arXiv subjects

G. S. Narayan

Publications and source records attributed to G. S. Narayan.

2 recordsLinked to original sources

arXiv2026

Probing a new subclass of llGRB-SN transients: Insights from EP250304a and its associated supernova

With the advent of the Einstein Probe (EP) mission, we are entering a new era in the study of gamma-ray bursts (GRBs), enabling the detection of faint, low-luminosity transients that would previously have gone undetected. EP250304a was an event discovered by EP associated with the broad-lined type Ic supernova (SN) SN 2025fhm located at z = 0.2. Despite no gamma-ray emission being detected at the time of the EP trigger, we identify evidence for a relativistic outflow consistent with a GRB-like jet across multiple wavelengths. We present a detailed spectral and photometric analysis of EP250304a/SN 2025fhm, including multi-band light curve modelling performed with the Redback Python package. We find that this event closely resembles low-luminosity GRB-SNe (llGRB-SNe) such as GRB 060218/SN 2006aj, GRB 100316D/SN 2010bh, and GRB 171205A/SN 2017iuk, all of which exhibit early-time emission consistent with a thermal shocked cocoon. These similarities suggest that EP250304A/SN 2025fhm may belong to an emerging subclass of shocked cocoon-dominated llGRB-SNe, representing the low-luminosity end of a broader continuum of engine-driven GRB-SN explosions.

astro-ph.HE↗

arXiv2022

SNAD Transient Miner: Finding Missed Transient Events in ZTF DR4 using k-D trees

We report the automatic detection of 11 transients (7 possible supernovae and 4 active galactic nuclei candidates) within the Zwicky Transient Facility fourth data release (ZTF DR4), all of them observed in 2018 and absent from public catalogs. Among these, three were not part of the ZTF alert stream. Our transient mining strategy employs 41 physically motivated features extracted from both real light curves and four simulated light curve models (SN Ia, SN II, TDE, SLSN-I). These features are input to a k-D tree algorithm, from which we calculate the 15 nearest neighbors. After pre-processing and selection cuts, our dataset contained approximately a million objects among which we visually inspected the 105 closest neighbors from seven of our brightest, most well-sampled simulations, comprising 89 unique ZTF DR4 sources. Our result illustrates the potential of coherently incorporating domain knowledge and automatic learning algorithms, which is one of the guiding principles directing the SNAD team. It also demonstrates that the ZTF DR is a suitable testing ground for data mining algorithms aiming to prepare for the next generation of astronomical data.

astro-ph.IM↗