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

TL;DR

This paper presents a simulation framework for timing-based localization of gamma-ray bursts using a fleet of Cubesats, demonstrating degree-scale accuracy and exploring machine learning for automation.

Contribution

It introduces a comprehensive simulation framework for all-sky gamma-ray localization with Cubesats and applies machine learning to automate the process.

Findings

Degree-scale localization for bright short gamma-ray bursts.

Machine learning achieves comparable accuracy to traditional methods.

Simulation framework incorporates orbital parameters and statistical localization methods.

Abstract

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.