arXiv subjects

Martin Topinka

Publications and source records attributed to Martin Topinka.

22 records · Page 2Linked to original sources

arXiv2022

GRB 190919B: Rapid optical rise explained as a flaring activity

Following the detection of a long GRB 190919B by INTEGRAL (INTErnational Gamma-Ray Astrophysics Laboratory), we obtained an optical photometric sequence of its optical counterpart. The light curve of the optical emission exhibits an unusually steep rise ~100 s after the initial trigger. This behaviour is not expected from a 'canonical' GRB optical afterglow. As an explanation, we propose a scenario consisting of two superimposed flares: an optical flare originating from the inner engine activity followed by the hydrodynamic peak of an external shock. The inner-engine nature of the first pulse is supported by a marginal detection of flux in hard X-rays. The second pulse eventually concludes in a slow constant decay, which, as we show, follows the closure relations for a slow cooling plasma expanding into the constant interstellar medium and can be seen as an optical afterglow sensu stricto.

astro-ph.HE↗

arXiv2021

INTEGRAL limits on past high-energy activity from FRB 20200120E in M81

The repeating fast radio burst FRB 20200120E is located in a globular cluster belonging to the nearby M81 galaxy. Its small distance (3.6 Mpc) and accurate localization make it an interesting target to search for bursting activity at high energies. From November 2003 to September 2021, the INTEGRAL satellite has obtained an exposure time of 18 Ms on the M81 sky region. We used these data to search for hard X-ray bursts from FRB 20200120E using the IBIS/ISGRI instrument, without finding any significant candidate, down to an average fluence limit of $\sim10^{-8}$ erg cm$^{-2}$ (20-200 keV). The corresponding limit on the isotropic luminosity for a burst of duration $Δt$ is $\sim10^{45} \left ( \frac{10~ms}{Δt} \right )$ erg s$^{-1}$, the deepest limit obtained for an extragalactic FRB in the hard X-ray range. This rules out the emission of powerful flares at a rate higher than 0.1 yr$^{-1}$ that could be expected in models invoking young hyper-active magnetars.

astro-ph.HE↗

arXiv2021

The simulation framework of the timing-based localization for future all-sky gamma-ray observations with a fleet of Cubesats

The timing-based localization, which utilize the triangulation principle with the different arrival time of gamma-ray photons, with a fleet of Cubesats is a unique and powerful solution for the future all-sky gamma-ray observation, which is a key for identification of the electromagnetic counterpart of the gravitational wave sources. The Cubesats Applied for MEasuring and Localising Transients (CAMELOT) mission is now being promoted by the Hungarian and Japanese collaboration with a basic concept of the nine Cubesats constellations in low earth orbit. The simulation framework for estimation of the localization capability has been developed including orbital parameters, an algorithm to estimate the expected observed profile of gamma-ray photons, finding the peak of the cross-correlation function, and a statistical method to find a best-fit position and its uncertainty. It is revealed that a degree-scale localization uncertainty can be achieved by the CAMELOT mission concept for bright short gamma-ray bursts, which could be covered by future large field of view ground-based telescopes. The new approach utilizing machine-learning approach is also investigated to make the procedure automated for the future large scale constellations. The trained neural network with 10$^6$ simulated light curves generated by the artificial short burst templates successfully predicts the time-delay of the real light curve and achieves a comparable performance to the cross-correlation algorithm with full automated procedures.

astro-ph.IM↗

arXiv2015

Machine Learning Search for Gamma-Ray Burst Afterglows in Optical Surveys

Thanks to the advances in robotic telescopes, the time domain astronomy leads to a large number of transient events detected in images every night. Data mining and machine learning tools used for object classification are presented. The goal is to automatically classify transient events for both further follow-up by a larger telescope and for statistical studies of transient events. A special attention is given to the identification of gamma-ray burst afterglows. Machine learning techniques is used to identify GROND gamma-ray burst afterglow among the astrophysical objects present in the SDSS archival images based on the $g'-r'$, $r'-i'$ and $i'-z'$ colour indices. The performance of the support vector machine, random forest and neural network algorithms is compared. A joint meta-classifier, built on top of the individual classifiers, can identify GRB afterglows with the overall accuracy of $\gtrsim 90\%$.

astro-ph.IM↗