Searcharxiv⌕ Search

arXiv subjects

S. V. Chekanov

Publications and source records attributed to S. V. Chekanov.

At least 19 recordsLinked to original sources
arXiv2026

On the Statistical Interpretation of Discoveries in LHC Data

We examine discovery criteria at the Large Hadron Collider (LHC) within a model-independent framework, with particular emphasis on the statistical signatures of new physics. This study is motivated by the recent shift from model-specific searches based on a small number of distributions to broad, model-agnostic strategies, which offer substantially greater sensitivity to unexpected phenomena. We revisit the well-known criterion of a local statistical significance of $5\,σ$ for the observation of new phenomena in invariant-mass distributions and discuss how this threshold should be modified to account for look-elsewhere effects arising not only from multiple bins within a given distribution, but also from the simultaneous consideration of multiple distributions. We present a simple but statistically conservative relation between local and global significances in the presence of multiple invariant-mass distributions at the LHC, which can serve as a useful first approximation for planning future measurements.

physics.data-an↗
arXiv2025

Discovering the Underlying Analytic Structure Within Standard Model Constants Using Artificial Intelligence

This paper presents a method for uncovering hidden analytic relationships among the fundamental parameters of the Standard Model (SM), a foundational theory in physics that describes the fundamental particles and their interactions, using symbolic regression and genetic programming. Using this approach, we identify the simplest analytic relationships connecting pairs of these constants and report several notable expressions obtained with relative precision better than 1%. These results may serve as valuable inputs for model builders and artificial intelligence methods aimed at uncovering hidden patterns among the SM constants, or potentially used as building blocks for a deeper underlying law that connects all parameters of the SM through a small set of fundamental constants.

hep-ph↗
arXiv2025

Evidence of Relationships Among Fundamental Constants of the Standard Model

This paper presents an approach to reducing the number of fundamental parameters in the Standard Model (SM) using genetic programming, a machine learning technique based on evolutionary algorithms. We outline the core principles of our method and identify the simplest analytic relationships among SM parameters. Our results suggest that the SM parameters associated with quark and boson masses are not randomly distributed, but instead follow a hierarchical structure within a high-dimensional functional space. The found analytic solution depends on only two input parameters, representing the simplest mathematical model that could provide a foundation for developing a future theoretical framework to address the SM.

hep-ph↗
arXiv2025

On the resolution of dual readout calorimeters

Dual readout calorimeters allow state-of-the-art resolutions for hadronic energy measurements. Their various incarnations are leading candidates for the calorimeter systems for future colliders. In this paper, we present a simple formula for the resolution of a dual readout calorimeter, which we verify with a toy simulation and with full simulation results. This formula can help those new to dual readout calorimetry understand its strengths and limitations. The paper also highlights that the dual readout correction works not just to compensate for binding energy loss, but also for energies escaping the calorimeter or clustering algorithm. Formulae are also presented for approximate resolutions and energy scales in terms of different sources of response.

physics.ins-det↗
arXiv2024

Estimation of the chances to find new phenomena at the LHC in a model-agnostic combinatorial analysis

In this paper, we estimate the number of event topologies that have the potential to be produced in $pp$ collisions at the Large Hadron Collider (LHC) without violating kinematic and other constraints. We use numerical calculations and combinatorics, guided by large-scale Monte Carlo simulations of Standard Model (SM) processes. Then, we set the upper limit on the probability that new physics may escape detection, assuming a model-agnostic approach. The calculated probability is unexpectedly large, and the fact that the LHC has not found new physics until now is not entirely surprising. We argue that the optimal direction for maximizing the chances of finding new physics is to use unsupervised machine learning for anomaly detection or algorithms designed for event classification.

hep-ph↗
arXiv2023

Geant4 simulations of sampling and homogeneous hadronic calorimeters with dual readout for future colliders

Hadronic calorimeters with dual readout measure both scintillation and Cherenkov lights produced in their active media. They offer improvements in energy resolution and, therefore, have become increasingly interesting due to the need for precision jet measurements at Higgs factories. This paper presents Geant4 simulations of single-particle responses in sampling and homogeneous calorimeters, and demonstrates the effect of inclusion of Cherenkov light in the reconstruction of energies. The simulations are performed with a single-photon precision.

physics.ins-det↗
arXiv2023

Muon Collider Forum Report

A multi-TeV muon collider offers a spectacular opportunity in the direct exploration of the energy frontier. Offering a combination of unprecedented energy collisions in a comparatively clean leptonic environment, a high energy muon collider has the unique potential to provide both precision measurements and the highest energy reach in one machine that cannot be paralleled by any currently available technology. The topic generated a lot of excitement in Snowmass meetings and continues to attract a large number of supporters, including many from the early career community. In light of this very strong interest within the US particle physics community, Snowmass Energy, Theory and Accelerator Frontiers created a cross-frontier Muon Collider Forum in November of 2020. The Forum has been meeting on a monthly basis and organized several topical workshops dedicated to physics, accelerator technology, and detector R&D. Findings of the Forum are summarized in this report.

hep-ex↗
arXiv2022

Snowmass 2021 White Paper: Electron Ion Collider for High Energy Physics

Electron Ion Collider (EIC) is a particle accelerator facility planned for construction at Brookhaven National Laboratory on Long Island, New York by the United States Department of Energy. EIC will provide capabilities of colliding beams of polarized electrons with polarized beams of proton and light ions. EIC will be one of the largest and most sophisticated new accelerator facilities worldwide, and the only new large-scale accelerator facility planned for construction in the United States in the next few decades. The versatility, resolving power and intensity of EIC will present many new opportunities to address some of the crucial and fundamental open scientific questions in particle physics. This document provides an overview of the science case of EIC from the perspective of the high energy physics community.

