CosmoSIS: A system for MC parameter estimation
- Univ. of Manchester, Manchester (United Kingdom)
- Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States); Univ. of Chicago, Chicago, IL (United States)
- Univ. of Chicago, Chicago, IL (United States)
- Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
CosmoSIS is a modular system for cosmological parameter estimation, based on Markov Chain Monte Carlo and related techniques. It provides a series of samplers, which drive the exploration of the parameter space, and a series of modules, which calculate the likelihood of the observed data for a given physical model, determined by the location of a sample in the parameter space. While CosmoSIS ships with a set of modules that calculate quantities of interest to cosmologists, there is nothing about the framework itself, nor in the Markov Chain Monte Carlo technique, that is specific to cosmology. Thus CosmoSIS could be used for parameter estimation problems in other fields, including HEP. This paper describes the features of CosmoSIS and show an example of its use outside of cosmology. Furthermore, it also discusses how collaborative development strategies differ between two different communities: that of HEP physicists, accustomed to working in large collaborations, and that of cosmologists, who have traditionally not worked in large groups.
- Research Organization:
- Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), High Energy Physics (HEP)
- Grant/Contract Number:
- AC02-07CH11359
- OSTI ID:
- 1250536
- Report Number(s):
- FERMILAB-CONF-15-237-CD; 1408677
- Journal Information:
- Journal of Physics. Conference Series, Vol. 664, Issue 7; Conference: 21st International Conference on Computing in High Energy and Nuclear Physics, Okinawa (Japan), 13-17 Apr 2015; ISSN 1742-6588
- Publisher:
- IOP PublishingCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Similar Records
astroABC : An Approximate Bayesian Computation Sequential Monte Carlo sampler for cosmological parameter estimation
CosmoSIS: Modular cosmological parameter estimation