Dr Fiorenzo Stoppa

Abstract:

We present SNID–SAGE (SuperNova IDentification–Spectral Analysis and Guided Exploration), a framework for supernova spectral classification with both a fully interactive graphical interface and a scriptable command-line pipeline for large-scale processing. The pipeline combines deterministic spectral pre-processing, FFT-based cross-correlation against a curated template library, ranking of candidate matches using a composite quality metric, and consolidation of redshift and classification solutions into a single result with associated quality and confidence estimates. SNID–SAGE includes an upgradeable template library (about 6000 spectra), interactive line identification with velocity measurements, and optional natural-language summaries of classification results. We evaluate SNID–SAGE using two complementary tests: (i) Leave-one-out cross-validation, in which each template spectrum is matched against the remainder of the library; and (ii) large-scale application to WISeREP spectra with valid coverage across the 4000–7000 Å interval, irrespective of spectral type, comprising approximately 46 000 spectra, with redshift validation against known host-galaxy measurements where available. The full validation results and the SNID–SAGE framework are publicly available, supporting integration into spectroscopic survey workflows.

Abstract:

We present ATLAS100 – a sample of 1729 supernovae and other explosive optical transients within ∼100 Mpc observed by the ATLAS survey over a span of 5.75 yr from 2017 September 21 to 2023 June 21. The volume-limited sample includes transients associated with galaxies with a spectroscopic redshift of , and spectroscopically classified transients within this redshift threshold where a host redshift was not available in existing catalogues. Our host galaxy list is constructed from aggregating all available galaxy redshift and distance catalogues. We carefully select all transients within a projected radius of 50 kpc of these hosts. The ATLAS100 transient sample has a host galaxy redshift completeness fraction of 83 per cent, consistent with expectations for the redshift completeness of local galaxy catalogues. Within this volume, the spectroscopic classifications are 87 per cent complete and we reclassify many ambiguous transients with joint light curve and spectroscopic considerations. Here, we release the catalogue together with compiled, binned, and cleaned ATLAS photometry for all transients. We fit the light curve data to derive peak luminosities and characteristic time-scales. We explore the sample characteristics, demographics, and discuss the completeness and purity of the sample. This is the first in a series of papers that will explore the rates and physical parameters of a complete and large sample of nearby supernovae and transients brighter than .

Abstract:

Context . Astronomical imaging aims to maximize signal capture while minimizing noise. It is difficult and expensive to enhance the signal-to-noise ratio directly on detectors, which has led to extensive research into advanced post-processing techniques. Aims . Removing background noise from images is a valuable preprocessing step for catalog-building tasks. We introduce BGRem, a machine-learning (ML)-based tool to remove background noise from astronomical images. Our aim is to improve image quality and enhance the performance of the subsequent analysis pipeline, from detecting faint sources to performing source characterization tasks. Methods . The BGRem tool uses a diffusion-based model with an attention U-Net as backbone, trained on simulated images for optical and gamma ( γ )-ray data from the MeerLICHT and Fermi-LAT telescopes. The tool learns to denoise astronomical images in a supervised manner over several diffusion steps. We performed preprocessing and postprocessing techniques, including normalization and median subtraction, on these images to make them suitable for the analysis pipeline. Results . We compared the performance of BGRem with SourceExtractor (SExtractor), a widely used tool for cataloging astronomical sources. The number of true positive sources using SExtractor increased by about 7% for MeerLICHT data when we used BGRem as a preprocessing step. We also show the generalizability of BGRem by testing it with optical images from different telescopes and on simulated γ -ray data representative of the Fermi-LAT telescope. In both cases, BGRem improves the source detection efficiency. Conclusions . The BGRem tool improves the source detection accuracy of traditional pixel-based methods by removing complex background noise. Using zero-shot approach, BGRem generalizes well to a wide range of optical images. The successful application of BGRem to simulated γ -ray images, alongside optical data, demonstrates its adaptability to distinct noise characteristics and observational domains. This cross-wavelength performance highlights its potential as a general-purpose background removal framework for multiwavelength astronomical surveys.

Abstract:

Context . Many applications in transient science, gravitational wave follow-up, and galaxy population studies require all-sky galaxy catalogs with reliable distances, extents, and stellar masses. However, existing catalogs often lack completeness beyond ~100 Mpc, suffer from stellar contamination, or do not provide homogeneous stellar mass estimates and size information. Aims . Our goal is to build a high-purity, high-completeness, all-sky galaxy catalog out to 2000 Mpc, specifically designed to support time-domain and multi-messenger astrophysics. Methods . We combined major galaxy catalogs and deep imaging surveys – including the Legacy Surveys, Pan-STARRS, DELVE, and SDSS – and added spectroscopic, photometric, and redshift-independent distances. We cleaned the sample using the Gaia catalog to remove stars and visually inspected all ambiguous cases below 100 Mpc through a classification platform that gathered 27 000 expert votes. Stellar masses were estimated using optical and mid-infrared profile-fit photometry, and we improved the accuracy of photometric distances by combining multiple independent estimates. Results . The resulting catalog, REGALADE, includes nearly 80 million galaxies with distances under 2000 Mpc. It provides stellar masses for 88% of the sample and ellipse fits for 80%. REGALADE is more than 90% complete for galaxies contributing 50% of the total r -band luminosity out to 360 Mpc. In science tests, it recovers 60% more known supernova hosts, doubles the number of low-luminosity transient hosts, and identifies more reliable hosts for ultraluminous and hyper-luminous X-ray sources. Conclusions . REGALADE is one of the most complete and reliable all-sky galaxy catalog to date for the nearby Universe, built for real-world applications in transient and multi-messenger astrophysics. The full dataset, visual classifications, and code will be released to support broad community use.

Abstract:

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 multiwavelength (gamma-ray) and multimessenger (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 ± 62 Mpc. Pan-STARRS tiled 286 and 318 deg2 (32% and 34% of the 90% sky localisation region) within 3 and 7 days of the GW signal, respectively. ATLAS covered 65% of the sky map 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 AT2025ulz, the purported counterpart to S250818k. The griz-band light curve, combined with our redshift measurement (z = 0.0849 ± 0.0003), all indicate that SN2025ulz is a type IIb supernova and thus not the counterpart to S250818k. We rule out the presence of an AT2017gfo-like kilonova within ≈27% of the distance posterior sampled by our Pan-STARRS pointings (≈9.1% across the total 90% 3D sky localisation). We demonstrate that early observations are optimal for probing the distance posterior of the 3D gravitational-wave sky map, and that SN2025ulz was a plausible kilonova candidate for ≲5 days, before ultimately being ruled out.