A5015986934- Particle physics theoretical and experimental studies
- High-Energy Particle Collisions Research
- Particle Detector Development and Performance
- Quantum Chromodynamics and Particle Interactions
- Computational Physics and Python Applications
- Dark Matter and Cosmic Phenomena
- Cosmology and Gravitation Theories
- Neutrino Physics Research
- Medical Imaging Techniques and Applications
- Advanced Data Storage Technologies
- Distributed and Parallel Computing Systems
- Spectroscopy and Chemometric Analyses
- Advanced Chemical Sensor Technologies
- Scientific Research and Discoveries
- Astrophysics and Cosmic Phenomena
- Superconducting Materials and Applications
- Anomaly Detection Techniques and Applications
- Radiation Detection and Scintillator Technologies
- Parallel Computing and Optimization Techniques
- Algorithms and Data Compression
- Spectroscopy Techniques in Biomedical and Chemical Research
University of Geneva
2024-2025
Istanbul University
2024
RWTH Aachen University
2021-2023
A bstract Autoencoders are widely used in machine learning applications, particular for anomaly detection. Hence, they have been introduced high energy physics as a promising tool model-independent new searches. We scrutinize the usage of autoencoders unsupervised detection based on reconstruction loss to show their capabilities, but also limitations. As particle benchmark scenario, we study tagging top jet images background QCD images. Although reproduce positive results from literature,...
Searches for anomalies are a significant motivation the LHC and help define key analysis steps, including triggers. We discuss specific examples how can be defined through probability density estimates, evaluated in physics space or an appropriate neural network latent space, model-dependence choosing data parameterisation. illustrate this classical k-means clustering, Dirichlet variational autoencoder, invertible networks. For two especially challenging scenarios of jets from dark sector we...
Searches for anomalies are a significant motivation the LHC and help define key analysis steps, including triggers. We discuss specific examples how can be defined through probability density estimates, evaluated in physics space or an appropriate neural network latent space, model-dependence choosing data parameterisation. illustrate this classical k-means clustering, Dirichlet variational autoencoder, invertible networks. For two especially challenging scenarios of jets from dark sector we...
We propose a new model-independent method for physics searches called Cluster Scanning. It uses the k-means algorithm to perform clustering in space of low-level event or jet observables, and separates potentially anomalous clusters construct signal-enriched region. The invariant mass spectra these two regions are then used determine whether resonant signal is present. A pseudo-analysis on LHC Olympics dataset with $Z'$ resonance shows that Scanning outperforms widely 4-parameter functional...
A bstract We propose a new model-independent method for physics searches called Cluster Scanning. It uses the k-means algorithm to perform clustering in space of low-level event or jet observables, and separates potentially anomalous clusters construct signal-enriched region. The spectra selected observable (e.g. invariant mass) these two regions are then used determine whether resonant signal is present. pseudo-analysis on LHC Olympics dataset with Z ′ resonance shows that Scanning...