A5035676573- Anomaly Detection Techniques and Applications
- Software System Performance and Reliability
- Particle physics theoretical and experimental studies
- Artificial Intelligence in Healthcare
- Gaussian Processes and Bayesian Inference
- Infectious Diseases and Mycology
- Generative Adversarial Networks and Image Synthesis
- Quantum Chromodynamics and Particle Interactions
- High-Energy Particle Collisions Research
- Advanced Data Compression Techniques
University of California, Berkeley
2020-2021
Lawrence Berkeley National Laboratory
2021
A bstract In the limit where partons become collinear to each other, scattering amplitudes factorize into a product of universal, process-independent building blocks and involving fewer partons. We compute these universal — known as splitting for two QCD up third loop order in QCD. Our results describe arbitrary time-like processes. Due violation strict factorization space-like processes, we specifically present three-parton at order. To achieve our results, perform expansion three-loop...
Histogram-based template fits are the main technique used for estimating parameters of high energy physics Monte Carlo generators. Parametrized neural network reweighting can be to extend this fitting procedure many dimensions and does not require binning. If fit is performed using reconstructed data, then expensive detector simulations must training networks. We introduce a new two-level approach that only requires one dataset with simulation set additional generation-level datasets without...
A common setting for scientific inference is the ability to sample from a high-fidelity forward model (simulation) without having an explicit probability density of data. We propose simulation-based maximum likelihood deconvolution approach in this called OmniFold. Deep learning enables be naturally unbinned and (variable-, and) high-dimensional. In contrast parameter estimation, goal remove detector distortions order enable variety down-stream tasks. Our deep generalization Richardson-Lucy...