One of the four big experiments at the world’s largest and most powerful particle accelerator, the Large Hadron Collider, is now testing Yandex’s machine learning technology, MatrixNet, on their data on B-meson decay.
This is a new stage in a long-term collaboration between the European Organization for Nuclear Research (CERN) and Yandex, which began in 2011 when the LHCb experiment started using our servers for some of their data simulation and continued in 2012 with Yandex supplying a prototype of a custom-built search tool for the LHC events. Now, Yandex’s machine learning technology is expected to help the CERN physicists boost precision levels in identifying extremely rare particle decays in the vast amount of data collected by LHCb. Comparing the number of observed events against predictions, scientists can confirm or refute their theories.
Bs0->mumu decay candidate observed at the LHCb experiment (photo by CERN)
In November last year the LHCb researchers reported that they had observed the decay of a Bs meson into a muon-antimuon pair for the first time. The statistical level of significance for this decay, however, did not allow to unequivocally qualify this a discovery. But, that there is not a statistically significant number of decays in the LHCb data does not mean that they are not there. It only means that a better tool or more data are needed to observe them with confidence. With MatrixNet, which allows to make decisions about data relevancy based on a very large number of factors, statistical levels of particle decay detection might turn out to be dramatically different. And this is one of the reasons why CERN liked the idea of using MatrixNet.
CERN is a very large-scale international laboratory where hypotheses, theories and models in theoretical physics are tested by running experiments and accumulating data, which can then be analysed and interpreted. Since the LHC was started in 2008, the LHCb experiment has been collecting data about over 10 billions particle events per year. When the LHC stops for an upgrade in spring this year, scientists will go into the analytical phase of their research.
MatrixNet is a high-precision tool that can make a difference in the quality of results obtained during data analysis. By joining CERN openlab, a framework to test and validate cutting-edge information technologies and services in partnership with industry at CERN, we will work this year on helping scientists find what they are looking for. As a CERN openlab Associate, the objective is to develop a service that could allow the CERN researchers to use MatrixNet for their purposes without additional assistance from our engineers, as it happens now. The launch of the MatrixNet service at CERN, scheduled for May 2013, will give the physicists an opportunity to detect particle decays more precisely, while we will be able to improve our machine learning technology by running it on a very large dataset. What MatrixNet does when applied to CERN’s event data is much like what it does to build a ranking formula for Yandex’s search engine. CERN’s use of MatrixNet on their data gives us an opportunity to expand the application range for our machine learning technology beyond web search into a new field – theoretical physics.