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20212026
most citedThe Landscape of Unfolding with Machine Learning

22 citations · 88 across the 25 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…

hep-ph2024

The Fundamental Limit of Jet Tagging

Joep Geuskens, Nishank Gite, Michael Krämer +4

Identifying the origin of high-energy hadronic jets ('jet tagging') has been a critical benchmark problem for machine learning in particle physics. Jets are ubiquitous at colliders…

hep-ph2024★ 1 cited

Multidimensional Deconvolution with Profiling

Huanbiao Zhu, Krish Desai, Mikael Kuusela +3

In many experimental contexts, it is necessary to statistically remove the impact of instrumental effects in order to physically interpret measurements. This task has been extensiv…

physics.ins-det2024★ 5 cited

Design of a SiPM-on-Tile ZDC for the future EIC and its Performance with Graph Neural Networks

Ryan Milton, Sebouh J. Paul, Barak Schmookler +5

We present a design for a high-granularity zero-degree calorimeter (ZDC) for the upcoming Electron-Ion Collider (EIC). The design uses SiPM-on-tile technology and features a novel…

physics.data-an2024★ 4 cited

Parnassus: An Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction

Etienne Dreyer, Eilam Gross, Dmitrii Kobylianskii +3

Detector simulation and reconstruction are a significant computational bottleneck in particle physics. We develop Particle-flow Neural Assisted Simulations (Parnassus) to address t…

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★ 19 cited

Solving Key Challenges in Collider Physics with Foundation Models

Vinicius Mikuni, Benjamin Nachman

Foundation Models are neural networks that are capable of simultaneously solving many problems. Large Language Foundation Models like ChatGPT have revolutionized many aspects of da…