Personne

Alexey Boyarsky

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https://people.epfl.ch/alexey.boyarsky?lang=fr

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https://people.epfl.ch/alexey.boyarsky?lang=fr

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Unités associées (1)

Laboratoire de physique des hautes énergies LS

Publications associées (54)

Veuillez noter qu'il ne s'agit pas d'une liste complète des publications de cette personne. Elle inclut uniquement les travaux sémantiquement pertinents. Pour une liste complète, veuillez consulter Infoscience.

Measurement of the 171Tm beta spectrum

Alexey Boyarsky, Lesya Shchutska

The beta spectrum of the main transition of the 171Tm was measured using a double focalizing spectrometer. The instrument was lately improved in order to reduce its low energy threshold to 34 keV. We used the spectrometer to measure the energy end-point of ...

2023

The Forward Physics Facility at the High-Luminosity LHC

Martin Bauer, Alexey Boyarsky, Enrico Bertuzzo, Lesya Shchutska, Wenjing Wu, Karan Kumar, Francesco Cerutti, Wei Liu, Patrick Foldenauer, Yi Zhang, Saurabh Nangia, Raffaele Tito D'Agnolo, Emanuele Copello, Ji Hyun Kim, Yiming Li, Zheng Wang, Jing Li

High energy collisions at the High-Luminosity Large Hadron Collider (LHC) produce a large number of particles along the beam collision axis, outside of the acceptance of existing LHC experiments. The proposed Forward Physics Facility (FPF), to be located s ...

2023

Exploring the potential of FCC-hh to search for particles from B mesons

Alexey Boyarsky, Lesya Shchutska

The Future Circular Collider (FCC-hh) is a proposed successor of the Large Hadron Collider (LHC). FCC-hh would push both the energy and intensity frontiers of searches for new physics particles. In particular, due to higher energy and luminosity than at th ...

Springer Nature2022

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Roberto Castello is a senior scientist and group leader at the EPFL Laboratory of Solar Energy and Building Physics. Physicist by training, he has extensive experience in collecting, classifying and interpreting large datasets using advanced data mining techniques and statistical methods. He received his MSc (2007) in Particle Physics and PhD (2010) in Physics and Astrophysics from the University of Torino. He worked as a postdoctoral researcher at the Belgian National Research Fund (2011-2014) and at the CERN Experimental Physics Department (2015-2017) as a research fellow and data scientist. He is primary author of more than 20 peer-reviewed publications and he presented at major international conferences in the high energy physics domain. In 2018 he joined the Solar Energy and Building Physics Laboratory (LESO-PB) to work on data mining and Machine Learning techniques for the built environment and renewable energy. His main research interests are: spatio-temporal modeling of renewable energy potential, energy consumption forecasting techniques, anomaly detection, and computer vision techniques for automated classification in the built environment. He leads the group of Urban Data Mining, Intelligence and Simulation at LESO-PB and he is a member of the NRP75 Big Data project (HyEnergy) of the Swiss National Science Foundation. He is a member of the Swiss Competence Centre for Energy Research (SCCER) and deputy leader of the working group on Leveraging Ubiquitous Energy Data. He has served as a scientific committee member, workshop organizer and speaker at international conferences (ICAE 2020, Applied Machine Learning Days 2019 and 2020, CISBAT 2019 and 2021 and SDS2020). Since 2017 he is member of the Geneva 2030 Ecosystem network, promoting the United Nations agenda towards the realization of the Sustainable Development Goals (SDGs).

Yiming Li

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Personnes menant des recherches similaires (32)