Astrostatistics, Astroinformatics and Big Data Analysis in Modern Astronomy Surveys

ABSTRACT

Astronomy in modern times largely depends on huge amount of data. Modern and planned astronomy surveys like EMU-ASKAP, WODAN, LOFAR, SKA related surveys and others are going to provide some interesting challenges for our existing statistical, computing and big data analysis techniques.

In this study, we are going to discuss statistical issues like power law distributions, mapping, shot noise, confusion, and signal-to-noise ratio analysis for upcoming continuum surveys. We will also discuss the applications of Al & machine learning techniques in analyzing large image and catalog data for statistical redshift moments, identifying source types, and even the possibility of discovering the previously unknown phenomenon. We will also discuss how the parallel computing is going to help us in tackling the problems related to the theoretical simulations for astronomical studies and analyzing the astronomical data.

Keywords: Astronomy; Machine Learning; Statistical Analysis; Artificial Intelligence; Parallel Computing