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Unsupervised clustering for collider physics

Published in Phys.Rev.D 103 (2021) 9, 092007, 2021

We propose a new method for unsupervised clustering for collider physics named UCluster, where information in the embedding space created by a neural network is used to categorize collision events into different clusters that share similar properties. Read more

Recommended citation: Vinicius Mikuni and Florencia Canelli Phys. Rev. D *103*, 092007 https://journals.aps.org/prd/abstract/10.1103/PhysRevD.103.092007

Score-based generative models for calorimeter shower simulation

Published in Phys.Rev.D 106 (2022) 9, 092009, 2022

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. Read more

Recommended citation: Vinicius Mikuni and Benjamin Nachman Phys. Rev. D *106*, 092009 https://journals.aps.org/prd/abstract/10.1103/PhysRevD.106.092009

Anomaly detection under coordinate transformations

Published in Phys.Rev.D 107 (2023) 1, 015009, 2022

There is a growing need for machine-learning-based anomaly detection strategies to broaden the search for beyond-the-Standard-Model physics at the Large Hadron Collider (LHC) and elsewhere. Read more

Recommended citation: Gregor Kasieczka, Radha Mastandrea, Vinicius Mikuni, Benjamin Nachman, Mariel Pettee, and David Shih Phys. Rev. D *107*, 015009 https://arxiv.org/abs/2209.06225

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