16 citations · 78 across the 28 of their papers we have counts for
7 papers · 1 filter
is all you need: production of multiple semi-invisible resonances at hadron colliders
Zhongtian Dong, Kyoungchul Kong, Konstantin T. Matchev +1
The stransverse mass variable was originally proposed for the study of hadron collider events in which parent particles are produced and then decay semi-invisibly. H…
Exploring the Truth and Beauty of Theory Landscapes with Machine Learning
Konstantin T. Matchev, Katia Matcheva, Pierre Ramond +1
Theoretical physicists describe nature by i) building a theory model and ii) determining the model parameters. The latter step involves the dual aspect of both fitting to the exist…
Seeking Truth and Beauty in Flavor Physics with Machine Learning
Konstantin T. Matchev, Katia Matcheva, Pierre Ramond +1
The discovery process of building new theoretical physics models involves the dual aspect of both fitting to the existing experimental data and satisfying abstract theorists' crite…
Identifying the Group-Theoretic Structure of Machine-Learned Symmetries
Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva +3
Deep learning was recently successfully used in deriving symmetry transformations that preserve important physics quantities. Being completely agnostic, these techniques postpone t…
Discovering Sparse Representations of Lie Groups with Machine Learning
Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva +3
Recent work has used deep learning to derive symmetry transformations, which preserve conserved quantities, and to obtain the corresponding algebras of generators. In this letter,…
Deep Learning Symmetries and Their Lie Groups, Algebras, and Subalgebras from First Principles
Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva +3
We design a deep-learning algorithm for the discovery and identification of the continuous group of symmetries present in a labeled dataset. We use fully connected neural networks…
Is the Machine Smarter than the Theorist: Deriving Formulas for Particle Kinematics with Symbolic Regression
Zhongtian Dong, Kyoungchul Kong, Konstantin T. Matchev +1
We demonstrate the use of symbolic regression in deriving analytical formulas, which are needed at various stages of a typical experimental analysis in collider phenomenology. As a…