Statistical Characterization and Classification of Astronomical Transients with Machine Learning in the era of the Vera C. Rubin Observatory

Astronomy has entered the multi-messenger data era and Machine Learning has found widespread use in a large variety of applications. The exploitation of synoptic (multi-band and multi-epoch) surveys, like LSST (Legacy Survey of Space and Time), requires an extensive use of automatic methods for data processing and interpretation. With data volumes in the petabyte domain, the discrimination of time-critical information has already exceeded the capabilities of human operators and crowds of scientists have extreme difficulty to manage such amounts of data in multi-dimensional domains. This work is focused on an analysis of critical aspects related to the approach, based on Machine Learning, to variable sky sources classification, with special care to the various types of Supernovae, one of the most important subjects of Time Domain Astronomy, due to their crucial role in Cosmology. The work is based on a test campaign performed on simulated data. The classification was carried out by comparing the performances among several Machine Learning algorithms on statistical parameters extracted from the light curves. The results make in evidence some critical aspects related to the data quality and their parameter space characterization, propaedeutic to the preparation of processing machinery for the real data exploitation in the incoming decade

Genotype-Phenotype Correlation in a Family with Brugada Syndrome Harboring the Novel p.Gln371* Nonsense Variant in the SCN5A Gene

Brugada syndrome (BrS) is marked by coved ST-segment elevation and increased risk of sudden cardiac death. The genetics of this syndrome are elusive in over half of the cases. Variants in the SCN5A gene are the single most common known genetic unifier, accounting for about a third of cases. Research models, such as animal models and cell lines, are limited. In the present study, we report the novel NM_198056.2:c.1111C>T (p.Gln371*) heterozygous variant in the SCN5A gene, as well as its segregation with BrS in a large family. The results herein suggest a pathogenic effect of this variant. Functional studies are certainly warranted to characterize the molecular effects of this variant