Speaker
Description
At the High Luminosity Large Hadron Collider (HL-LHC), many
proton-proton collisions happen during a single bunch crossing. This
leads on average to tens of thousands of particles emerging from the
interaction region. Two major factors impede finding charged particle
trajectories from measured hits in the tracking detectors. First,
deciding whether a given set of hits was produced by a common particle
is an under-specified task. State-of-the-art reconstruction models
usually tackle this issue via so-called track following only at a
later stage after considering many hits. Second, assuming a nearly
perfect hit-particle decision function, constructing possible hit
combinations to their compatibility using this decision function is a
combinatorial problem. Thus, the traditional approach will grow
exponentially as the number of simultaneous collisions increase at the
HL-LHC and pose a major computational challenge.
We propose a framework for Similarity Hashing and Learning for Track
Reconstruction (SHLTR) where multiple small regions of the detector
are reconstructed in parallel with minimal fake rate. We use hashing
techniques to separate the detector search space into buckets. The
particle purity of these buckets, i.e. how many hits from the same
particle are contained, is increased using locality sensitivity in
feature space where per-hit features beyond just its position are
considered. The bucket size is sufficiently small to significantly
reduce the complexity of track reconstruction within the buckets or
regions.
A neural network selects valid combinations in the buckets and builds
up full trajectories by connected components search independently of
global positions of the hits and detector geometry. The whole process
occurs simultaneously in the multiple regions of the detector and
curved particles are found by allowing buckets to overlap. We present
first results of such a track reconstruction chain including
efficiency, fake estimates, and computational performances in µ=200
datasets.
| Consider for promotion | Yes |
|---|
