Search Results for author: Vinicius Mikuni

Found 11 papers, 8 papers with code

The Landscape of Unfolding with Machine Learning

no code implementations29 Apr 2024 Nathan Huetsch, Javier Mariño Villadamigo, Alexander Shmakov, Sascha Diefenbacher, Vinicius Mikuni, Theo Heimel, Michael Fenton, Kevin Greif, Benjamin Nachman, Daniel Whiteson, Anja Butter, Tilman Plehn

Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions.

Unifying Simulation and Inference with Normalizing Flows

1 code implementation29 Apr 2024 Haoxing Du, Claudius Krause, Vinicius Mikuni, Benjamin Nachman, Ian Pang, David Shih

There have been many applications of deep neural networks to detector calibrations and a growing number of studies that propose deep generative models as automated fast detector simulators.


Improving Generative Model-based Unfolding with Schrödinger Bridges

1 code implementation23 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.

Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation

1 code implementation10 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.

High-dimensional and Permutation Invariant Anomaly Detection

1 code implementation6 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.

Anomaly Detection Density Estimation

Score-based Generative Models for Calorimeter Shower Simulation

1 code implementation17 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.

Online-compatible Unsupervised Non-resonant Anomaly Detection

1 code implementation11 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.

Anomaly Detection

Point Cloud Transformers applied to Collider Physics

1 code implementation9 Feb 2021 Vinicius Mikuni, Florencia Canelli

Methods for processing point cloud information have seen a great success in collider physics applications.

BIG-bench Machine Learning Jet Tagging

Unsupervised clustering for collider physics

no code implementations28 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

ABCNet: An attention-based method for particle tagging

2 code implementations13 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

Cannot find the paper you are looking for? You can Submit a new open access paper.