# Astrophysics and Big Data: Challenges, Methods, and Tools

## TL;DR

This paper discusses the challenges posed by the increasing volume, variety, and velocity of astrophysical data, and explores machine learning methods and architectures for effective big data analysis in astrophysics.

## Contribution

It reviews current challenges and proposes solutions involving machine learning and big data tools tailored for astrophysical data analysis.

## Key findings

- Machine learning models can improve data processing efficiency.
- Advanced architectures are essential for handling petabyte-scale data.
- New tools are needed for complex, high-velocity astrophysical data.

## Abstract

Nowadays there is no field research which is not flooded with data. Among the sciences, Astrophysics has always been driven by the analysis of massive amounts of data. The development of new and more sophisticated observation facilities, both ground-based and spaceborne, has led data more and more complex (Variety), an exponential growth of both data Volume (i.e., in the order of petabytes), and Velocity in terms of production and transmission. Therefore, new and advanced processing solutions will be needed to process this huge amount of data. We investigate some of these solutions, based on machine learning models as well as tools and architectures for Big Data analysis that can be exploited in the astrophysical context.