Bulletin of the American Physical Society
APS April Meeting 2017
Volume 62, Number 1
Saturday–Tuesday, January 28–31, 2017; Washington, DC
Session R9: LHC: Ongoing |
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Sponsoring Units: DPF Chair: Mike Tuts, Columbia University Room: Roosevelt 1 |
Monday, January 30, 2017 10:45AM - 10:57AM |
R9.00001: Alignment of the Muon System at the CMS Experiment Ryan Mueller, Luca Perniè, Yuriy Pakhotin, Teruki Kamon, Alexei Safonov, Malachi Brown The muon detectors of the CMS experiment provide fast trigger decisions, muon identifications and muon track measurements. Alignment of the muon detectors is crucial for accurate reconstruction of events with high pT muons that are present in signatures for many new physics scenarios. The muon detector’s relative positions and orientations with respect to the inner silicon tracker may be precisely measured using reconstructed tracks propagating from the interaction point. This track-based alignment procedure is capable of aligning individual muon detectors to within 100 microns along sensitive modes. However, weak (insensitive) modes may not be well measured due to the system’s design and cause systematic miss-measurements. In this report, we present a new track-based procedure which enables all 6 alignment parameters - 3 positions and 3 rotations for each individual muon detector. The improved algorithm allows for measurement of weak modes and considerably reduced related systematic uncertainties. We describe results of the alignment procedure obtained with 2016 data. [Preview Abstract] |
Monday, January 30, 2017 10:57AM - 11:09AM |
R9.00002: Electron Identification with the ATLAS detector Lucas Flores Electron identification for $pp$ collisions at $\sqrt{s}=$ 13 TeV with the ATLAS detector is performed using a likelihood based method both online in the trigger and offline. The likelihood is an improvement on the cut based method. A multivariate analysis technique (MVA), the likelihood method is able to simultaneously assess many characteristics before making a decision. This likelihood is constructed from variables that discriminate between electrons and backgrounds. As some of these variables depend on the number of pileup collisions per bunch crossing, it is advantageous to have an event-by-event measure of the number of pileup collisions. This can be used to adjust the likelihood discriminant value as a function of the pileup to ensure that the electron identification remains efficient at high pileup, without drastically increasing the amount of background accepted. This is currently being done with the number of primary vertices offline and the average number of collisions online as the measures for pileup. A potential improvement presented here is to measure the pileup with the TRT local Track Occupancy, which is a more local descriptor of activity around an electron candidate and is available both online and offline. [Preview Abstract] |
Monday, January 30, 2017 11:09AM - 11:21AM |
R9.00003: Improvement of the CMS Muon Reconstruction Performance due to Precise Track-based Alignment Malachi Brown The performance of the CMS muon system depends on a precise knowledge of the positions and orientations of all its elements. The muon tracks reconstructed in proton-proton collision data at the LHC are used to align the individual muon detectors with respect to the inner silicon tracker. The alignment procedure measures these positions and provides geometries of the muon system that must be validated to ensure the performance of the detectors. In this report we present a set of sophisticated validation tools, developed to test the accuracy of a given muon system geometry with data from collisions. The validation procedure uses events with pairs of muons from Z-boson decays and events with very high pT muons, in order to quantify the reconstruction performance of the muon system for a given geometry. Kinematic properties of muons reconstructed using information from the muon system are compared to the properties of muons built using information from the tracker. We demonstrate improvements of the muon reconstruction performance after track-based alignment procedures are performed with 2016 data. [Preview Abstract] |
Monday, January 30, 2017 11:21AM - 11:33AM |
R9.00004: Performance of b jet identification {\$}pp{\$} collisions at {\$}$\backslash $sqrt\textbraceleft s\textbraceright $=${\$} 13 TeV with the ATLAS detector Wasikul Islam The efficient identification of jets from bottom quarks (b-jets) is one of the~most important techniques for many physics analyses at the Large Hadron Collider, including studies of the Higgs boson, the top quark, and searches beyond the Standard Model.~ The performance is characterized by b-tagging efficiency (probability to identify a b-jet as such) and the mistag rate (probability to mistakenly accept a non-b-jet). Mistags occur as a result of finite detector resolution, presence of long-lived particles, and material interactions.~ As these effects can be different between the experimental data and simulation, it is important to measure the b-tagging performance in data and derive the correction factors for the simulation.~ I will describe various methods to measure the performance of b jet identification in {\$}pp{\$} collisions~at {\$}$\backslash $sqrt\textbraceleft s\textbraceright $=${\$} 13 TeV with the ATLAS detector. [Preview Abstract] |
Monday, January 30, 2017 11:33AM - 11:45AM |
R9.00005: Improvement of Event Synchronization in the ATLAS Pixel Readout Development Logan Adams As the LHC continues in Run2, the B-Layer still uses the Atlas-SiROD Pixel readout system initially developed for Run 1. The higher luminosity occurring during Run 2 results in higher occupancy causing increased desynchronization errors in the Pixel Readout. In order to ensure lasting operation of the B-Layer until it is replaced after Run 3, changes were made to the firmware and software to add debug capabilities to identify when the errors are crossing certain thresholds and change the internal control logic accordingly. These features also allow for better debugging of the Event Counter Reset addition to the firmware. This talk will focus on the features implemented and measurements to demonstrate the positive impact on the Pixel DAQ system. A Pixel front-end chip emulator which can be used for readout system development beyond Run 3 will also be discussed. [Preview Abstract] |
Monday, January 30, 2017 11:45AM - 11:57AM |
R9.00006: ATLAS trigger operations: Monitoring with ``Xmon'' rate prediction system Andrew Aukerman, Tae Min Hong We present the operations and online monitoring with the ``Xmon'' rate prediction system for the trigger system at the ATLAS Experiment. A two-level trigger system reduces the LHC's bunch-crossing rate, 40 MHz at design capacity, to an average recording rate of about 1 kHz, while maintaining a high efficiency of selecting events of interest. The Xmon system uses the luminosity value to predict trigger rates that are, in turn, compared with incoming rates. The predictions rely on past runs to parameterize the luminosity dependency of the event rate for a trigger algorithm. Some examples are given to illustrate the performance of the tool during recent operations. [Preview Abstract] |
Monday, January 30, 2017 11:57AM - 12:09PM |
R9.00007: ATLAS Muon Identification and Reconstruction Performance in 2016 Laura Bergsten This talk aims to describe the ATLAS muon identification and reconstruction performance in 2016. Reconstruction and isolation efficiencies as well as the transverse momentum scale and transverse momentum resolution are measured from LHC proton-proton collision data recorded by ATLAS in 2016 at a center-of-mass-energy of 13 TeV. The measurements are performed by studying the abundantly produced and well known $J/\Psi$ and $Z$-boson resonances decaying into two oppositely-charged muons. Muon momenta ranging from $5$ to a few hundred GeV and covering the pseudo-rapidity range $|\eta| < 2.5$ are found to be in good agreement with tuned detector simulations. Furthermore, the performance of the detector for data collected in 2016 is compared to the performance for data collected during 2015. [Preview Abstract] |
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