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.
Total project length: 175 hours.
Python, C++, and some previous experience in Machine Learning.
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