Could MT2 be a singularity variable?

Park, Chan Beom

doi:10.1007/jhep11(2021)042

Cited by 3 publications

(2 citation statements)

References 47 publications

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“…7(a) in three or two spatial dimensions , and the Cambridge M T2 variable from Sec. V.B (Park, 2021a). However, there are also more recently discovered singularity variables like the Δ 4 variable (Agrawal et al, 2014;Debnath et al, 2019), which is applicable to the three-step, two-body decay chain in Fig.…”

Section: Singularity Variablesmentioning

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: Singularity Variablesmentioning

confidence: 99%

Kinematic variables and feature engineering for particle phenomenology

Franceschini,

Kim,

Kong

et al. 2023

Rev. Mod. Phys.

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“…We will not further investigate the features of analytical reconstruction in our current study, since these masses are unknown a priory when applying the method to new physics beyond the SM, and it is difficult to generalize unlike other methods. Finally, we refer to Refs [75][76][77][78][79][80] for readers who are interested in the singularity variables.…”

Section: A6 Analytic Reconstructionmentioning

confidence: 99%

Resolving Combinatorial Ambiguities in Dilepton $t \bar t$ Event Topologies with Neural Networks

Alhazmi,

Dong,

Huang

et al. 2022

Preprint

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We study the potential of deep learning to resolve the combinatorial problem in SUSYlike events with two invisible particles at the LHC. As a concrete example, we focus on dileptonic t t events, where the combinatorial problem becomes an issue of binary classification: pairing the correct lepton with each b quark coming from the decays of the tops. We investigate the performance of a number of machine learning algorithms, including attention-based networks, which have been usedfor a similar problem in the fully-hadronic channel of t t production; and the Lorentz Boost Network, which is motivated by physics principles. We then consider the general case when the underlying mass spectrum is unknown, and hence no kinematic endpoint information is available. Compared against existing methods based on kinematic variables, we demonstrate that the efficiency for selecting the correct pairing is greatly improved by utilizing deep learning techniques.

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