arXiv subjects

Sergei Chekanov

Publications and source records attributed to Sergei Chekanov.

7 recordsLinked to original sources

arXiv2026

Compact Representation of Particle-Collision Events for Physics-Informed Machine Learning

We introduce a compact, physics-driven event representation, RMM-C46, designed to compress the high-dimensional rapidity mass matrix (RMM) into a low-dimensional, interpretable feature set suitable for physics-informed machine learning (ML) and quantum computing applications. The full RMM encodes detailed pairwise correlations among jets, b-jets, leptons, photons, and missing transverse energy but contains more than a thousand values per event, making it computationally heavy for large-scale training and incompatible with current low-qubit quantum devices. The proposed RMM-C46 input space for ML preserves the physical block structure of the RMM through aggregated invariant mass, rapidity difference, and transverse energy components, reducing the size of the original RMM by over an order of magnitude while maintaining interpretability. Applied to simulated proton-proton collisions at centre-of-mass energy of 13.6 TeV, these representations match or exceed the discriminative performance of the full RMM in both supervised and unsupervised ML tasks. Their compactness, stability, and physics transparency also make them naturally compatible with near-term quantum machine learning architectures. RMM-C46 provides a scalable, efficient, and quantum-ready alternative to the full RMM for next-generation collider physics analyses.

hep-ph↗

arXiv2026

Building an AI-native Research Ecosystem for Experimental Particle Physics: A Community Vision

Experimental particle physics seeks to understand the universe by probing its fundamental particles and forces and exploring how they govern the large-scale processes that shape cosmic evolution. This whitepaper presents a vision for how Artificial Intelligence (AI) can accelerate discovery in this field. We outline grand challenges that must be addressed to enable transformative breakthroughs and describe how current and planned experimental facilities can implement this vision to advance our understanding of the vast and complex physical world from the smallest to the largest scales. We show how facilities currently under construction, such as the HL-LHC, DUNE and soon EIC, can both benefit from and serve as proving grounds for this vision, while also enabling a longer-term goal for how future experiments -- like FCC-ee at CERN, IceCube-Gen2, a Muon Collider in the U.S., and smaller to mid-scale projects -- can be fully AI-native. We describe how a truly national-scale collaboration, jointly managed across large funding partners, and involving both DOE laboratories and universities, can make this happen.

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arXiv2025

New Physics Searches at the LHC through Event-based Anomaly Detection and Development of ADFilter Web-tool

This work presents advancements in model-agnostic searches for new physics at the Large Hadron Collider (LHC) through the application of event-based anomaly detection techniques utilizing unsupervised machine learning. We discuss the advantages of the anomaly detection approach, as demonstrated in a recent ATLAS analysis, and introduce ADFilter, a web-based tool designed to process collision events using autoencoders based on deep unsupervised neural networks. ADFilter calculates loss distributions for input events, aiding in determining the degree to which events can be considered anomalous. Real-life examples are provided to demonstrate how the tool can be used to reinterpret existing LHC results, with the goal of significantly improving exclusion limits. Furthermore, we present a comparative study between anomaly detection and supervised machine learning techniques, using the search for heavy resonances decaying into two or more Higgs bosons as a representative case to demonstrate the application and effectiveness of these methods.

hep-ph↗

arXiv2018

Quantum Sensing for High Energy Physics

Report of the first workshop to identify approaches and techniques in the domain of quantum sensing that can be utilized by future High Energy Physics applications to further the scientific goals of High Energy Physics.

hep-ex↗

arXiv2016

Machine learning techniques in searches for $t\bar{t}h$ in the $h \rightarrow b\bar{b}$ decay channel

Study of the production of pairs of top quarks in association with a Higgs boson is one of the primary goals of the Large Hadron Collider over the next decade, as measurements of this process may help us to understand whether the uniquely large mass of the top quark plays a special role in electroweak symmetry breaking. Higgs bosons decay predominantly to \bbbar, yielding signatures for the signal that are similar to $t\bar{t}$ + jets with heavy flavor. Though particularly challenging to study due to the similar kinematics between signal and background events, such final states ($t\bar{t} b \bar{b}$) are an important channel for studying the top quark Yukawa coupling. This paper presents a systematic study of machine learning (ML) methods for detecting $t\bar{t}h$ in the $h \rightarrow b\bar{b}$ decay channel. Among the eight ML methods tested, we show that two models, extreme gradient boosted trees and neural network models, outperform alternative methods. We further study the effectiveness of ML algorithms by investigating the impact of feature set and data size, as well as the structure of the models. While extended feature set and larger training sets expectedly lead to improvement of performance, shallow models deliver comparable or better performance than their deeper counterparts. Our study suggests that ensembles of trees and neurons, not necessarily deep, work effectively for the problem of $t\bar{t}h$ detection.

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arXiv2016

Study Of Boosted W-Jets And Higgs-Jets With the SiFCC Detector

We study the detector performance in the reconstruction of hadronically-decaying W bosons and Higgs bosons at very high energy proton colliders using a full GEANT4 simulation of the SiFCC detector. The W and Higgs bosons carry transverse momentum in the multi-TeV range, which results in collimated decay products that are reconstructed as a single jet. We present a measurement of the energy response and resolution of boosted W-jets and Higgs-jets and show the separation of two sub-jets within the boosted boson jet.

hep-ex↗

arXiv2013

Snowmass Energy Frontier Simulations

This document describes the simulation framework used in the Snowmass Energy Frontier studies for future Hadron Colliders. An overview of event generation with {\sc Madgraph}5 along with parton shower and hadronization with {\sc Pythia}6 is followed by a detailed description of pile-up and detector simulation with {\sc Delphes}3. Details of event generation are included in a companion paper cited within this paper. The input parametrization is chosen to reflect the best object performance expected from the future ATLAS and CMS experiments; this is referred to as the "Combined Snowmass Detector". We perform simulations of $pp$ interactions at center-of-mass energies $\sqrt{s}=$ 14, 33, and 100 TeV with 0, 50, and 140 additional $pp$ pile-up interactions. The object performance with multi-TeV $pp$ collisions are studied for the first time using large pile-up interactions.

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