1 citations · 3 across the 6 of their papers we have counts for
4 papers · 1 filter
Evidence of Relationships Among Fundamental Constants of the Standard Model
S. V. Chekanov, H. Kjellerstrand
This paper presents an approach to reducing the number of fundamental parameters in the Standard Model (SM) using genetic programming, a machine learning technique based on evoluti…
Discovering the Underlying Analytic Structure Within Standard Model Constants Using Artificial Intelligence
S. V. Chekanov, H. Kjellerstrand
This paper presents a method for uncovering hidden analytic relationships among the fundamental parameters of the Standard Model (SM), a foundational theory in physics that describ…
Enhancing Sensitivity for Di-Higgs Boson Searches Using Anomaly Detection and Supervised Machine Learning Techniques
Sergei V. Chekanov, Wasikul Islam, Nicholas Luongo
This paper explores different strategies for enhancing sensitivity to new heavy resonances that decay into two or more Higgs bosons. This is achieved using two neural network archi…
Searches for new physics in collision events using a statistical technique for anomaly detection
S. V. Chekanov
This paper discusses a statistical anomaly-detection method for model-independent searches for new physics in collision events produced at the Large Hadron Collider (LHC). The meth…