5 citations · 11 across the 16 of their papers we have counts for
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Stable and Interpretable Jet Physics with IRC-Safe Equivariant Feature Extraction
Partha Konar, Vishal S. Ngairangbam, Michael Spannowsky +1
Deep learning has achieved remarkable success in jet classification tasks, yet a key challenge remains: understanding what these models learn and how their features relate to known…
Theory-informed neural networks for particle physics
Barry M. Dillon, Michael Spannowsky
We present a theory-informed reinforcement-learning framework that recasts the combinatorial assignment of final-state particles in hadron collider events as a Markov decision proc…
Improved Ground State Estimation in Quantum Field Theories via Normalising Flow-Assisted Neural Quantum States
Vishal S. Ngairangbam, Michael Spannowsky, Timur Sypchenko
We propose a hybrid variational framework that enhances Neural Quantum States (NQS) with a Normalising Flow-based sampler to improve the expressivity and trainability of quantum ma…
Enhancing anomaly detection with topology-aware autoencoders
Vishal S. Ngairangbam, Błażej Rozwoda, Kazuki Sakurai +1
Anomaly detection in high-energy physics is essential for identifying new physics beyond the Standard Model. Autoencoders provide a signal-agnostic approach but are limited by the…
Three-Body Non-Locality in Particle Decays
Paweł Horodecki, Kazuki Sakurai, Abhyoudai S. Shaleena +1
The exploration of entanglement and Bell non-locality among multi-particle quantum systems offers a profound avenue for testing and understanding the limits of quantum mechanics an…