Connecting The Dots 2018

Session

Young Scientist Forum

21 Mar 2018, 16:10

Physics-Astronomy Auditorium A118 (University of Washington Seattle)

Conveners

Young Scientist Forum: YSF

• Gordon Watts (University of Washington (US))

6. Clustering with adaptive similarity measure for track reconstruction

Sabrina Amrouche (RSA - Universite de Geneve (CH))

21/03/2018, 16:10

3: Machine learning approaches

Oral

The track reconstruction task of ATLAS and CMS will become computationally increasingly challenging with the LHC high luminosity upgrade. In the context of taking advantage of machine learning techniques, a clustering algorithm is proposed to group together hits that belong to the same particle. Clustering is referred to as unsupervised classification and is widely applied to big data. The...

13. Implementation and performance of the ATLAS pixel clustering neural networks

Louis-Guillaume Gagnon (Universite de Montreal (CA))

21/03/2018, 16:30

5: Advanced usage of tracks

Oral

The high particle densities produced by the Large Hadron Collider (LHC) mean that in the ATLAS pixel detector the clusters of deposited charge start to merge. A neural network-based approach is used to estimate the number of particles contributing to each cluster, and to accurately estimate the hit positions even in the presence of multiple particles. This talk or poster will thoroughly...

22. Splitting Strip Detector Clusters in Dense Environments

Ben Nachman (Lawrence Berkeley National Lab. (US))

21/03/2018, 16:50

5: Advanced usage of tracks

Oral

Tracking in high density environments, particularly in high energy jets, plays an important role in many physics analyses at the LHC. In such environments, there is significant degradation of track reconstruction performance. Between runs 1 and 2, ATLAS implemented an algorithm that splits pixel clusters originating from multiple charged particles, using charge information, resulting in the...

17. Tracking in Dense Environments for the HL-LHC ATLAS Detector

Felix Cormier (University of British Columbia (CA))

21/03/2018, 17:10

5: Advanced usage of tracks

Oral

Tracking in dense environments, such as in the cores of high-energy jets, will be key for new physics searches as well as measurements of the Standard Model at the High Luminosity LHC (HL-LHC). The HL-LHC will operate in challenging conditions with large radiation doses and high pile-up (up to $\mu$=200). The current tracking detector will be replaced with a new all-silicon Inner Tracker for...

45. Track Finding in the COMET Experiment using Boosted Decision Trees

Ewen Gillies (Imperial College London)

21/03/2018, 17:30

3: Machine learning approaches

Oral

The Coherent Muon to Electron Transition (COMET) experiment is designed to search for muon to electron conversion, a process which has very good sensitivity to Beyond the Standard Model physics. The first phase of the experiment is currently under construction at J-PARC. This phase is designed to probe muon to electron conversion 100 times better than the current limit. The experiment will...