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K. Garcia-Cifuentes

Publications and source records attributed to K. Garcia-Cifuentes.

2 recordsLinked to original sources

Understanding the Nature of the Optical Emission in Gamma-Ray Bursts: Analysis from TAROT, COATLI, and RATIR Observations

We collected the optical light curve data of 227 gamma-ray bursts (GRBs) observed with the TAROT, COATLI, and RATIR telescopes. These consist of 133 detections and 94 upper limits. We constructed average light curves in the observer and rest frames in both X-rays (from {\itshape Swift}/XRT) and in the optical. Our analysis focused on investigating the observational and intrinsic properties of GRBs. Specifically, we examined observational properties, such as the optical brightness function of the GRBs at $T=1000$ seconds after the trigger, as well as the temporal slope of the afterglow. We also estimated the redshift distribution for the GRBs within our sample. Of the 227 GRBs analysed, we found that 116 had a measured redshift. Based on these data, we calculated a local rate of $ρ_0=0.2$ Gpc$^{-3}$ yr$^{-1}$ for these events with $z<1$. To explore the intrinsic properties of GRBs, we examined the average X-ray and optical light curves in the rest frame. We use the {\scshape afterglowpy} library to generate synthetic curves to constrain the parameters typical of the bright GRB jet, such as energy (${\langle} {E_{0}}{\rangle}\sim 10^{53.6}$~erg), opening angle (${\langle}θ_\mathrm{core}{\rangle}\sim 0.2$~rad), and density (${\langle}n_\mathrm{0}{\rangle}\sim10^{-2.1}$ cm$^{-3}$). Furthermore, we analyse microphysical parameters, including the fraction of thermal energy in accelerated electrons (${\langle}ε_e{\rangle}\sim 10^{-1.37}$) and in the magnetic field (${\langle}ε_B{\rangle}\sim10^{-2.26}$), and the power-law index of the population of non-thermal electrons (${\langle}p{\rangle}\sim 2.2$).

astro-ph.HE↗

Identification of Extended Emission Gamma-Ray-Bursts Candidates using Machine Learning

Gamma-Ray bursts (GRBs) have been traditionally classified based on their duration. The increasing number of extended emission (EE) GRBs, lasting typically more than 2 seconds but with properties similar to those of a short GRBs, challenges the traditional classification criteria. In this work, we use the t-Distributed Stochastic Neighbor Embedding (t-SNE), a machine learning technique, to classify GRBs. We present the results for GRBs observed until July 2022 by the Swift/BAT instrument in all its energy bands. We show the effects of varying the learning rate and perplexity parameters as well as the benefit of pre-processing the data by a non-parametric noise reduction technique FABADA. Consistently with previous works, we show that the t-SNE method separates GRBs in two subgroups. We also show that EE GRBs reported by various authors under different criteria tend to cluster in a few regions of our t-SNE maps, and identify seven new EE GRB candidates by using the gamma-ray data provided by the automatic pipeline of Swift/BAT and the proximity with previously identified EE GRBs.

astro-ph.HE↗