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Regular version of the site

CERN Workshop at the HSE Faculty of Computer Science

Thirty school students from Moscow and the Moscow Region recently had an opportunity to meet international researchers and analyze data obtained from the Large Hadron Collider at a workshop organized by HSE’s Faculty of Computer Science, Yandex and CERN.


CERN workshops for school students have been held for several years in many countries. In 2017, Russian school children joined the project with the support of the HSE Faculty of Computer Science and Saint Petersburg State University.

Denis Derkach and Fedor Ratnikov, Senior Research Fellows at the HSE Laboratory of Methods for Big Data Analysis, oversaw the organization of the event. This particular laboratory focuses on applying the latest methods in data analysis to high energy physics. The lab’s staff also conducts research in other areas, such as the application of machine learning technologies and neuroscience.

‘CERN workshops for school students are great in that they allow prospective students interested in science and technology to choose their future specialization and career path knowingly,’ says Ivan Arzhantsev, Dean of the HSE Faculty of Computer Science. He adds: ‘since the start of the 20th century, scientific study has been largely focused on large projects that were historically associated with physics. At this workshop, students learned about the direct connection between two large projects — an experiment conducted at CERN, and modern data analysis. The second project is not limited to a particular accelerator. In fact, it has been developed at many research centres and IT companies, and this project has significantly affected our lives in recent years.’

Denis Derkach worked as a research fellow at CERN before coming to the HSE Faculty of Computer Science, while Fedor Ratnikov has been involved in CERN research projects for more than 12 years. Therefore, students were able to learn details about the Large Hadron Collider first hand. ‘Our laboratory is engaged in several joint projects with CERN member universities,’ explains Denis Derkach. He notes: ‘We work with the universities of Rome, Cambridge, MIT and many others. The tasks that we solve require in-depth knowledge of both particle physics and Big Data analysis methods. The solutions offered at our laboratory are already quite suitable for online selection of interesting events in experiments with the Large Hadron Collider. We are now working on automation systems to monitor and evaluate the quality of the data generated. Moreover, the project is based on the application of techniques developed by our lab’s doctoral students.’

Why Work with Big Data

The CERN workshop was comprised of lectures on physics research conducted through the Large Hadron Collider, data analysis, and laboratory tests of collider data. It also included a teleconference with students from other countries (Italy, France and Brazil) and CERN researchers Francesca Dordei and Eduardo Rodriguez.

According to the organizers, there are no serious criteria for student selection. Instead, the main criterion is keen interest, ‘as this is the key factor in the development of science and technology.’ ‘Commenting about preparation, the lecture for senior students requires thorough elaboration of the material,’ says Denis Derkach. ‘Young people, who come to these events, are already prepared and can pose interesting questions. We also should not forget that, in just a few hours, we have to cover several semesters’ worth of physics studies,’ he notes. Students from several lyceums in Moscow and the Moscow Region attended the event along with their teachers.

The Dual Importance of Mistakes and Findings

The event’s activities, as noted by the students themselves, included two parts. During the first part, the participants were asked to visually analyze 30 real events (i.e., recorded results of proton-proton collisions in the collider) and identify the products of a D^0 meson collapse. The students learned about D^0 mesons at introductory lectures. During the second part, the students measured the D^0 meson lifetime. All students were provided with the same data, which, in turn, enabled them to make special selections in order to reduce the background contribution while also increasing the signal significance. From the approximation of the collapse-time distribution, everyone was able to obtain a result describing the lifetime. A similar measurement conducted during an LHCb experiment in 2011 has improved our understanding of the first seconds of the universe’s development and the absence of anti-matter in the world around us.

The results of the first part of the work of all students (from Russia and three other countries) were brought together, thereby allowing researchers to obtain a good statistical signal for the D^0 meson. ‘This section should help students understand that, by combining the efforts of many participants from various countries, it is possible to ensure a significant improvement of the results. And this is the essence of any international research collaboration,’ says Fedor Ratnikov.

The second part demonstrated to students that the same data may generate different results that are internally consistent, but nonetheless display varying accuracy depending on the preferred processing method. Therefore, a given result is not so much a mechanical procedure, but a process of intense intellectual activity, tests, mistakes and findings.

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