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Alina Volnova

Publications and source records attributed to Alina Volnova.

2 recordsLinked to original sources

Deciphering the Physical Origin of GRB 240825A: A Long GRB Lacking a Bright Supernova

We present a comprehensive multiwavelength analysis of GRB 240825A, a bright gamma-ray burst (GRB) detected by Fermi and Swift, with a prompt duration ($T_{\rm 90}$ $\sim$ 4 sec in 50-300 keV in GBM) near the boundary separating short and long GRBs, prompting a detailed investigation into its classification and progenitor. We use classical prompt metrics (duration, minimum variability timescale (MVT), lag, and spectral hardness) and modern classification techniques (machine-learning (ML) based t-SNE, support vector machine, energy-hardness-duration, and $\varepsilon \equiv E_{γ,\mathrm{iso},52} / E_{p,z,2}^{5/3}$) and find most properties (prompt energetics, placement on the Amati relation, and spectral lag) of GRB 240825A consistent with a collapsar origin. However, extensive late-time optical and NIR follow-up with the 10.4m GTC and 8.4m binocular LBT telescopes reveals no bright supernova (like SN 1998bw) is detected down to stringent limits (e.g., $m_r > 25.0$ mag at 17.59 days), despite a redshift of $z = 0.659$ measured from GTC spectroscopy. Host galaxy SED modeling with Prospector indicates a massive, and star-forming galaxy-typical of collapsar GRB hosts, though with a large offset. We compare these findings with hybrid events like GRB 211211A, GRB 230307A, GRB 200826A, including SNe-GRBs, and conclude that GRB 240825A most likely originated from a massive star collapse, possibly with the associated SN heavily obscured or intrinsically faint. This study emphasizes the need for multiwavelength follow-up and a multi-layered classification to determine GRB progenitors.

astro-ph.HE

SNAD: enabling discovery in the era of big data

In the era of wide-field surveys and big data in astronomy, the SNAD team is exploiting the potential of modern datasets for discovering new, unforeseen, or rare astrophysical objects and phenomena with machine learning (ML). The SNAD pipeline was built under the hypothesis that, although automatic ML algorithms have a crucial role to play in this task, the scientific discovery is only completely realized when such systems are designed to boost the impact of domain knowledge experts. Our key contributions include the development of the Coniferest Python library, which offers implementations of two active learning algorithms with an ``expert in loop'', and the creation of the SNAD Transient Miner, facilitating the search for specific types of transients. We have also developed the SNAD Viewer, a web portal that provides a centralized view of individual objects from the Zwicky Transient Facility's (ZTF) data releases, making the analysis of potential anomalies more efficient. Finally, when applied to ZTF data, our approach has resulted in more than a hundred new supernova (SN) candidates, along with a few other non-catalogued objects, such as red dwarf flares, superluminous SNe, RS CVn type variables, and young stellar objects.

astro-ph.HE