arXiv subjects

Mohanraj Senniappan

Publications and source records attributed to Mohanraj Senniappan.

3 recordsLinked to original sources

arXiv2023

tilepy: rapid tiling strategies in mid/small FoV observatories

The challenges inherent to time-domain multi-messenger astronomy require strategic actions so that adapted, optimized follow-up observations are performed efficiently. In particular, poorly localized events require dedicated tiling and/or targeted, follow-up campaigns so that the region in which the source really is can be efficiently covered, increasing the chances to detect the multi-wavelength counterpart. We have developed the python package "tilepy" to rapidly derive the observation scheduling of large uncertainty localization events by small/mid-FoV instruments. We will describe several mature follow-up scheduling strategies. These range from an option to use of low-resolution grids, to the full integration of sky regions and targeted observations using galaxy catalogs. The algorithms consider the visibility constraints of customisable observatories and allow to schedule observations in both astronomical darkness and in moonlight conditions. Developed initially to provide a rapid response to gravitational wave (GW) alerts by Imaging Atmospheric Cherenkov Telescopes (IACTs), they have been proven successful, as shown by the GW follow-up during O2 and O3 with the H.E.S.S. telescopes, and particularly in the follow-up of GW170817, the first binary neutron star (BNS) merger ever detected. Here we will present a generalisation of these rapid strategies to other alerts showing large uncertainties in the localization, like Gamma-Ray Burst (GRB) alerts from Fermi-GBM. We will also demonstrate the flexibility of {\it tilepy} in scheduling observations for a large variety of observatories. We will conclude by describing the latest developments of these algorithms that are able to derive optimised follow-up schedules across multiple observatories and networks of telescopes.

astro-ph.IM↗

arXiv2021

Studies of Gamma-Ray Shower Reconstruction Using Deep Learning

The Cosmic Multiperspective Event Tracker (CoMET) R&D project aims to optimize the techniques for the detection of soft-spectrum sources through very-high-energy gamma-ray observations using particle detectors (called ALTO detectors), and atmospheric Cherenkov light collectors (called CLiC detectors). The accurate reconstruction of the energies and maximum depths of gamma-ray events using a surface array only, is an especially challenging problem at low energies, and the focus of the project. In this contribution, we leverage Convolutional Neural Networks (CNNs) using the ALTO detectors only, to try to improve reconstruction performance at lower energies ( < 1 TeV ) as compared to the SEMLA analysis procedure, which is a more traditional method using manually derived features.

astro-ph.IM↗

arXiv2021

The CoMET multiperspective event tracker for wide field-of-view gamma-ray astronomy

The CoMET R&D project focuses on the development of a new technique for the observation of very high-energy (VHE) $γ$-rays from the ground at energies above ~200 GeV, thus covering emission from soft-spectrum sources. The CoMET array under study combines 1242 particle detector units, distributed over a circular area of ~160 m in diameter and placed at a very high altitude (5.1 km), with atmospheric Cherenkov light detectors. The atmospheric Cherenkov light detectors, inspired by the "HiSCORE" design and improved for the energy range of interest, can be operated together with the particle detectors during clear nights. As such, the instrument becomes a Cosmic Multiperspective Event Tracker (CoMET). CoMET is expected to improve the reconstruction of arrival direction, energy and shower maximum determination for $γ$-ray-induced showers during darkness, which is crucial for the reduction of background contamination from cosmic rays. Prototypes of both particle and atmospheric Cherenkov light detectors are already installed at Linnaeus University in Sweden, while in parallel we simulate the full detector response and estimate the reconstruction improvement for $γ$-ray events. In this contribution, we present Monte-Carlo simulations of the detector array, consisting of CORSIKA shower simulations and custom detector response simulations, together with the coupling of particle and atmospheric Cherenkov light information, the reconstruction strategy of the complete array and the detection performance on point-like VHE $γ$-ray sources.

astro-ph.IM↗