A Phenomenological Cost Model for High Energy Particle Accelerators
arXiv:1404.4097 · doi:10.1088/1748-0221/9/07/T07002
Abstract
Accelerator-based high-energy physics have been in the forefront of scientific discoveries for more than half a century. The accelerator technology of the colliders has progressed immensely, while the beam energy, luminosity, facility size, and cost have grown by several orders of magnitude. The method of colliding beams has not fully exhausted its potential but has slowed down considerably in its progress. In this paper we derive a simple scaling model for the cost of large accelerators and colliding beam facilities based on costs of 17 big facilities which have been either built or carefully estimated. Although this approach cannot replace an actual cost estimate based on an engineering design, this parameterization is to indicate a somewhat realistic cost range for consideration of what future frontier accelerator facilities might be fiscally realizable.
arXiv comment15 pages, 4 figures
References in corpus (7)
- High energy particle colliders: past 20 years, next 20 years and beyond
- CLIC e+e- Linear Collider Studies
- When will we know a muon collider is feasible? Status and directions of muon accelerator R&D
- On Phenomenology of Complex Scientific Systems
- TLEP, first step in a long-term vision for HEP
- Proton-proton and electron-positron collider in a 100 km ring at Fermilab
- Assessing Risk in Costing High-energy Accelerators: from Existing Projects to the Future Linear Collider
Cited by in corpus (8)
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- Report of the Snowmass 2021 Collider Implementation Task Force
- Fermilab Proton Accelerator Complex Status and Improvement Plans
- The socio-economic impact of a breakthrough in the particle accelerators' technology: a research agenda
- CEPC Cost Model Study and Circumference Optimization