Andrew Glenn
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Research Interests
Andrew Glenn has a total of 1000 co-authors. Here are the top co-authors by number of shared publications:
- Kenta Shigaki
Hiroshima University
160 shared publications Y. Kwon
151 shared publicationsA. Oskarsson
Department of Physics Lund University Box 118 SE-221 00 Lund Sweden
150 shared publicationsH. Hamagaki
150 shared publicationsA. Khanzadeev
149 shared publicationsT. Chujo
149 shared publications
Latest Publications
- Open Access
- 13 March 2020
- Published by American Physical Society (APS) in Physical Review D
- Vol. 101 (5) , 052006
and production at forward rapidity in collisions atThe PHENIX experiment at the Relativistic Heavy Ion Collider has measured the differential cross section, mean transverse momentum, mean transverse momentum squared of inclusive , and cross section ratio of to at forward rapidity in collisions at via the dimuon decay channel. Comparison is made to inclusive cross sections measured at and 2.76–13 TeV. The result is also compared to leading-order nonrelativistic QCD calculations coupled to a color-glass-condensate description of the low- gluons in the proton at low transverse momentum () and to next-to-leading order nonrelativistic QCD calculations for the rest of the range. These calculations overestimate the data at low . While consistent with the data within uncertainties above , the calculations are systematically below the data. The total cross section times the branching ratio is BR .- 13 March 2020
- Published by American Physical Society (APS) in Physical Review D
- Vol. 101 (5) , 052006
Open Accessand production at forward rapidity in collisions atThe PHENIX experiment at the Relativistic Heavy Ion Collider has measured the differential cross section, mean transverse momentum, mean transverse momentum squared of inclusive , and cross section ratio of to at forward rapidity in collisions at via the dimuon decay channel. Comparison is made to inclusive cross sections measured at and 2.76–13 TeV. The result is also compared to leading-order nonrelativistic QCD calculations coupled to a color-glass-condensate description of the low- gluons in the proton at low transverse momentum () and to next-to-leading order nonrelativistic QCD calculations for the rest of the range. These calculations overestimate the data at low . While consistent with the data within uncertainties above , the calculations are systematically below the data. The total cross section times the branching ratio is BR . - 1 February 2020
- Published by Elsevier in Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
- Vol. 954, 161204
Comparison of pulse shape discrimination performance of stilbene and liquid scintillator under high count-rate active interrogation conditions
- 1 February 2020
- Published by Elsevier in Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
- Vol. 954, 161204
Comparison of pulse shape discrimination performance of stilbene and liquid scintillator under high count-rate active interrogation conditions
- 25 July 2019
- Published by Wiley in Statistical Analysis and Data Mining: An ASA Data Science Journal
- Vol. 12 (6) , 479-488
Gaussian mixture models as automated particle classifiers for fast neutron detectors
Pulse shape discrimination (PSD) is the task of classifying electronic pulse shapes for different particle types such as gamma rays and fast neutrons interacting in scintillators and read out by photo sensitive detectors. This field has been limited in its adoption of techniques found in the statistical learning community. Methods initially employed in the 1960s for analog electronic circuitry persist in the current PSD literature describing operations performed on digitized pulses, which are amenable to statistical rigor. Despite vast amounts of data collected at low energy levels, traditional PSD methods are unable to discriminate particles below a certain threshold. In this work, Gaussian mixture models (GMMs) are used as a clustering technique for fast neutron detection in the absence of labeled data. GMMs yield improvements spanning the energy spectrum in a desirably efficient, unsupervised fashion. An extension, the Dirichlet Process GMM, provides further flexibility and classification improvement.
- 25 July 2019
- Published by Wiley in Statistical Analysis and Data Mining: An ASA Data Science Journal
- Vol. 12 (6) , 479-488
Gaussian mixture models as automated particle classifiers for fast neutron detectors
Pulse shape discrimination (PSD) is the task of classifying electronic pulse shapes for different particle types such as gamma rays and fast neutrons interacting in scintillators and read out by photo sensitive detectors. This field has been limited in its adoption of techniques found in the statistical learning community. Methods initially employed in the 1960s for analog electronic circuitry persist in the current PSD literature describing operations performed on digitized pulses, which are amenable to statistical rigor. Despite vast amounts of data collected at low energy levels, traditional PSD methods are unable to discriminate particles below a certain threshold. In this work, Gaussian mixture models (GMMs) are used as a clustering technique for fast neutron detection in the absence of labeled data. GMMs yield improvements spanning the energy spectrum in a desirably efficient, unsupervised fashion. An extension, the Dirichlet Process GMM, provides further flexibility and classification improvement.