arXiv subjects

Cecile Caillol

Publications and source records attributed to Cecile Caillol.

4 recordsLinked to original sources

arXiv2026

Machine Can Automatically Discover Parametric Functions to Model HEP Data

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 can be automated by a machine using symbolic regression, which performs a data-driven search over function space without requiring prior knowledge of what an adequate function should look like. We present the SymbolFit package, which pairs symbolic regression with uncertainty modeling to target HEP analysis use cases, and demonstrate it on the CMS and ATLAS Run 2 dijet spectra: 560 independent seeded runs across seven simple fit configurations generated over 1000 functions fitting the spectra with $\chi^2/\text{NDF}\approx 1$, and 111 of the runs rediscovered the very dijet and UA2 functions used in published dijet searches.

hep-ex↗

arXiv2024

SymbolFit: Automatic Parametric Modeling with Symbolic Regression

We introduce SymbolFit, a framework that automates parametric modeling by using symbolic regression to perform a machine-search for functions that fit the data while simultaneously providing uncertainty estimates in a single run. Traditionally, constructing a parametric model to accurately describe binned data has been a manual and iterative process, requiring an adequate functional form to be determined before the fit can be performed. The main challenge arises when the appropriate functional forms cannot be derived from first principles, especially when there is no underlying true closed-form function for the distribution. In this work, we develop a framework that automates and streamlines the process by utilizing symbolic regression, a machine learning technique that explores a vast space of candidate functions without requiring a predefined functional form because the functional form itself is treated as a trainable parameter, making the process far more efficient and effortless than traditional regression methods. We demonstrate the framework in high-energy physics experiments at the CERN Large Hadron Collider (LHC) using five real proton-proton collision datasets from new physics searches, including background modeling in resonance searches for high-mass dijet, trijet, paired-dijet, diphoton, and dimuon events. We show that our framework can flexibly and efficiently generate a wide range of candidate functions that fit a nontrivial distribution well using a simple fit configuration that varies only by random seed, and that the same fit configuration, which defines a vast function space, can also be applied to distributions of different shapes, whereas achieving a comparable result with traditional methods would have required extensive manual effort.

hep-ex↗

arXiv2024

Electroweak results in two-photon collisions at the LHC

The LHC is also a high-energy photon collider. The ATLAS and CMS experiments are exploring a wide diphoton energy range with various approaches in heavy-ion and proton collisions to precisely measure quantum electrodynamics processes and constrain the existence of physics beyond the standard model.

hep-ph↗

arXiv2022

Charged Lepton Flavour Violation in Heavy Particle DEcays

Charged lepton flavor violation is an unambiguous signature for New Physics. Here we present a summary of the theoretical and experimental status of the search for charged lepton flavor violation in heavy particle decays, in particular in the decays of the Z and Higgs bosons, and of the top quark. Decays of beyond-Standard-Model particles such as a Z' or an additional scalar particle are also discussed. Finally the prospects for such searches at proposed future electron-positron colliders are reviewed.

hep-ph↗