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Shower Separation in Five Dimensions for Highly Granular Calorimeters using Machine Learning / CALICE Collaboration
To achieve state-of-the-art jet energy resolution for Particle Flow, sophisticated energy clustering algorithms must be developed that can fully exploit available information to separate energy deposits from charged and neutral particles. Three published neural network-based shower separation models were applied to simulation and experimental data to measure the performance of the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL) technological prototype in distinguishing the energy deposited by a single charged and single neutral hadron for Particle Flow. [...]
arXiv:2407.00178.- 2024-10-24 - 29 p. - Published in : JINST 19 (2024) P10027 Fulltext: 2407.00178 - PDF; document - PDF;

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Software Compensation for Highly Granular Calorimeters using Machine Learning / CALICE Collaboration
A neural network for software compensation was developed for the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL). The neural network uses spatial and temporal event information from the AHCAL and energy information, which is expected to improve sensitivity to shower development and the neutron fraction of the hadron shower. [...]
arXiv:2403.04632.- 2024-04 - 27 p. - Published in : JINST 19 (2024) P04037 Fulltext: 2403.04632 - PDF; Publication - PDF;

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Design, Construction and Commissioning of a Technological Prototype of a Highly Granular SiPM-on-tile Scintillator-Steel Hadronic Calorimeter / CALICE Collaboration
The CALICE collaboration is developing highly granular electromagnetic and hadronic calorimeters for detectors at future energy frontier electron-positron colliders. After successful tests of a physics prototype, a technological prototype of the Analog Hadron Calorimeter has been built, based on a design and construction techniques scalable to a collider detector. [...]
arXiv:2209.15327; CALICE-PUB-2022-003.- 2023-11-22 - 37 p. - Published in : JINST 18 (2023) P11018 Fulltext: 2209.15327 - PDF; document - PDF;

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Structural Adaptions in a Membrane Enzyme that Terminates Endocannabinold Signaling / Bracey, M H ; Hanson, M A ; Masuda, K R ; Stevens, R C ; Cravatt, B F
SLAC-REPRINT-2002-233.- Stanford, CA : SLAC, 2002 - Published in : Science 298 (2002) 1793

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