The OXO Signal Extraction Framework
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Abstract
OXO is a C++ signal extraction framework for particle physics with an emphasis on Bayesian statistics. It is written for use in analysis in any particle physics experiment, but its design was informed by SNO+ analyses, particularly the 0\(\nu \beta \beta \) search. In a nutshell, OXO is a set of C++ classes which represent the major elements of an analysis: the probability distribution functions, parametrisations of systematic uncertainty, test statistics and optimisation or sampling algorithms.
References
- 1.Cowan G, Cranmer K, Gross E, Vitells O (2011) Asymptotic formulae for likelihood-based tests of newphysics. Eur Phys J C71:1554 [Erratum: Eur Phys J C73:2501 (2013)]. https://doi.org/10.1140/epjc/s10052-011-1554-0, https://doi.org/10.1140/epjc/s10052-013-2501-z. arXiv:1007.1727
- 2.Biller SD, Oser SM (2015) Another look at confidence intervals: proposal for a more relevant and transparent approach. Nucl Instrum Methods A774:103–119. https://doi.org/10.1016/j.nima.2014.11.081. arXiv:1405.5010
- 3.Gilks WR, Richardson S, Spiegelhalter D (1995) Markov chain Monte Carlo in practice. Chapman & Hall/CRC interdisciplinary statistics. Taylor & Francis, Hoboken. https://books.google.co.uk/books?id=TRXrMWY_i2IC
- 4.Neal RM (2012) MCMC using Hamiltonian dynamics. arXiv:1206.1901
- 5.Afshar HM, Domke J (2015) Reflection, refraction, and Hamiltonian Monte Carlo. In: Cortes C et al (eds) Advances in neural information processing systems, vol 28. Curran Associates, Red Hook, pp 3007–3015. http://papers.nips.cc/paper/5801-reflection-refraction-and-hamiltonian-monte-carlo.pdf
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