Detecting Gamma-Ray Burst (GRB) signals from triggerless data poses significant challenges due to high noise levels, a problem similarly encountered in the Large High Altitude Air Shower Observatory's Water Cherenkov Detector Array (LHAASO-WCDA) triggerless data analysis. This research aims to enhance the GRB triggerless data algorithm that leverages gamma-ray showers' distinct spatial...
This presentation delves into integrating quantum computing into transformer architectures to enhance High Energy Physics (HEP) analysis performance. By encoding classical HEP data into quantum states using a quantum-trainable circuit, we aim to harness the strengths of both quantum and classical computing. This hybrid approach is designed to improve data processing and analysis. The...
In this presentation, we discuss the latest developments and applications of Multivariate Analysis (MVA) techniques within the Belle II experiment. The Belle II experiment, operating at the SuperKEKB accelerator in Japan, aims to explore the fundamental interactions of particles and test the limits of the Standard Model of particle physics.
MVA techniques are essential tools in the analysis...
High-energy physics relies on large and accurate samples of simulated events, but generating these samples with GEANT4 is CPU intensive. The ATLAS experiment has employed generative adversarial networks (GANs) for fast shower simulation, which is an important approach to solving the problem. Quantum GANs, leveraging the advantages of quantum computing, have the potential to outperform standard...
We apply machine-learning techniques to the effective-field-theory analysis of the $e^+e^- \to W^+W^-$ processes at future lepton colliders, and demonstrate their advantages in comparison with conventional methods, such as optimal observables. In particular, we show that machine-learning methods are more robust to %effects of systematic uncertainties, initial state radiations
detector...
The search for heavy resonances beyond the Standard Model (BSM) is a key objective at the LHC. While the recent use of advanced deep neural networks for boosted-jet tagging significantly enhances the sensitivity of dedicated searches, it is limited to specific final states, leaving vast potential BSM phase space underexplored. In this talk, we introduce a novel experimental method,...
Exact solutions to combinatorial optimization problems are challenging to obtain using classical computing. The current tenet in the field is that quantum computers can address these problems more efficiently. While promising algorithms require fault-tolerant quantum hardware, variational algorithms have emerged as viable candidates for near-term devices. The success of these algorithms hinges...
