49 citations · 217 across the 22 of their papers we have counts for
22 papers
Search for Accelerator-Produced Sub-GeV Dark Matter with the NOvA Near Detector
S. Abubakar, M. Acero, B. Acharya +184
The NuMI facility at Fermilab produces a high-intensity beam of muon neutrinos and antineutrinos, designed to study neutrino oscillations. This beam may also be a source of dark ma…
Fast and Precise Track Fitting with Machine Learning
Ryan Miller, Alexander Shmakov, Kyuho Oh +5
Efficient and accurate particle tracking is crucial for measuring Standard Model parameters and searching for new physics. This task consists of two major computational steps: trac…
Reconstruction of boosted and resolved multi-Higgs-boson events with symmetry-preserving attention networks
Haoyang Li, Marko Stamenkovic, Alexander Shmakov +12
The production of multiple Higgs bosons at the CERN LHC provides a direct way to measure the trilinear and quartic Higgs self-interaction strengths as well as potential access to b…
Measurement of the double-differential cross section of muon-neutrino charged-current interactions with low hadronic energy in the NOvA Near Detector
M. A. Acero, B. Acharya, P. Adamson +208
The NOvA collaboration reports cross-section measurements for charged-current interactions with low hadronic energy (maximum kinetic energy of 250 MeV for protons and 175 MeV…
Measurement of d2sigma/d|q|dEavail in charged current neutrino-nucleus interactions at <Ev> = 1.86 GeV using the NOvA Near Detector
M. A. Acero, B. Acharya, P. Adamson +205
Double- and single-differential cross sections for inclusive charged-current neutrino-nucleus scattering are reported for the kinematic domain 0 to 2 GeV/c in three-momentum transf…
The Landscape of Unfolding with Machine Learning
Nathan Huetsch, Javier Mariño Villadamigo, Alexander Shmakov +9
Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions. We describe a set of known, upgraded, and new…
Function Approximation for Reinforcement Learning Controller for Energy from Spread Waves
Soumyendu Sarkar, Vineet Gundecha, Sahand Ghorbanpour +5
The industrial multi-generator Wave Energy Converters (WEC) must handle multiple simultaneous waves coming from different directions called spread waves. These complex devices in c…
Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion
Alexander Shmakov, Kevin Greif, Michael James Fenton +3
The measurements performed by particle physics experiments must account for the imperfect response of the detectors used to observe the interactions. One approach, unfolding, stati…
Search for -Violating Neutrino Nonstandard Interactions with the NOvA Experiment
NOvA Collaboration, M. A. Acero, B. Acharya +204
This Letter reports a search for charge-parity () symmetry violating nonstandard interactions (NSI) of neutrinos with matter using the NOvA Experiment, and examines their effec…
AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning
Mohammadamin Tavakoli, Yin Ting T. Chiu, Alexander Shmakov +3
Deep learning-based reaction predictors have undergone significant architectural evolution. However, their reliance on reactions from the US Patent Office results in a lack of inte…
Reconstruction of Unstable Heavy Particles Using Deep Symmetry-Preserving Attention Networks
Michael James Fenton, Alexander Shmakov, Hideki Okawa +5
Reconstructing unstable heavy particles requires sophisticated techniques to sift through the large number of possible permutations for assignment of detector objects to the underl…
End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics
Alexander Shmakov, Kevin Greif, Michael Fenton +3
High-energy collisions at the Large Hadron Collider (LHC) provide valuable insights into open questions in particle physics. However, detector effects must be corrected before meas…
Interpretable Joint Event-Particle Reconstruction for Neutrino Physics at NOvA with Sparse CNNs and Transformers
Alexander Shmakov, Alejandro Yankelevich, Jianming Bian +1
The complex events observed at the NOvA long-baseline neutrino oscillation experiment contain vital information for understanding the most elusive particles in the standard model.…
Skip Training for Multi-Agent Reinforcement Learning Controller for Industrial Wave Energy Converters
Soumyendu Sarkar, Vineet Gundecha, Sahand Ghorbanpour +4
Recent Wave Energy Converters (WEC) are equipped with multiple legs and generators to maximize energy generation. Traditional controllers have shown limitations to capture complex…
Deep Learning Models of the Discrete Component of the Galactic Interstellar Gamma-Ray Emission
Alexander Shmakov, Mohammadamin Tavakoli, Pierre Baldi +3
A significant point-like component from the small scale (or discrete) structure in the H2 interstellar gas might be present in the Fermi-LAT data, but modeling this emission relies…
Improved Modeling of the Discrete Component of the Galactic Interstellar Gamma-ray Emission and Implications for the Fermi-LAT Galactic Center Excess
Christopher M. Karwin, Alex Broughton, Simona Murgia +3
The aim of this work is to improve models for the gamma-ray discrete or small-scale structure related to H2 interstellar gas. Reliably identifying this contribution is important to…
Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation
Mohammadamin Tavakoli, Alexander Shmakov, Francesco Ceccarelli +1
It is fundamental for science and technology to be able to predict chemical reactions and their properties. To achieve such skills, it is important to develop good representations…
SPANet: Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attention
Alexander Shmakov, Michael James Fenton, Ta-Wei Ho +3
The creation of unstable heavy particles at the Large Hadron Collider is the most direct way to address some of the deepest open questions in physics. Collisions typically produce…
A* Search Without Expansions: Learning Heuristic Functions with Deep Q-Networks
Forest Agostinelli, Shahaf S. Shperberg, Alexander Shmakov +3
Efficiently solving problems with large action spaces using A* search remains a significant challenge. This is because, for each iteration of A* search, the number of nodes generat…
Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks
Michael James Fenton, Alexander Shmakov, Ta-Wei Ho +3
Top quarks, produced in large numbers at the Large Hadron Collider, have a complex detector signature and require special reconstruction techniques. The most common decay mode, the…
ColosseumRL: A Framework for Multiagent Reinforcement Learning in -Player Games
Alexander Shmakov, John Lanier, Stephen McAleer +3
Much of recent success in multiagent reinforcement learning has been in two-player zero-sum games. In these games, algorithms such as fictitious self-play and minimax tree search c…
Solving the Rubik's Cube Without Human Knowledge
Stephen McAleer, Forest Agostinelli, Alexander Shmakov +1
A generally intelligent agent must be able to teach itself how to solve problems in complex domains with minimal human supervision. Recently, deep reinforcement learning algorithms…