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

• Implement a Neural Autoregressive Flow (NAF) model to estimate the background distribution and apply it to a representative physics analysis searching for a resonance excess over a smooth background.

Expected results

• Results from training Neural Autoregressive Flow model to estimate the background on a physics analysis application searching for a resonance over a smooth background.

Test

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

Requirements

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

Mentors

• Sergei Gleyzer (University of Alabama)
• Meenakshi Narain (Brown University)
• Ulrich Heintz (Brown University)
• Aneesh Heintz (Cornell University)
• Daniel Li (Brown University)
• Emanuele Usai (Brown University)

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

• MLBENDER

Participating Organizations

• Alabama
• Brown
• Cornell

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