Background Estimation with Neural AutoRegressive Flows

Description

Data driven background estimation is crucial for many scientific searches, including searches for new phenomena in experimental datasets. Neural autoregressive flows (NAF) is a deep generative model that can be used for general transformations, and is therefore attractive for this application.

The MLBENDER project focuses on studying how to develop such transformations that can be learned and applied to a region of interest.

Duration

Total project length: 175 hours.

Task ideas

Expected results

Test

Please use this link to access the test for this project.

Requirements

Python, C++, and some previous experience in Machine Learning.

Mentors

Please DO NOT contact mentors directly by email. Instead, please email ml4-sci@cern.ch with Project Title and include your CV and test results. The mentors will then get in touch with you.

Corresponding Project

Participating Organizations