Dustin Anderson

Comparison of the Z/γ ∗ + jets to γ + jets cross sections in pp collisions at √s= 8 TeV

A bstractA comparison of the differential cross sections for the processes Z/γ* + jets and photon (γ)+jets is presented. The measurements are based on data collected with the CMS detector at s=8

The HEP.TrkX Project: Deep Learning for Particle Tracking

\sqrt{s}=8

On timing properties of LYSO-based calorimeters

TeV corresponding to an integrated luminosity of 19.7 fb−1. The differential cross sections and their ratios are presented as functions of pT. The measurements are also shown as functions of the jet multiplicity. Differential cross sections are obtained as functions of the ratio of the Z/γ*pT to the sum of all jet transverse momenta and of the ratio of the Z/γ*pT to the leading jet transverse momentum. The data are corrected for detector effects and are compared to simulations based on several QCD calculations.

The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking

We present studies on the performance and characterization of the time resolution of LYSO-based calorimeters. Results for an LYSO sampling calorimeter and an LYSO-tungsten Shashlik calorimeter are presented. We demonstrate that a time resolution of 30 ps is achievable for the LYSO sampling calorimeter. We discuss timing calorimetry as a tool for mitigating the effects due to the large number of simultaneous interactions in the high luminosity environment foreseen for the Large Hadron Collider.

Precision timing calorimeter for high energy physics

Charged particle reconstruction in dense environments, such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC) is a challenging pattern recognition problem. Traditional tracking algorithms, such as the combinatorial Kalman Filter, have been used with great success in HEP experiments for years. However, these state-of-the-art techniques are inherently sequential and scale quadratically or worse with increased detector occupancy. The HEP.TrkX project is a pilot project with the aim to identify and develop cross-experiment solutions based on machine learning algorithms for track reconstruction. Machine learning algorithms bring a lot of potential to this problem thanks to their capability to model complex non-linear data dependencies, to learn effective representations of high-dimensional data through training, and to parallelize easily on high-throughput architectures such as FPGAs or GPUs. In this paper we present the evolution and performance of our recurrent (LSTM) and convolutional neural networks moving from basic 2D models to more complex models and the challenges of scaling up to realistic dimensionality/sparsity.

Precision Timing Measurements for High Energy Photons

Current and future high energy physics particle colliders are capable to provide instantaneous luminosities of 1034cm-2s-1 and above. The high center of mass energy, the large number of simultaneous collision of beam particles in the experiments and the very high repetition rates of the collision events pose huge challenges. They result in extremely high particle fluxes, causing very high occupancies in the particle physics detectors operating at these machines. To reconstruct the physics events, the detectors have to make as much information as possible available on the final state particles. We briefly discuss how timing information with a precision of around 10 ps and below can aid the reconstruction of the physics events under such challenging conditions. We discuss different detector concepts which can provide time measurements for charged particles and photons with a precision in the range of a few 10 ps. We present in detail updated measurements utilizing a Lutetium-yttrium oxyorthosilicate (LYSO) based calorimeter prototype. With an improved understanding of the signal creation, light propagation and detection characteristics we achieve a precision of down to 30 ps for electrons with energies of 30 GeV. Further we present beam test measurements with a multichannel plate based detectors and studies using silicon detectors. We discuss possible implementations based on these different technologies in a large scale particle physics detector.