2015 Fiscal Year Annual Research Report
Ｔ２ＫとＳＫ実験データの組合せによるニュートリノ質量とミキシングパラメータの制限
| Project/Area Number |
14F04796
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| Research Institution | The University of Tokyo |
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Principal Investigator |
MARTENS Kai 東京大学, カブリ数物連携宇宙研究機構, 准教授 (20535025)
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| Co-Investigator(Kenkyū-buntansha) |
CALLAND RICHARD 東京大学, カブリ数物連携宇宙研究機構, 外国人特別研究員
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| Project Period (FY) |
2014-04-25 – 2017-03-31
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| Keywords | RJMCMC / GPU / CUDA / infinite mixture model / non-parametric model / MCMC |
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| Outline of Annual Research Achievements |
The main focus of this period was to work on improving the reconstruction of Super-Kamiokande atmospheric neutrino data. To facilitate this, several computers were purchased using the grant-in-aid research funds, which contain graphics processing units. The improvements to the reconstruction software has 2 main focusses; improving the ring finding algorithm and improving the speed. The ring finding improvement is done via modelling the event with a non-parametric Bayesian mixture model, from which the posterior probability of the model given the data can be computed numerically using a Reversible Jump Markov Chain Monte Carlo. The second task is to improve the speed. Currently the reconstruction takes ～5 minutes to complete per event, and given that the Monte Carlo contains millions of events, this is a huge computational burden. The algorithm is highly parallelizable, and as such I converted most of the charge and time prediction code, along with the likelihood computation to run on GPUs using the CUDA software API. This resulted in a significant speed improvement with negligible sacrifice to numerical precision. The speed improvement is on the order of 50 times faster than the original version.
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| Current Status of Research Progress |
Current Status of Research Progress
3: Progress in research has been slightly delayed.
Reason
The project is a little behind schedule because of unforeseen issues related to the original reconstruction software. It was found that the calculation of time likelihood in the original code contained many approximations, some of which inhibited the new mixture model method. A significant amount of time was used to debug and correct these issues, however the result of this is that the time likelihood has been improved for the original algorithm. Besides this, the new reconstruction method is working and now exhaustive validation studies are ongoing. The GPU acceleration has greatly improved the turnaround time for debugging and developing the code, so progress from this point is expected to be much faster.
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| Strategy for Future Research Activity |
The plan for FY2016 is to run the new reconstruction algorithm on the atmospheric Monte Carlo and data, to produce new samples which can be used in the oscillation analysis. Using these samples, more detailed sensitivity studies can be undertaken using the analysis framework developed in FY2014. Work is currently ongoing with other groups to improve the systematic uncertainties, so I intend to combine efforts with these groups to come to an acceptable parameterization of uncertainties to be used in the final analysis.
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Research Products
(1 results)
