What is it about?
In high-energy physics, the so-called Tsallis function is widely used to describe the transverse-momentum distributions of charged hadrons. We discover, using machine learning, a function that closely resembles the Tsallis distribution from experimentally measured data at CERN experiments.
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Why is it important?
Symbolic regression was introduced in 1970 as a tool for automating scientific discovery. However, its application in sciences, including physical sciences, is limited. This application shows that it can successfully learn physical laws in a purely data-driven manner. It also shows that prior knowledge of the data is important to deliver meaningful results.
Perspectives
This paper represents a successful application of symbolic regression and thus invites physicists and natural scientists to use symbolic regression to boost scientific discovery.
Nour Makke
Read the Original
This page is a summary of: Data-driven discovery of Tsallis-like distribution using symbolic regression in high-energy physics, PNAS Nexus, October 2024, Oxford University Press (OUP),
DOI: 10.1093/pnasnexus/pgae467.
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