STI Publications - View Publication Form #16535
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Publication Information
Title | Integration of a high-acceptance detector into a high-luminosity collider | ||||
Abstract | Modern colliders push not only the luminosity limits but the detector capabilities as well. Full reconstruction of a collision event requires information about all collision products including those propagating close to the colliding beams. Development of such a detector region design poses unique challenges not only from each of the machine and detector points of view separately but from the machine-detector interface point of view as well where there are no clear boundaries between the detector region and machine components. We present a self-consistent scheme for integration of a nearly-full acceptance detector into an asymmetric high-luminosity collider. This is the first time when a detector region must simultaneously include and reconcile such a large combination of new and conventional design features. They include a near-beam detection system, polarized crabbed beams with a tens-of-mrads crossing angle, strong detector solenoid as well as multiple design aspects of a high-lumin | ||||
Author(s) | Guohui Wei, Bamunuvita Gamage, Fanglei Lin, Vasiliy Morozov, Fulvia Pilat, Yuhong Zhang, Y. Nosochkov, M.-H. Wang | ||||
Publication Date | To Appear in 2021 | ||||
Category | Beam Dynamics | ||||
Document Type | Journal Article | ||||
Primary Institution | Thomas Jefferson National Accelerator Facility, Newport News | ||||
Affiliation | Accelerator Ops, R&D / Cntr-Adv Studies of Acce / Ctr for Adv Stud of Accel | ||||
Funding Source | Nuclear Physics (NP) | ||||
Proprietary? | No | ||||
This publication conveys | Technical Science Results | ||||
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Associated with an experiment | No | ||||
Associated with EIC | No | ||||
Supported by Jefferson Lab LDRD Funding | No |
Journal Article
Journal Name | Physical Review Accelerators and Beams |
Refereed | No |
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Issue | |
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Attachments/Datasets/DOI Link
Document(s) |
Integration_Detector_Journal2.pdf
(STI Document)
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Dataset(s) | (none) |
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