相关论文: $\mathcal{CP}$-Analyses with Symbolic Regression

Conformal Prediction (CP) serves as a robust framework that quantifies uncertainty in predictions made by Machine Learning (ML) models. Unlike traditional point predictors, CP generates statistically valid prediction regions, also known as…

The ATLAS experiment at the LHC has measured the Higgs boson couplings and mass, and searched for invisible Higgs boson decays, using multiple production and decay channels with up to 4.7 fb$^{-1}$ of $pp$ collision data at $\sqrt{s}=7$ TeV…

Interpretability is crucial for machine learning in many scenarios such as quantitative finance, banking, healthcare, etc. Symbolic regression (SR) is a classic interpretable machine learning method by bridging X and Y using mathematical…

Identifying governing equations for a dynamical system is a topic of critical interest across an array of disciplines, from mathematics to engineering to biology. Machine learning -- specifically deep learning -- techniques have shown their…

Machine Learning (ML) techniques are rapidly finding a place among the methods of High Energy Physics data analysis. Different approaches are explored concerning how much effort should be put into building high-level variables based on…

Current experimental data on the 125 GeV Higgs boson still allow room for large CP violation. The observables usually considered in this context are triple product asymmetries, which require an input of four visible particles after imposing…

We investigate the potential of the $pp\to \gamma\gamma\gamma$ process to probe CP-conserving and CP-violating dimension-six operators of Higgs-gauge boson interactions in a model-independent Standard Model effective field theory framework…

We demonstrate how probes of CP-violating observables in Higgs di-tau decays at prospective future lepton colliders could provide a test of weak scale baryogenesis with significant discovery potential. Measurements at the Circular Electron…

We present results of an investigation to study CP violation in the Higgs sector in $t\bar t$ production at a $\gamma\gamma$-collider, via the process $ \gamgam \ra \phi \ra t \bar t$ where the $\phi$ is a scalar with indeterminate CP…

Symbolic regression (SR) aims to find symbolic expressions that describe datasets. Due to its inherent interpretability, is a powerful paradigm for scientific discovery. Recent advances have expanded SR to describe related phenomena using a…

The observation of resonances is unequivocal evidence of new physics beyond the Standard Model at the Large Hadron Collider (LHC). So far, inclusive and model dependent searches have not provided evidence of new resonances, indicating that…

We analyze the prospect for observing the intermediate neutral Higgs boson ($h_2$) in its decay to two lighter Higgs bosons ($h_1$) at the presently operating hadron colliders in the framework of the CP violating MSSM using the PYTHIA event…

In this paper we consider CP-violating new-physics contributions to the decay $t \to b \bar b c$. We examine the prospects for detecting such new physics at the LHC, which requires studying the process $gg \to t (\to b \bar b c) \bar t (\to…

The program HiggsSignals confronts the predictions of models with arbitrary Higgs sectors with the available Higgs signal rate and mass measurements, resulting in a likelihood estimate. A new version of the program, HiggsSignals-2, is…

Symbolic regression aims to find a function that best explains the relationship between independent variables and the objective value based on a given set of sample data. Genetic programming (GP) is usually considered as an appropriate…

We introduce a robust, interpretable machine learning (ML) framework that combines numerical regression for high-accuracy predictions with symbolic regression to uncover the underlying physics. This hybrid approach effciently derives…

In high-stakes scenarios, such as medical imaging applications, it is critical to equip the predictions of a regression model with reliable confidence intervals. Recently, Conformal Prediction (CP) has emerged as a powerful statistical…

In a regression task, a function is learned from labeled data to predict the labels at new data points. The goal is to achieve small prediction errors. In symbolic regression, the goal is more ambitious, namely, to learn an interpretable…

The detection of production of a pair of Higgs bosons before the end of LHC operation would be clear evidence of New Physics (NP). As searches for non-resonant production of Higgs pairs are being designed it is of particular importance to…

We perform a detailed hadron-level study on the sensitivity of Higgs boson pair production via the $WW^{*}WW^{*}$ channel with the final state $3 \ell 2 j$ + missing $E_T$ at the LHC with the collision energy $\sqrt{S} = 14$ TeV and a…