Dr Fiorenzo Stoppa

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.

Abstract:

Modern astronomical surveys deliver immense volumes of transient detections, yet distinguishing real astrophysical signals (for example, explosive events) from bogus imaging artefacts remains a challenge. Convolutional neural networks are effectively used for real versus bogus classification; however, their reliance on opaque latent representations hinders interpretability. Here we show that large language models (LLMs) can approach the performance level of a convolutional neural network on three optical transient survey datasets (Pan-STARRS, MeerLICHT and ATLAS) while simultaneously producing direct, human-readable descriptions for every candidate. Using only 15 examples and concise instructions, Google’s LLM, Gemini, achieves a 93% average accuracy across datasets that span a range of resolution and pixel scales. We also show that a second LLM can assess the coherence of the output of the first model, enabling iterative refinement by identifying problematic cases. This framework allows users to define the desired classification behaviour through natural language and examples, bypassing traditional training pipelines. Furthermore, by generating textual descriptions of observed features, LLMs enable users to query classifications as if navigating an annotated catalogue, rather than deciphering abstract latent spaces. As next-generation telescopes and surveys further increase the amount of data available, LLM-based classification could help bridge the gap between automated detection and transparent, human-level understanding.

Abstract:

Aims. 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. Methods. Our method, called ASTA (Automated Satellite Tracking for Astronomy), combined deep learning and computer vision techniques for effective satellite trail detection. It employed 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 of manually labeled full-field MeerLICHT telescope images prepared using the user-friendly LABKIT annotation tool. This approach ensured high-quality and precise annotations while facilitating quick and efficient data refinements, which streamlined the overall model development process. The thorough annotation process was crucial for the model to effectively learn the characteristics of satellite trails and generalize its detection capabilities to new, unseen data. Results. The U-Net performance was evaluated on a test set of 20 000 image patches, both with and without satellite trails, achieving approximately 0.94 precision and 0.94 recall at the selected threshold. For each detected satellite, ASTA demonstrated a high detection efficiency, recovering approximately 97% of the pixels in the trails, resulting in a False Negative Rate (FNR) of only 0.03. When applied to around 200 000 full-field MeerLICHT images focusing on Geostationary (GEO) and Geosynchronous (GES) satellites, ASTA identified 1742 trails −19.1% of the detected trails – that could not be matched to any objects in public satellite catalogs. This indicates the potential discovery of previously uncatalogued satellites or debris, confirming ASTA’s effectiveness in both identifying known satellites and uncovering new objects.

Abstract:

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 timescale 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 65 cm primary mirror, coupled with a 110Mpix CCD detector, that provides an instantaneous field-of-view of 2.7 square degrees, sampled at 0.″564 pixel⁻¹. 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 2019 October. After a prolonged COVID-19 hiatus, science operations started on 2023 April 1 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.

Abstract:

Massive stars are predominantly born in stellar associations or clusters¹. Their radiation fields, stellar winds and supernovae strongly impact their local environment. In the first few million years of a cluster's life, massive stars are dynamically ejected and run away from the cluster at high speed². However, the production rate of dynamically ejected runaways is poorly constrained. Here we report on a sample of 55 massive runaway stars ejected from the young cluster R136 in the Large Magellanic Cloud. An astrometric analysis of Gaia data^3-5 reveals two channels of dynamically ejected runaways. The first channel ejects massive stars in all directions and is consistent with dynamical interactions during and after the birth of R136. The second channel launches stars in a preferred direction and may be related to a cluster interaction. We found that 23-33% of the most luminous stars initially born in R136 are runaways. Model predictions^2,6,7 have significantly underestimated the dynamical escape fraction of massive stars. Consequently, their role in shaping and heating the interstellar and galactic media and their role in driving galactic outflows are far more important than previously thought^8,9.