HSE Becomes LHCb Associate Member at CERN

The Higher School of Economics has joined the LHCb collaboration at the Large Hadron Collider, which is operated by the European Organization for Nuclear Research. The group from HSE will consist of researchers from the Laboratory of Methods for Big Data Analysis (LAMBDA). This will give HSE researchers full access to data from the collaboration and allow the university to participate in various projects.

‘From the perspective of the international academic community, participating in CERN experiments is a unique indication of a university’s quality,’ notes Denis Derkach, Senior Research Fellow with the Laboratory of Methods for Big Data Analysis (LAMBDA). ‘This assumes the highest scientific level of research. If you look at the world’s top 100 universities, for example those on the QS ranking, 90% of them participate in CERN experiments.’

Researchers with the Faculty of Computer Science have been working with the LHCb collaboration for more than two years already. The Yandex School of Data Analysis (which has been a collaboration participant since 2015) has previously been given the opportunity to work with data from the experiment. The HSE group is now planning to further develop projects such as particle identification and LHCb calorimeter optimisation together with Yandex and other participants of the collaboration.

‘Our team members have already proven themselves through their accomplishments. It’s enough to say that the high-level selection of data collected within the experiment is largely based on an algorithm developed by our team,’ comments Fedor Ratnikov, a Senior Research Fellow with LAMBDA.

By joining the collaboration, the group assumes certain obligations and, as a participant, will go through regular checks to assess the group’s activities and overall contribution towards the experiment.

In the future, HSE researchers and their colleagues from the School of Data Analysis, as LHCb collaboration participants, are planning to continue using machine-learning methods in high-energy physics.

‘We see great potential for developing machine-learning in high-energy physics,’ says LAMBDA Head Andrey Ustyuzhanin. ‘At the same time, the problems that arise in physics serve as the impetus for new approaches in machine-learning that can be applied to other areas as well. For example, we have a project to develop an algorithm that searches for anomalies in data storage systems. In [the project] we apply approaches that were used in working with data from the Large Hadron Collider.’

The LHCb experiment is being carried out to research the asymmetry between matter and antimatter (charge-parity violation) in the universe, particularly when b quarks, or ‘beauty’ quarks, interact. This is why there is a ‘b’ in LHCb – it stands for Large Hadron Collider beauty experiment. Despite the lack of b quarks in the contemporary universe, these quarks were dispersed soon after the Big Bang. B and anti b quarks are very unstable and quickly decay into a number of other particles. Physicists believe that by researching variations in b and anti b quark decay, we can begin to understand the nature of antimatter.