PoS - Proceedings of Science
Volume 350 - 7th Annual Conference on Large Hadron Collider Physics (LHCP2019) - Parallel Performance
Overview of Machine Learning and Big Data Tools at HEP experiments
A.M. Castañeda Hernández
Full text: pdf
Pre-published on: September 03, 2019
Published on: December 04, 2019
Following the preparation for the High Luminosity era of the Large Hadron Collider (LHC) and the imminent increase on the
frequency of collisions by one order of magnitude it is evident the need for the development and implementation of new tools to
optimize several tasks such as particle identification, reconstruction, data storage and processing.
Many of these implementations will be based on machine learning algorithms that have the potential to process signals in a
smarter way than current technologies allowing to fully exploit the detector capabilities of the LHC experiments and increase
the probability to find new physics phenomena.
DOI: https://doi.org/10.22323/1.350.0234
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
Creative Commons LicenseCopyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.