7 papers · 1 filter
Conformal calibration and look-elsewhere effect in anomaly detection for new-physics searches
Jack Y. Araz, Michael Spannowsky
Machine-learned anomaly detection is reshaping searches for new physics, but it has outrun the statistics used to interpret it. A raw anomaly score has no calibrated meaning, a mod…
Searching for axions with quantum interferometry
Tanmay Kumar Poddar, Michael Spannowsky
Quantum phase measurements offer a complementary route to axion searches. We show that axion-photon interactions can imprint both Aharonov-Bohm (AB) and Berry phases in experimenta…
Quantum-Inspired Tensor Network Autoencoders for Anomaly Detection: A MERA-Based Approach
Emre Gurkanli, Michael Spannowsky
We investigate whether a multiscale tensor-network architecture can provide a useful inductive bias for reconstruction-based anomaly detection in collider jets. Jets are produced b…
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…
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…