Project
BigDataHEP: Understanding Big Data in High Energy Physics: finding a needle in many haystacks

Code PTDC/FIS-PAR/29147/2017, POCI/01-0145-FEDER-029147

Beneficiary Entity

LIP - Laboratório de Instrumentação e Física Experimental de Partículas

Project summary

Despite of the tremendous success of the experimental confirmation of the Standard Model of Particle Physics, which culminated with the Higgs boson discovery by the Large Hadron Collider (LHC) experiments in 2012, there are still a significant number of unanswered questions. Important points, such as description of gravitational interactions in unified form with the other fundamental interactions, the fermion mass hierarchy, the number of fermion generations, the matter-antimatter asymmetry and the cosmological evidence for dark matter and dark energy, are not yet successfully explained. The large amount of data yet to be collected at the LHC, as well as the new generation of experiments attempting the direct detection of dark matter provide a unique opportunity to probe the current paradigm of Particle Physics and test hypotheses to go beyond it. Such purpose, however, requires the ability to fully exploit the available experimental data, since an efficient analysis might represent the difference between being able, or not, to observe subtle new physics effects or to probe rare properties of the matter.

The proposed project aims to develop complex analysis techniques to be used both in proton-proton and heavy ions collision data at the LHC and in the data collected by the LZ experiment. The goal is not to perform the data analysis itself, since this will be done by the experimental collaborations, but to develop new methods and tools to be used in such context, providing as well the computational tools required to efficiently process large datasets. Such data processing methods are particularly important in the context of the proposed machine learning techniques, since the training phase of these methods can be particularly challenging. Since some of the team members of this project are also active members of the ATLAS and LZ Collaborations the propagation of such tools to the experimental community is facilitated. Furthermore, this project also aims at using similar data analysis techniques in a different context, namely on the quality control of the industrial production of printed circuit boards. In this context machine learning techniques will be used to establish the source of the contaminants identified by chemical analysis. Some of the team members have previous experience in collaborating with the electronics industry, ensuring that the correct focus will be considered for the industrial application of the proposed methods. Finally, a key aspect of the project is the advanced training and knowledge dissemination in the field of data science.

Support under

Reforçar a investigação, o desenvolvimento tecnológico e a inovação

Region of Intervention

Centro  /  Lisboa  /  Norte

Funding