LECX: a cubesat experiment to detect and localize cosmic explosions in hard X-rays

ABSTRACT With the advent of the nanosat/cubesat revolution, new opportunities have appeared to develop and launch small (∼1000 cm3), low-cost (∼US$ 1M) experiments in space in very short time frames (∼2 yr). In the field of high-energy astrophysics, in particular, it is a considerable challenge to design instruments with compelling science and competitive capabilities that can fit in very small satellite buses, such as a cubesat platform, and operate them with very limited resources. Here, we describe a hard X-ray (30–200 keV) experiment, LECX (‘Localizador de Explosões Cósmicas de Raios X’ – Locator of X-Ray Cosmic Explosions), that is capable of detecting and localizing within a few degrees events like gamma-ray bursts and other explosive phenomena in a 2U-cubesat platform, at a rate of ∼5 events per year. In the current gravitational wave era of astronomy, a constellation or swarm of small spacecraft carrying instruments such as LECX can be a very cost-effective way to search for electromagnetic counterparts of gravitational wave events produced by the coalescence of compact objects.

The High-Cadence Transient Survey (HiTS): Early Supernova Light-Curves

Supernovae (SNe) are cosmic explosions which are usually represented in a small region of the luminosity–time-scale diagram when discussing the variable sky. However, there are different time-scales involved in the evolution of SNe that are not reflected by that representation. This talk reviewed some of the physical mechanisms driving the SN light-curve diversity, especially at early times. It then discussed our efforts in the astroinformatics laboratory at CMM and at MAS to discover very young SNe using large etendue telescopes such as Blanco/DECam; those efforts led to the real-time discovery of more than one hundred SNe, some of them very young, under the High cadence Transient Survey (HiTS). We showed that, by comparing hydrodynamical models in the literature with HiTS SNe using Markov Chain Monte Carlo to sample from the posterior in a Bayesian approach, we can constrain the physical parameters that are driving the early time-evolution of these events. We also discussed how these data are being used for different projects, such as the discovery of asteroids and variable stars, and for testing different machine-learning algorithms in an interdisciplinary approach.