activity
20232026
most citedThe MadNIS Reloaded

30 citations · 179 across the 19 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

hep-ph2024★ 4 cited

Generative Unfolding with Distribution Mapping

Anja Butter, Sascha Diefenbacher, Nathan Huetsch +4

Machine learning enables unbinned, highly-differential cross section measurements. A recent idea uses generative models to morph a starting simulation into the unfolded data. We sh…

physics.ins-det2024★ 8 cited

CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation

Claudius Krause, Michele Faucci Giannelli, Gregor Kasieczka +66

We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of i…

hep-ph2024★ 14 cited

Differentiable MadNIS-Lite

Theo Heimel, Olivier Mattelaer, Tilman Plehn +1

Differentiable programming opens exciting new avenues in particle physics, also affecting future event generators. These new techniques boost the performance of current and planned…

hep-ph2024★ 22 cited

The Landscape of Unfolding with Machine Learning

Nathan Huetsch, Javier Mariño Villadamigo, Alexander Shmakov +9

Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions. We describe a set of known, upgraded, and new…

hep-ph2024★ 9 cited

Constraining the Higgs Potential with Neural Simulation-based Inference for Di-Higgs Production

Radha Mastandrea, Benjamin Nachman, Tilman Plehn

Determining the form of the Higgs potential is one of the most exciting challenges of modern particle physics. Higgs pair production directly probes the Higgs self-coupling and sho…

hep-ph2024★ 22 cited

CaloDREAM -- Detector Response Emulation via Attentive flow Matching

Luigi Favaro, Ayodele Ore, Sofia Palacios Schweitzer +1

Detector simulations are an exciting application of modern generative networks. Their sparse high-dimensional data combined with the required precision poses a serious challenge. W…

astro-ph.CO2024★ 4 cited

PINNferring the Hubble Function with Uncertainties

Lennart Röver, Björn Malte Schäfer, Tilman Plehn

The Hubble function characterizes a given Friedmann-Robertson-Walker spacetime and can be related to the densities of the cosmological fluids and their equations of state. We show…