215 citations · 896 across the 30 of their papers we have counts for
11 papers · 1 filter
Geometric algebra as the input language of collider foundation models
E. Abasov, L. Dudko, F. Grigoryev +2
A hard hadron-collider event is represented here as a single geometric object: the kinematics and object-type labels of all reconstructed final-state particles in one multivector $…
A Methodology for Developing Foundational Transformer Models in Collider Physics Analysis
E. Abasov, L. Dudko, E. Iudin +4
We present a methodology for training foundational transformer models capable of processing collider data with diverse kinematic signatures. Our universal foundation model is desig…
Applying Normalizing Flows for spin correlations reconstruction in associated top-quark pair and dark matter production
E. Abasov, L. Dudko, E. Iudin +5
We apply a unified machine-learning framework based on Normalizing Flows (NFs) for the event-by-event reconstruction of invisible momenta and the subsequent evaluation of spin-sens…
Manifestations of Dark Matter in processes with three and four Top-Quarks
E. Abasov, E. Boos, V. Bunichev +8
This study explores possible manifestations of Dark Matter (DM) in processes involving multiple top quarks at the LHC. We analyze both the associated production of scalar and pseud…
Reconstruction of angular correlations in the associated top quark and the dark matter mediator production
E. Abasov, L. Dudko, E. Iudin +5
For the process of single top quark production within the "simplified model" with a scalar dark matter mediator, a new variable based on angular correlations was presented, for the…
Separation of left-handed and anomalous right-handed vector operators contributions into the Wtb vertex for single and double resonant top quark production processes using a neural network
E. Abasov, E. Boos, V. Bunichev +8
The paper describes the application of deep neural networks for the searchdeviations from the Standard Model predictions at the Wtb vertex in the processes of single and double res…
Application of Kolmogorov-Arnold Networks in high energy physics
E. Abasov, P. Volkov, G. Vorotnikov +5
Kolmogorov-Arnold Networks represent a recent advancement in machine learning, with the potential to outperform traditional perceptron-based neural networks across various domains…
Search for dark matter mediator in the production of three and four top quarks
E. Abasov, E. Boos, V. Bunichev +8
In the context of simplified models of dark matter, the contributions of a scalar mediator to top quark production processes are considered. Tree-level and one-loop contributions o…
Angular correlations in associated top-quark and dark matter production at Large Hadron Collider
E. Abasov, E. Boos, V. Bunichev +7
In the paper, we consider the processes of single top-quark production in association with dark matter particles through the t-channel in simplified models with scalar and pseudosc…
General recipe to form input space for deep learning analysis of HEP scattering processes
Andrei Chernoded, Lev Dudko, Georgi Vorotnikov +4
Deep learning neural network technique (DNN) is one of the most efficient and general approach of multivariate data analysis of the collider experiments. The important step of the…
Beyond the Standard Model Physics at the HL-LHC and HE-LHC
X. Cid Vidal, M. D'Onofrio, P. J. Fox +294
This is the third out of five chapters of the final report [1] of the Workshop on Physics at HL-LHC, and perspectives on HE-LHC [2]. It is devoted to the study of the potential, in…