Search Results for author: Sascha Diefenbacher

Found 6 papers, 4 papers with code

The Landscape of Unfolding with Machine Learning

no code implementations29 Apr 2024 Nathan Huetsch, Javier Mariño Villadamigo, Alexander Shmakov, Sascha Diefenbacher, Vinicius Mikuni, Theo Heimel, Michael Fenton, Kevin Greif, Benjamin Nachman, Daniel Whiteson, Anja Butter, Tilman Plehn

Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions.

Improving Generative Model-based Unfolding with Schrödinger Bridges

1 code implementation23 Aug 2023 Sascha Diefenbacher, Guan-Horng Liu, Vinicius Mikuni, Benjamin Nachman, Weili Nie

Machine learning-based unfolding has enabled unbinned and high-dimensional differential cross section measurements.

CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation

2 code implementations8 May 2023 Erik Buhmann, Sascha Diefenbacher, Engin Eren, Frank Gaede, Gregor Kasieczka, Anatolii Korol, William Korcari, Katja Krüger, Peter McKeown

Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics.

DCTRGAN: Improving the Precision of Generative Models with Reweighting

2 code implementations3 Sep 2020 Sascha Diefenbacher, Engin Eren, Gregor Kasieczka, Anatolii Korol, Benjamin Nachman, David Shih

We introduce a post-hoc correction to deep generative models to further improve their fidelity, based on the Deep neural networks using the Classification for Tuning and Reweighting (DCTR) protocol.

GANplifying Event Samples

no code implementations14 Aug 2020 Anja Butter, Sascha Diefenbacher, Gregor Kasieczka, Benjamin Nachman, Tilman Plehn

A critical question concerning generative networks applied to event generation in particle physics is if the generated events add statistical precision beyond the training sample.

Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed

2 code implementations11 May 2020 Erik Buhmann, Sascha Diefenbacher, Engin Eren, Frank Gaede, Gregor Kasieczka, Anatolii Korol, Katja Krüger

Accurate simulation of physical processes is crucial for the success of modern particle physics.

Instrumentation and Detectors High Energy Physics - Experiment High Energy Physics - Phenomenology Data Analysis, Statistics and Probability

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