Welcome To MaCh3!
MaCh3
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Introduction
The Markov Chain 3 flavour is a framework born in 2013 as a Bayesian MCMC fitter for T2K oscillation analysis. It has now been used for multiple T2K Oscillation analyses both at the Near and Far detectors throughout the years and is also used by the DUNE and HK oscillation analysis groups as well as for joint fits between T2K and NOvA and T2K and SK’s atmospheric data.
The framework has also evolved to allow non MCMC modules to interrogate the likelihoods implemented.
If something is unclear please contact us via
Indico If you need a password, please reach out to MaCh3-leadership for access.
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Markov-Chain Monte Carlo sampling
Start Here
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Tutorial
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Run a dummy experiment and quickly explore MaCh3's functionality.
User Guide
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Learn the concepts behind Markov Chain Monte Carlo and Bayesian analysis.
API Reference
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Understand the high-level structure of the MaCh3 framework.
Release Notes
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Find when features and changes were introduced.
Bibliography
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Browse the scientific references cited throughout the MaCh3 documentation.
Python Interface
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Explore pyMaCh3 and learn how to interact with MaCh3 from Python.
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Recent results & publications
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14+ papers using MaCh3
34+ theses and dissertations
2013 → present
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Most Recent Publication
The DUNE Collaboration. A Bayesian approach to the long-baseline neutrino oscillation sensitivity of DUNE.
Most Recent PhD
Naseem Khan — Prototyping of and sensitivity studies for a gaseous argon near detector for the deep underground neutrino experiment
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See the full list of results and publications |