Search Results for author:
Found 9 papers, 7 papers with code
Improving Generative Model-based Unfolding with Schrödinger Bridges
Machine learning-based unfolding has enabled unbinned and high-dimensional differential cross section measurements.
Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation
Score based generative models are a new class of generative models that have been shown to accurately generate high dimensional calorimeter datasets.
High-dimensional and Permutation Invariant Anomaly Detection
Methods for anomaly detection of new physics processes are often limited to low-dimensional spaces due to the difficulty of learning high-dimensional probability densities.
Score-based Generative Models for Calorimeter Shower Simulation
Score-based generative models are a new class of generative algorithms that have been shown to produce realistic images even in high dimensional spaces, currently surpassing other state-of-the-art models for different benchmark categories and applications.
New directions for surrogate models and differentiable programming for High Energy Physics detector simulation
The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources.
Online-compatible Unsupervised Non-resonant Anomaly Detection
There is a growing need for anomaly detection methods that can broaden the search for new particles in a model-agnostic manner.
Point Cloud Transformers applied to Collider Physics
Methods for processing point cloud information have seen a great success in collider physics applications.
Unsupervised clustering for collider physics
We propose a new method for Unsupervised clustering in particle physics named UCluster, where information in the embedding space created by a neural network is used to categorise collision events into different clusters that share similar properties.
Data Analysis, Statistics and Probability High Energy Physics - Experiment
ABCNet: An attention-based method for particle tagging
In high energy physics, graph-based implementations have the advantage of treating the input data sets in a similar way as they are collected by collider experiments.
Data Analysis, Statistics and Probability High Energy Physics - Phenomenology