hep-ph↗
arXiv2022

Event-based anomaly detection for new physics searches at the LHC using machine learning

This paper discusses model-agnostic searches for new physics at the Large Hadron Collider (LHC) using anomaly-detection techniques for the identification of event signatures that deviate from the Standard Model (SM). We investigate anomaly detection in the context of machine-learning approaches using autoencoders, and illustrate expected shapes of invariant masses in the outlier region using Monte Carlo simulations. Challenges and conceptual limitations of this approach are discussed.

hep-ph↗
arXiv2022

Precision timing for collider-experiment-based calorimetry

In this White Paper for the 2021 Snowmass process, we discuss aspects of precision timing within electromagnetic and hadronic calorimeter systems for high-energy physics collider experiments. Areas of applications include particle identification, event and object reconstruction, and pileup mitigation. Two different system options are considered, namely cell-level timing capabilities covering the full detector volume, and dedicated timing layers integrated in calorimeter systems. A selection of technologies for the different approaches is also discussed.

physics.ins-det↗
arXiv2022

Some aspects of impact of the Electron Ion Collider on particle physics at the Energy Frontier

This overview describes several science cases at the Electron-Ion-Collider (EIC) experiment which are traditional to general particle physics. It has an emphasis on connections between future measurements at the EIC and the physics topics explored at high-energy frontier colliders. It covers several selected topics, such as parton density functions, multi-quarks states, correlations of final-state particles, precision QCD measurements and forward jet physics. We will discuss possible EIC measurements that can improve previous experimental results obtained at high-energy (HEP) experiments.

hep-ph↗
arXiv2021

A note on blind technique for new physics searches in particle physics

This paper attempts to classify various blinding strategies used in particle physics. It argues that the blinding technique is not used consistently throughout searches for new physics. More importantly, the blinding technique, in its traditional sense, cannot be applicable for many current and future searches when statistical precision of data significantly exceeds the current level of our understanding of Standard Model (SM) backgrounds.

hep-ex↗
arXiv2021

Model-independent searches for new physics in multi-body invariant masses

Model-independent searches for physics beyond the Standard Model typically focus on invariant masses of two objects (jets, leptons or photons). In this study we explore opportunities for similar model-agnostic searches in multi-body invariant masses. In particular, we focus on the situations when new physics can be observed in a model-independent way in three- and four-body invariant masses of jets and leptons. Such searches may have good prospects in finding new physics in the situations when two-body invariant masses, that have been extensively explored at collider experiments in the past, cannot provide sufficient signatures for experimental observations.

hep-ph↗
arXiv2021

Machine learning using rapidity-mass matrices for event classification problems in HEP

Supervised artificial neural networks with the rapidity-mass matrix (RMM) inputs were studied using several Monte Carlo event samples for various pp collision processes. The study shows the usability of this approach for general event classification problems. The proposed standardization of the ANN feature space can simplify searches for signatures of new physics at the LHC when using machine learning techniques. In particular, we illustrate how to improve signal-over-background ratios in searches for new physics, how to filter out Standard Model events for model-agnostic searches, and how to separate gluon and quark jets for Standard Model measurements.

hep-ph↗
arXiv2021

Jas4pp -- a Data-Analysis Framework for Physics and Detector Studies

This paper describes the Jas4pp framework for exploring physics cases and for detector-performance studies of future particle collision experiments. Jas4pp is a multi-platform Java program for numeric calculations, scientific visualization in 2D and 3D, storing data in various file formats and displaying collision events and detector geometries. It also includes complex data-analysis algorithms for function minimisation, regression analysis, event reconstruction (such as jet reconstruction), limit settings and other libraries widely used in particle physics. The framework can be used with several scripting languages, such as Python/Jython, Groovy and JShell. Several benchmark tests discussed in the paper illustrate significant improvements in the performance of the Groovy and JShell scripting languages compared to the standard Python implementation in C. The improvements for numeric computations in Java are attributed to recent enhancements in the Java Virtual Machine.

physics.comp-ph↗
arXiv2020

Physics potential of timing layers in future collider detectors

The physics potential of timing layers with a few tens of pico-second resolution in the calorimeters of future collider detectors is explored. These studies show how such layers can be used for particle identification and illustrate the potential for detecting new event signatures originating from physics beyond the standard model.

physics.ins-det↗
arXiv2020

Automated detector simulation and reconstruction parametrization using machine learning

Rapidly applying the effects of detector response to physics objects (e.g. electrons, muons, showers of particles) is essential in high energy physics. Currently available tools for the transformation from truth-level physics objects to reconstructed detector-level physics objects involve manually defining resolution functions. These resolution functions are typically derived in bins of variables that are correlated with the resolution (e.g. pseudorapidity and transverse momentum). This process is time consuming, requires manual updates when detector conditions change, and can miss important correlations. Machine learning offers a way to automate the process of building these truth-to-reconstructed object transformations and can capture complex correlation for any given set of input variables. Such machine learning algorithms, with sufficient optimization, could have a wide range of applications: improving phenomenological studies by using a better detector representation, allowing for more efficient production of Geant4 simulation by only simulating events within an interesting part of phase space, and studies on future experimental sensitivity to new physics.

physics.data-an↗