https://doi.org/10.1140/epjd/s10053-021-00101-y
Regular Article - Atomic Physics
Measurement of the principal quantum number distribution in a beam of antihydrogen atoms
1
Stefan Meyer Institute for Subatomic Physics, Austrian Academy of Sciences, 1030, Vienna, Austria
2
Ulmer Fundamental Symmetries Laboratory, RIKEN, 351-0198, Saitama, Japan
3
Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Brescia, Brescia, Italy
4
INFN, sez. Pavia, Pavia, Italy
5
Institute of Physics, Graduate School of Arts and Sciences, University of Tokyo, 153-8902, Tokyo, Japan
6
Graduate School of Advanced Sciences and Engineering, Hiroshima University, 739-8530, Hiroshima, Japan
7
RIKEN Nishina Center for Accelerator-Based Science, 351-0198, Saitama, Japan
8
CERN, Geneva, Switzerland
9
Dipartimento di Scienza e Alta Tecnologia, Università degli Studi dell’Insubria, Como, Italy
10
Department of Physics, Tokyo University of Science, 162-8601, Tokyo, Japan
11
Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark
12
CEA CESTA, 33114, Le Barp, France
13
Inst. Particle Physics and Astrophysics, ETH Zurich, Zurich, Switzerland
14
Data Technology, Vienna, Austria
15
School of Science, University of Tokyo, 113-0033, Tokyo, Japan
16
Max Planck Institute for Nuclear Physics, Heidelberg, Germany
a
bernadette.kolbinger@cern.ch
Received:
6
August
2020
Accepted:
14
December
2020
Published online:
8
March
2021
The ASACUSA (Atomic Spectroscopy And Collisions Using Slow Antiprotons) collaboration plans to measure the ground-state hyperfine splitting of antihydrogen in a beam at the CERN Antiproton Decelerator with initial relative precision of 
or better, to test the fundamental CPT (combination of charge conjugation, parity transformation and time reversal) symmetry between matter and antimatter. This challenging goal requires a polarised antihydrogen beam with a sufficient number of antihydrogen atoms in the ground state. The first measurement of the quantum state distribution of antihydrogen atoms in a low magnetic field environment of a few mT is described. Furthermore, the data-driven machine learning analysis to identify antihydrogen events is discussed.
© The Author(s) 2021
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