Resolving combinatorial ambiguities in dilepton 
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 event topologies with neural networks

Alhazmi, Haider; Dong, Zhongtian; Huang, Li; Kim, Jeong Han; Kong, Kyoungchul; Shih, David

doi:10.1103/physrevd.105.115011

Cited by 5 publications

(1 citation statement)

References 56 publications

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“…17. In addition to invisible momentum reconstruction , M 2 has been used in various collider analyses, including applications to Higgs mass measurements (Konar and Swain, 2016b), new particle mass measurements Baer et al, 2017;, distinguishing symmetric and asymmetric events , resolving combinatorial ambiguities Alhazmi et al, 2022), measurements of the top-Higgs Yukawa CP structures (Gonçalves, Kong, and Kim, 2018;Gonçalves et al, 2022), applications to compressed top searches (Cho et al, 2015), Z 0 searches (Kim, Lee, and Park, 2015), applications in the antler topology (Konar and Swain, 2016a), etc. (Konar and Swain, 2016aSwain, ,2016bSwain, , 2017Swain and Konar, 2015).…”

Section: (2008b)mentioning

confidence: 99%

Kinematic variables and feature engineering for particle phenomenology

Franceschini,

Kim,

Kong

et al. 2023

Rev. Mod. Phys.

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Kinematic variables play an important role in collider phenomenology, as they expedite discoveries of new particles by separating signal events from unwanted background events and allow for measurements of particle properties such as masses, couplings, and spins. For the past ten years, an enormous number of kinematic variables have been designed and proposed, primarily for the experiments at the CERN Large Hadron Collider, allowing for a drastic reduction of highdimensional experimental data to lower-dimensional observables, from which one can readily extract underlying features of phase space and develop better-optimized data-analysis strategies. Recent developments in the area of phase-space kinematics are reviewd, and new kinematic variables with important phenomenological implications and physics applications are summarized. Recently proposed analysis methods and techniques specifically designed to leverage new kinematic variables are also reviewed. As machine learning is currently percolating through many fields of particle physics, including collider phenomenology, the interconnection and mutual complementarity of kinematic variables and machine-learning techniques are discussed. Finally, the manner in which utilization of kinematic variables originally developed for colliders can be extended to other highenergy physics experiments, including neutrino experiments, is discussed.

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Section: (2008b)mentioning

confidence: 99%

Kinematic variables and feature engineering for particle phenomenology

Franceschini,

Kim,

Kong

et al. 2023

Rev. Mod. Phys.

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Interpretable deep learning models for the inference and classification of LHC data

Ngairangbam,

Spannowsky

2024

J. High Energ. Phys.

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The Shower Deconstruction methodology is pivotal in distinguishing signal and background jets, leveraging the detailed information from perturbative parton showers. Rooted in the Neyman-Pearson lemma, this method is theoretically designed to differentiate between signal and background processes optimally in high-energy physics experiments. A key challenge, however, arises from the combinatorial growth associated with increasing jet constituents, which hampers its computational feasibility. We address this by demonstrating that the likelihood derived from comparing the most probable signal and background shower histories is equally effective for discrimination as the conventional approach of summing over all potential histories in top quark versus Quantum Chromodynamics (QCD) scenarios. We propose a novel approach by conceptualising the identification of the most probable shower history as a Markov Decision Process (MDP). Utilising a sophisticated modular point-transformer architecture, our method efficiently learns the optimal policy for this task. The developed neural agent excels in constructing the most likely shower history and demonstrates robust generalisation capabilities on unencountered test data. Remarkably, our approach mitigates the complexity inherent in the inference process, achieving a linear scaling relationship with the number of jet constituents. This offers a computationally viable and theoretically sound method for signal-background differentiation, paving the way for more effective data analysis in particle physics.

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Portraying double Higgs at the Large Hadron Collider II

Huang

Kang

Kim

et al. 2022

J. High Energ. Phys.

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The Higgs potential is vital to understand the electroweak symmetry breaking mechanism, and probing the Higgs self-interaction is arguably one of the most important physics targets at current and upcoming collider experiments. In particular, the triple Higgs coupling may be accessible at the HL-LHC by combining results in multiple channels, which motivates to study all possible decay modes for the double Higgs production. In this paper, we revisit the double Higgs production at the HL-LHC in the final state with two b-tagged jets, two leptons and missing transverse momentum. We focus on the performance of various neural network architectures with different input features: low-level (four momenta), high-level (kinematic variables) and image-based. We find it possible to bring a modest increase in the signal sensitivity over existing results via careful optimization of machine learning algorithms making a full use of novel kinematic variables.

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Resolving combinatorial ambiguities in dilepton tt¯ event topologies with neural networks

Cited by 5 publications

References 56 publications

Kinematic variables and feature engineering for particle phenomenology

Kinematic variables and feature engineering for particle phenomenology

Interpretable deep learning models for the inference and classification of LHC data

Portraying double Higgs at the Large Hadron Collider II

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