Search Results for author:
Found 9 papers, 4 papers with code
CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge.
Refereeing the Referees: Evaluating Two-Sample Tests for Validating Generators in Precision Sciences
We propose a robust methodology to evaluate the performance and computational efficiency of non-parametric two-sample tests, specifically designed for high-dimensional generative models in scientific applications such as in particle physics.
Multiple testing for signal-agnostic searches of new physics with machine learning
In this work, we address the question of how to enhance signal-agnostic searches by leveraging multiple testing strategies.
Goodness of fit by Neyman-Pearson testing
The Neyman-Pearson strategy for hypothesis testing can be employed for goodness of fit if the alternative hypothesis is selected from data by exploring a rich parametrised family of models, while controlling the impact of statistical fluctuations.
Fast kernel methods for Data Quality Monitoring as a goodness-of-fit test
We here propose a machine learning approach for monitoring particle detectors in real-time.
Comparative Study of Coupling and Autoregressive Flows through Robust Statistical Tests
Normalizing Flows have emerged as a powerful brand of generative models, as they not only allow for efficient sampling of complicated target distributions, but also deliver density estimation by construction.
CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds
Precision measurements and new physics searches at the Large Hadron Collider require efficient simulations of particle propagation and interactions within the detectors.
Efficient Unsupervised Learning for Plankton Images
We compare our method with state-of-the-art unsupervised approaches, where a set of pre-defined hand-crafted features is used for clustering of plankton images.
Learning new physics efficiently with nonparametric methods
We present a machine learning approach for model-independent new physics searches.
