5 papers
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…
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…