Volume 422 -
The Tenth Annual Conference on Large Hadron Collider Physics (LHCP2022) -
Poster Session
Constraining Deep Neural Network classifiers’ systematic uncertainty via input feature space reduction
Full text:
pdf
Pre-published on:
March 27, 2023
Published on:
June 21, 2023
Abstract
In this work, we show how using a sub-optimal set of input features can lead to higher systematic uncertainty associated with Deep Neural Network classifier predictions. For this study, we considered the case of highly boosted di-jet resonances produced in pp collisions decaying to two b-quarks to be selected against an overwhelming QCD background. Results from a Monte Carlo simulation with HEP pseudo-detectors are shown.
DOI: https://doi.org/10.22323/1.422.0242
How to cite
Metadata are provided both in "article" format (very similar to INSPIRE) as this helps creating very compact bibliographies which can be beneficial to authors and readers, and in "proceeding" format which is more detailed and complete.
Open Access
Copyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.