Conveners
Reconstruction
- Christina Peters (University of Delaware)
- Joel Walker (Sam Houston State University)
Reconstruction
- Julia Lynne Gonski (Columbia University (US))
- Etienne Dreyer (Weizmann Institute of Science (IL))
The particle-flow (PF) algorithm is of central importance to event reconstruction at the CMS detector, and has been a focus of developments in light of planned Phase-2 running conditions with an increased pileup and detector granularity. Current rule-based implementations rely on extrapolating tracks to the calorimeters, correlating them with calorimeter clusters, subtracting charged energy...
The reconstruction and calibration of hadronic final states in the ATLAS detector present complex experimental challenges. For isolated pions in particular, classifying $\pi^0$ versus $\pi^{\pm}$ and calibrating pion energy deposits in the ATLAS calorimeters are key steps in the hadronic reconstruction process. The baseline methods for local hadronic calibration were optimized early in the...
Particle reconstruction is a task underlying virtually all analyses of collider-detector data. Recently, the application of deep learning algorithms on graph-structured low-level features has suggested new possibilities beyond the scope of traditional parametric approaches. In particular, we explore the possibility to reconstruct and classify individual neutral particles in a collimated...
Hadronic jets and missing transverse energy are key experimental probes when searching for new physics or performing standard model precision measurements in collision events at the LHC. In this work, we propose a graph neural network algorithm for obtaining a global event description that demonstrates greatly improved resolution in the aforementioned objects obtained with a fast simulation of...
We present ν-Flows, a novel method for restricting the likelihood space of neutrino kinematics in high energy collider experiments using conditional normalizing flows and deep invertible neural networks.
This method allows the recovery of the full neutrino momentum, which is usually left as a free parameter, and permits one to sample neutrino values under a learned conditional likelihood...
We have been studying the use of deep neural networks (DNNs) to identify and locate primary vertices (PVs) in proton-proton collisions at the LHC. Earlier work focused on finding primary vertices in simulated LHCb data using a hybrid approach that started with kernel density estimators (KDEs) derived from the ensemble of charged track parameters and predicted “target histograms” from which...
In a decade from now, the Upgrade II of LHCb experiment will face an instantaneous luminosity ten times higher than in the current Run 3 conditions. This will bring LHCb to a new era, with huge event sizes and typically several signal heavy-hadron decays per event. The trigger scope will shift from deciding ‘which events are interesting?’ to ‘which parts of the event are interesting?’. To...
Calorimetric muon energy estimation in high-energy physics is an example of a likelihood-free inference (LFI) problem, where simulators that implicitly encode the likelihood function are used to mimic complex particle interactions at different values of the physical parameters. Recently, Kieseler et al. (2022) exploited simulated measurements from a dense, finely segmented calorimeter to infer...
We develop a nearest neighbor algorithm for regressor for the problem of estimating the energy of multi-TeV muons in a high-granularity calorimeter, exploiting the pattern of soft photon deposits around the muon track. The algorithm is heavily overparametrized by assigning weights and biases to the training events. Parameters are learnt by batch gradient descent. The performance compares...
We describe a new scale-invariant jet clustering algorithm which does not impose a fixed cone size on the event. The proposed construction unifies fat-jet finding, substructure axis-finding, and recursive filtering of soft wide-angle radiation into a single procedure. The sequential clustering measure history facilitates high-performance substructure tagging with a boosted decision tree. ...
