Published in Eur.Phys.J.Plus 135 (2020) 6, 463, 2020
In high energy physics, graph-based implementations have the advantage of treating the input data sets in a similar way as they are collected by collider experiments. Read more
Recommended citation: Vinicius Mikuni and Florencia Canelli Eur. Phys. J. Plus *135*, 463 (2020) https://link.springer.com/article/10.1140/epjp/s13360-020-00497-3
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
Published in Mach.Learn.Sci.Tech. 2 (2021) 3, 035027, Sci.Technol. 2 035027, 2021
Methods for processing point cloud information have seen a great success in collider physics applications. Read more
Recommended citation: Vinicius Mikuni and Florencia Canelli 2021 Mach. Learn.: Sci. Technol. *2* 035027 https://iopscience.iop.org/article/10.1088/2632-2153/ac07f6
Published in Phys.Rev.D 105 (2022) 5, 055006, 2022
There is a growing need for anomaly detection methods that can broaden the search for new particles in a model-agnostic manner. Read more
Recommended citation: Vinicius Mikuni, Benjamin Nachman, and David Shih Phys. Rev. D *105*, 055006 https://journals.aps.org/prd/abstract/10.1103/PhysRevD.105.055006
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
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
Published in arXiv, 2022
The CERN Large Hadron Collider was built to uncover fundamental particles and their interactions at the energy frontier. Read more
Recommended citation: Gouskos, L., Iemmi, F., Liechti, S., Maier, B., Mikuni, V., & Qu, H. (2022). Optimal transport for a global event description at high-intensity hadron colliders. arXiv preprint arXiv:2211.02029. https://arxiv.org/abs/2211.02029
Published in arXiv, 2023
Many particle physics datasets like those generated at colliders are described by continuous coordinates. Read more
Recommended citation: Mikuni, V., Nachman, B., Pettee, M. (2023). Fast Point Cloud Generation with Diffusion Models in High Energy Physics. arXiv preprint arXiv:2304.01266. https://arxiv.org/abs/2304.01266
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