ML4Jets2020

Session

Experimental methods

17 Jan 2020, 13:00

KC 802 (Kimmel Center for University Life)

Conveners

Experimental methods

• Philip Coleman Harris (Massachusetts Inst. of Technology (US))

14. Deep Learning based Energy Reconstruction and Event Generation for the CALICE AHCAL

Erik Buhmann (Hamburg University (DE))

17/01/2020, 13:00

The CALICE collaboration is developing high-granular calorimeters for the application of particle flow reconstruction to calorimetry in future linear collider experiments. An engineering prototype for an analogue hadron calorimeter (AHCAL) was assembled by the CALICE collaboration. Events measured by the AHCAL include 5-dimensional information in 22k channels: the 3D location, energy and...

31. Convolutional neural networks with event images for pileup mitigation [cancelled due to illness]

Bernard Brickwedde (Johannes Gutenberg Universitaet Mainz (DE))

17/01/2020, 13:20

The addition of multiple, nearly simultaneous proton proton collisions to hard-scatter collisions (in-time pileup) is a significant challenge for most physics analyses at the LHC. Many techniques have been proposed to mitigate the impact of pileup on jets and other reconstructed objects. This study investigates the application of convolutional neural networks to pileup mitigation by treating...

45. Secondary Vertex finding in Jets with Graph Neural Networks

Eilam Gross (Weizmann Institute of Science (IL))

17/01/2020, 13:30

Secondary vertex finding is a crucial task for identifying jets containing heavy flavor hadron decays.
Bottom jets in particular have a very distinctive topology of 𝑏→𝑐→𝑠 decay which gives rise to two secondary vertices with high invariant mass and several associated charged tracks.

Existing secondary vertex finding algorithms search for intersecting particle tracks, and group them into...

50. Mixture Density Networks for tracking in dense environments on ATLAS

Elham E Khoda (University of British Columbia (CA))

17/01/2020, 13:50

The high collision energy and luminosity of the LHC allow to study jets and hadronically-decaying tau leptons at extreme energies with the ATLAS detector. These signatures lead to topologies with charged particles with an angular separation smaller than the size of the ATLAS Inner Detector sensitive elements and consequently to a reduced track reconstruction efficiency. In order to regain part...

26. Deep learning methods to improve Particle Flow reconstruction

Sanmay Ganguly (Weizmann Institute of Science (IL))

17/01/2020, 14:10

Canonical particle flow algorithm tries to estimate neutral energy deposition in calorimeter by first performing a matching between calorimeter deposits and track
direction and subsequently subtracting the track momenta from the matched cluster energy deposition.
We propose a Deep Learning based method
for estimating the energy fraction of individual components for each cell of the...

35. Machine Learning Based Jet $p_{T}$ Reconstruction in ALICE

Hannah Bossi (Yale University (US))

17/01/2020, 14:30

Reconstructing the jet transverse momentum ($p_{\rm T}$) is a challenging task, particularly in heavy-ion collisions due to the large fluctuating background from the underlying event. While ALICE's standard area-based method effectively corrects for the average background, it does not account for region-to-region fluctuations. These residual fluctuations are handled in an unfolding procedure...

70. Beyond 4D Tracking: Cluster Shapes for Track Seeding

Joshua Isaacson (Fermilab)

Tracking is one of the most time consuming aspects of event reconstruction at the Large Hadron Collider (LHC) and its high-luminosity upgrade (HL-LHC). Innovative detector technologies extend tracking to four-dimensions by including timing in the pattern recognition and parameter estimation. However, existing and future hardware already has additional information that is largely unused by...