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Symbolic regression automates function discovery for high energy physics data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method using symbolic regression to automatically discover parametric functions for modeling high energy physics (HEP) data. This approach automates the previously manual and intuitive process of finding suitable functions for data analysis. The SymbolFit package, designed for HEP use cases, was demonstrated on CMS and ATLAS Run 2 dijet spectra, successfully generating numerous functions that fit the data well and even rediscovering functions used in published searches. AI

IMPACT Automates complex data modeling in scientific research, potentially accelerating discovery in fields like high energy physics.

RANK_REASON The cluster describes a new methodology and software package presented in an arXiv paper for scientific data analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Symbolic regression automates function discovery for high energy physics data

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The cluster describes a new methodology and software package presented in an arXiv paper for scientific data analysis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ho Fung Tsoi, Dylan Rankin, Cecile Caillol, Miles Cranmer, Sridhara Dasu, Javier Duarte, Philip Harris, Elliot Lipeles ·

    Machine Can Automatically Discover Parametric Functions to Model HEP Data

    arXiv:2607.19750v1 Announce Type: cross Abstract: In HEP data analyses, finding an adequate function to model binned data has largely relied on a manual process: guess a functional form by intuition, fit, examine, then repeat until successful. We show that this iterative process …

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