As new cutting-edge instruments are deployed, the traditional analytic tools used by astronomers start to buckle under the amount and velocity of the astronomical data collected. Tools adapted from the growing field of data science and involving high levels of automation, high-performance computing and artificial intelligence are becoming more vital than ever for processing, investigating and analyzing these increasingly large astronomical datasets.
Our group seeks to develop such tools. Some examples developed in recent years are available on GitHub.
catsHTM is a tool for fast accessing and cross-matching large astronomical catalogs, originally written in Matlab by Eran O. Ofek and later developped in Python by Maayane Soumagnac. The python version of the catsHTM code is available on GitHub. The Matlab version is available here. The package is described in details in Soumagnac & Ofek 2018. It has been extensively used by the community, e.g. as part of the AMPEL package or the ALERCE broker.
PhotoFit is a tool for calculating and visualising the evolution in time of the effective radius, temperature and luminosity of a supernova (or any target assumed to behave as a blackbody) from multiple-bands photometry. The package is described in details in Soumagnac et al. 2020.
The SLAB-Diffusion package is a tool for modeling the radiative diffusion of photons through a slab of circumstellar material (CSM), e.g. in order to simulate the observables (effective blackbody temperature and radius) of an interacting Supernova. The code is available on GitHub. The scientific motivations for SLAB-Diffusion and the package are described in details in Soumagnac et al. 2018.