arXiv subjects

David Lawrence

Publications and source records attributed to David Lawrence.

21 records · Page 2Linked to original sources

arXiv2018

Construction and Performance of the Barrel Electromagnetic Calorimeter for the GlueX Experiment

The barrel calorimeter is part of the new spectrometer installed in Hall D at Jefferson Lab for the GlueX experiment. The calorimeter was installed in 2013, commissioned in 2014 and has been operating routinely since early 2015. The detector configuration, associated Monte Carlo simulations, calibration and operational performance are described herein. The calorimeter records the time and energy deposited by charged and neutral particles created by a multi-GeV photon beam. It is constructed as a lead and scintillating-fiber calorimeter and read out with 3840 large-area silicon photomultiplier arrays. Particles impinge on the detector over a wide range of angles, from normal incidence at 90 degrees down to 11.5 degrees, which defines a geometry that is fairly unique among calorimeters. The response of the calorimeter has been measured during a running experiment and performs as expected for electromagnetic showers below 2.5 GeV. We characterize the performance of the BCAL using the energy resolution integrated over typical angular distributions for $π^0$ and $η$ production of $σ_E/E$=5.2\%/$\sqrt{E(\rm{GeV})} \oplus$ 3.6\% and a timing resolution of $σ$\,=\,150\,ps at 1\,GeV.

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arXiv2017

StarCraft II: A New Challenge for Reinforcement Learning

This paper introduces SC2LE (StarCraft II Learning Environment), a reinforcement learning environment based on the StarCraft II game. This domain poses a new grand challenge for reinforcement learning, representing a more difficult class of problems than considered in most prior work. It is a multi-agent problem with multiple players interacting; there is imperfect information due to a partially observed map; it has a large action space involving the selection and control of hundreds of units; it has a large state space that must be observed solely from raw input feature planes; and it has delayed credit assignment requiring long-term strategies over thousands of steps. We describe the observation, action, and reward specification for the StarCraft II domain and provide an open source Python-based interface for communicating with the game engine. In addition to the main game maps, we provide a suite of mini-games focusing on different elements of StarCraft II gameplay. For the main game maps, we also provide an accompanying dataset of game replay data from human expert players. We give initial baseline results for neural networks trained from this data to predict game outcomes and player actions. Finally, we present initial baseline results for canonical deep reinforcement learning agents applied to the StarCraft II domain. On the mini-games, these agents learn to achieve a level of play that is comparable to a novice player. However, when trained on the main game, these agents are unable to make significant progress. Thus, SC2LE offers a new and challenging environment for exploring deep reinforcement learning algorithms and architectures.

cs.LG↗

arXiv2013

A measurement of the energy and timing resolution of GlueX Forward Calorimeter using an electron beam

The performance of the GlueX Forward Calorimeter was studied using a small version of the detector and a variable energy electron beam derived from the Hall B tagger at Jefferson Lab. For electron energies from 110 MeV to 260 MeV, which are near the lower-limits of the design sensitivity, the fractional energy resolution was measured to range from 20% to 14%, which meets the design goals. The use of custom 250 MHz flash ADCs for readout allowed precise measurements of signal arrival times. The detector achieved timing resolutions of 0.38 ns for a single 100 mV pulse, which will allow timing discrimination of photon beam bunches and out-of-time background during the operation of the GlueX detector.

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