely on feature estimation from catalogues, a process susceptible to introducing inaccuracies, thereby potentially jeopardizing the classification's reliability. Certain galaxies, especially those not manifesting as extended sources, can be misclassified when their shape parameters and flux solely drive the inference. We aim to create a robust and accurate classification network for identifying stars and galaxies directly from astronomical images. By leveraging convolutional neural networks (CNN) and additional information about the source position, we aim to accurately classify all stars and galaxies within a survey, particularly those with a signal-to-noise ratio (S/N) near the detection limit. Methods. The AutoSourceID-Classifier (ASID-C) algorithm developed here uses 32x32 pixel single filter band source cutouts generated by the previously developed ASID-L code. ASID-C utilizes CNNs to distinguish these cutouts into stars or galaxies, leveraging their strong feature-learning capabilities. Subsequently, we employ a modified Platt Scaling calibration for the output of the CNN. This technique ensures that the derived probabilities are effectively calibrated, delivering precise and reliable results. Results. We show that ASID-C, trained on MeerLICHT telescope images and using the Dark Energy Camera Legacy Survey (DECaLS) morphological classification, outperforms similar codes like SourceExtractor. ASID-C opens up new possibilities for accurate celestial object classification, especially for sources with a S/N near the detection limit. Potential applications of ASID-C, like real-time star-galaxy classification and transient's host identification, promise significant contributions to astronomical research.

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arXiv2023

AutoSourceID-FeatureExtractor. Optical image analysis using a two-step mean variance estimation network for feature estimation and uncertainty characterisation

Aims. In astronomy, machine learning has been successful in various tasks such as source localisation, classification, anomaly detection, and segmentation. However, feature regression remains an area with room for improvement. We aim to design a network that can accurately estimate sources' features and their uncertainties from single-band image cutouts, given the approximated locations of the sources provided by the previously developed code AutoSourceID-Light (ASID-L) or other external catalogues. This work serves as a proof of concept, showing the potential of machine learning in estimating astronomical features when trained on meticulously crafted synthetic images and subsequently applied to real astronomical data. Methods. The algorithm presented here, AutoSourceID-FeatureExtractor (ASID-FE), uses single-band cutouts of 32x32 pixels around the localised sources to estimate flux, sub-pixel centre coordinates, and their uncertainties. ASID-FE employs a two-step mean variance estimation (TS-MVE) approach to first estimate the features and then their uncertainties without the need for additional information, for example the point spread function (PSF). For this proof of concept, we generated a synthetic dataset comprising only point sources directly derived from real images, ensuring a controlled yet authentic testing environment. Results.We show that ASID-FE, trained on synthetic images derived from the MeerLICHT telescope, can predict more accurate features with respect to similar codes such as SourceExtractor and that the two-step method can estimate well-calibrated uncertainties that are better behaved compared to similar methods that use deep ensembles of simple MVE networks. Finally, we evaluate the model on real images from the MeerLICHT telescope and the Zwicky Transient Facility (ZTF) to test its transfer learning abilities.

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