1 code implementation • 23 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.
1 code implementation • 10 Jul 2023 • Fernando Torales Acosta, Vinicius Mikuni, Benjamin Nachman, Miguel Arratia, Bishnu Karki, Ryan Milton, Piyush Karande, Aaron Angerami
Score based generative models are a new class of generative models that have been shown to accurately generate high dimensional calorimeter datasets.
1 code implementation • 6 Jun 2023 • Vinicius Mikuni, Benjamin Nachman
Methods for anomaly detection of new physics processes are often limited to low-dimensional spaces due to the difficulty of learning high-dimensional probability densities.
1 code implementation • 17 Jun 2022 • Vinicius Mikuni, Benjamin Nachman
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.
no code implementations • 15 Mar 2022 • Andreas Adelmann, Walter Hopkins, Evangelos Kourlitis, Michael Kagan, Gregor Kasieczka, Claudius Krause, David Shih, Vinicius Mikuni, Benjamin Nachman, Kevin Pedro, Daniel Winklehner
The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources.
1 code implementation • 11 Nov 2021 • Vinicius Mikuni, Benjamin Nachman, David Shih
There is a growing need for anomaly detection methods that can broaden the search for new particles in a model-agnostic manner.
1 code implementation • 9 Feb 2021 • Vinicius Mikuni, Florencia Canelli
Methods for processing point cloud information have seen a great success in collider physics applications.
no code implementations • 28 Sep 2020 • Vinicius Mikuni, Florencia Canelli
We propose a new method for Unsupervised clustering in particle physics named UCluster, where information in the embedding space created by a neural network is used to categorise collision events into different clusters that share similar properties.
Data Analysis, Statistics and Probability High Energy Physics - Experiment
2 code implementations • 13 Jan 2020 • Vinicius Mikuni, Florencia Canelli
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.
Data Analysis, Statistics and Probability High Energy Physics - Phenomenology