A5108064166- Particle physics theoretical and experimental studies
- High-Energy Particle Collisions Research
- Particle Detector Development and Performance
- Quantum Chromodynamics and Particle Interactions
- Dark Matter and Cosmic Phenomena
- Computational Physics and Python Applications
- Neutrino Physics Research
- Cosmology and Gravitation Theories
- Distributed and Parallel Computing Systems
- Radiation Detection and Scintillator Technologies
- Medical Imaging Techniques and Applications
- Astrophysics and Cosmic Phenomena
- Black Holes and Theoretical Physics
- Nuclear reactor physics and engineering
- Atomic and Subatomic Physics Research
- Anomaly Detection Techniques and Applications
- Adversarial Robustness in Machine Learning
- Statistical Methods and Inference
- Geophysical and Geoelectrical Methods
- Advanced X-ray and CT Imaging
- Image Processing and 3D Reconstruction
- Algorithms and Data Compression
- Big Data Technologies and Applications
- Generative Adversarial Networks and Image Synthesis
- Nuclear Engineering Thermal-Hydraulics
University of California, Irvine
2020-2025
University of Manchester
2023-2024
Atlas Scientific (United States)
2024
National Tsing Hua University
2021-2024
Institute of Modern Physics
2023-2024
Fudan University
2023-2024
University of Washington
2021-2024
Institute of High Energy Physics
2023-2024
Chinese Academy of Sciences
2023-2024
The University of Adelaide
2018-2023
Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions. We describe a set of known, upgraded, new methods ML-based unfolding. The performance these approaches are evaluated on the same two datasets. find that all techniques capable accurately reproducing particle-level spectra complex observables. Given conceptually diverse, they offer an exciting toolkit class measurements can probe Standard Model with...
The measurements performed by particle physics experiments must account for the imperfect response of detectors used to observe interactions. One approach, unfolding, statistically adjusts experimental data detector effects. Recently, generative machine learning models have shown promise performing unbinned unfolding in a high number dimensions. However, all current approaches are limited fixed set observables, making them unable perform full-event variable dimensional environment collider...
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, ``all-jet'' channel, results 6-jet final-state which is particularly difficult to reconstruct $pp$ collisions due number of permutations possible. We present novel approach this class problem, based on neural networks using generalized attention mechanism, that we call symmetry preserving (spa-net). train one such...
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 measurements can compared to certain theoretical predictions or from other detectors. Methods solve this \textit{inverse problem} of mapping observations quantities underlying collision are essential parts many physics analyses LHC. We investigate and compare various generative deep learning methods approximate inverse...
Abstract Reconstructing unstable heavy particles requires sophisticated techniques to sift through the large number of possible permutations for assignment detector objects underlying partons. An approach based on a generalized attention mechanism, symmetry preserving networks (SPA-NET), has been previously applied top quark pair decays at Large Hadron Collider which produce only hadronic jets. Here we extend SPA-NET architecture consider multiple input object types, such as leptons, well...
A bstract We study the effectiveness of theoretically-motivated high-level jet observables in extreme context jets with a large number hard sub-jets (up to N = 8). Previous studies indicate that are powerful, interpretable tools probe substructure for ≤ 3 sub-jets, but deep neural networks trained on low-level constituents match or slightly exceed their performance. extend this work up 8 using particle-flow (PFNs) and Transformer based estimate loose upper bound classification...
The creation of unstable heavy particles at the Large Hadron Collider is most direct way to address some deepest open questions in physics. Collisions typically produce variable-size sets observed which have inherent ambiguities complicating assignment decay products particles. Current strategies for tackling these challenges physics community ignore physical symmetries and consider all possible permutations do not scale complex configurations. Attention based deep learning methods sequence...
The classification of jets as quark- versus gluon-initiated is an important yet challenging task in the analysis data from high-energy particle collisions and search for physics beyond Standard Model. recent integration deep neural networks operating on low-level detector information has resulted significant improvements power quark/gluon jet tagging models. However, improved such models trained simulated samples come at cost reduced interpretability, raising concerns about their...
The measurements performed by particle physics experiments must account for the imperfect response of detectors used to observe interactions. One approach, unfolding, statistically adjusts experimental data detector effects. Recently, generative machine learning models have shown promise performing unbinned unfolding in a high number dimensions. However, all current approaches are limited fixed set observables, making them unable perform full-event variable dimensional environment collider...
Reconstructing unstable heavy particles requires sophisticated techniques to sift through the large number of possible permutations for assignment detector objects underlying partons. An approach based on a generalized attention mechanism, symmetry preserving networks (Spa-Net), has been previously applied top quark pair decays at Large Hadron Collider which produce only hadronic jets. Here we extend Spa-Net architecture consider multiple input object types, such as leptons, well global...
Searches for new physics in the top quark sector are of great theoretical interest, yet some powerful avenues discovery remain unexplored. We characterize expected statistical power LHC dataset to constrain single production heavy partners $T$ decaying a and photon or gluon. describe an effective interaction which could generate such production, though limits apply range models. find sensitivity cross sections $10^{2}-10^{5}$ fb range, masses between 300 1000 GeV, depending on decay mode.
The primary aim of automated performance improvement is to reduce the running time programs while maintaining (or improving on) functionality. In this paper, Genetic Programming used find improvements in regular expressions for an array target programs, representing first application software run-time Regular Expression language. This particular problem interesting as there may be many possible alternative which perform same task exhibiting subtle differences performance. A benchmark suite...
We study the effectiveness of theoretically-motivated high-level jet observables in extreme context jets with a large number hard sub-jets (up to $N=8$). Previous studies indicate that are powerful, interpretable tools probe substructure for $N\le 3$ sub-jets, but deep neural networks trained on low-level constituents match or slightly exceed their performance. extend this work up $N=8$ using particle-flow (PFNs) and Transformer based estimate loose upper bound classification A...