[a]Sami Caroff

# The Real Time Analysis framework of the Cherenkov Telescope Array’s Large-
Sized Telescope

Pierre Aubert  Enrique Garcia  Gilles Maurin  Vincent Pollet  Thomas Vuillaume

###### Abstract

The Large-Sized Telescopes (LSTs) of the Cherenkov Telescope Array Observatory
(CTAO) will play a crucial role in the study of transient gamma-ray sources,
such as gamma-ray bursts and flaring active galactic nuclei. The low energy
threshold of LSTs makes them particularly well suited for the detection of
these phenomena. The ability to detect and analyze gamma-ray transients in
real-time is essential for quickly identifying and studying these rare and
fleeting events. In this conference, we will present recent advances in the
real-time analysis of data from the LST-1, the first prototype of LST located
in the Canary island of La Palma. We will discuss in particular the
development of new algorithms for event reconstruction and background
rejection. These advances will enable rapid identification and follow-up
observation of transient gamma-ray sources, making the LST-1 a powerful tool
for the study of the dynamic universe. The implementation of this framework in
the future Array Control and Data Acquisition System (ACADA) of CTAO will be
discussed as well, based on the experience with LST.

##  1 Introduction

Gamma-ray astronomy aims to study the cosmic particle accelerators of the
universe, through the analysis of their high-energy electromagnetic emissions.
It covers various types of sources, from Galactic ones such as supernovae
remnants and pulsar wind nebulae to extragalactic ones like active galactic
nuclei. Recently, the first detections of the afterglow radiations from gamma-
ray bursts [1, 2, 3] were achieved at very high energies (VHE, $E>100$ GeV),
opening a new research area around transient phenomena. Moreover, recent
observations of the electromagnetic counterpart of gravitational wave emitters
have motivated a fast development of the field.

The Cherenkov Telescope Array Observatory (CTAO) represents the next
generation of ground-based observatories. In October 2018, the first prototype
of the Large-Sized Telescope, called LST-1, was installed and inaugurated at
the Roque de los Muchachos Observatory (ORM). It has been actively collecting
data since November 2019. The LST-1 boasts a 23-meters diameter reflector
dish, a lightweight mechanical structure, and a rapid repositioning system.
Its primary purpose is to detect low-energy gamma rays, above approximately
$20$ GeV, and to enable swift follow-up observations of transient events.
Efficiently monitoring alerts and observing transient events is only possible
with a robust and fast (real-time) analysis system, which processes data
immediately after they are collected during observations and provides timely
feedback to operators to initiate appropriate decisions and generate alerts.
The analysis process can be divided into two sequential stages: first, the
reconstruction, characterization, and selection of gamma rays from the
recorded images, and second, the search for sources within the telescope’s
field of view.

This proceeding only covers the first part which is the real-time analysis
(RTA) of the LST-1, prototype of the CTAO online-reconstruction. Section 2
describes the global architecture and the technical options adopted while
section 3 exposes the performance and results obtained on Markarian 421 data.

##  2 Data Analysis

The initial stage of the analysis process, known as reconstruction, focuses on
processing the uncalibrated raw data (R0) to derive the parameters of each
recorded event (DL3-data). These parameters include the energy, direction, and
a gamma-ray like classification score (called gammaness). The LST-1 telescope
R0 data consists of a sequences of 40 images, each containing 1855 pixels, for
every recorded event. In its nominal mode, the telescope operates at a rate of
$\sim$10 kHz, generating a data flow of $\sim$3 GB/s of the raw data but the
rate can go up to 15 kHz in case of extreme astrophysical events. Looking
ahead to the future CTA observatory, the expected data flow will be
approximately 17 GB/s for the northern site and 27 GB/s for the southern site.

The historical Hillas method [4] serves as the reference reconstruction
approach, renowned for its simplicity and robustness. Its modern version
comprises three consecutive stages, implemented in three corresponding
software blocks:

  * •

The R0->DL1 stage includes data calibration, images aggregation and cleaning
for each event. It also encompasses the extraction of Hillas and timing
parameters (as defined in [5]). This stage significantly reduces the data
volume from several GB/s to less than 150 MB/s. Most of the output data
consists of the final calibrated images used for monitoring the data
acquisition (DAQ), while the Hillas parameters correpond to only a few MB/s.

  * •

The DL1->DL2 stage focuses on evaluating the energy, direction, and gammaness.
The input and output data sizes are approximately the same, amounting to a few
MB/s.

  * •

DL2->DL3 stage corresponds to the selection of gamma-like events, further
reducing the data flow to a few kB/s.

###  2.1 Software Architecture

To ensure seamless data acquisition, the RTA is performed on dedicated
servers, with communication between the DAQ system and the analysis servers
established through the network. At least two servers are required to receive
data from the LST-1. R0 data is streamed in the Protocol Buffer
format111https://developers.google.com/protocol-buffers/docs/overview handled
by ZeroMQ222https://zeromq.org, an open-source universal messaging library.

The Slurm333https://slurm.schedmd.com/documentation.html workload manager
plays a vital role in resources and processes management. This open-source,
fault-tolerant, and highly scalable cluster management and job scheduling
system is specifically designed for both large and small Linux clusters.

On each server, four processes of R0->DL1, each with two threads, continuously
run to process the data stream. Each thread handles an approximate event rate
of 1.25 kHz, resulting in a total processing capacity of 10 kHz under nominal
operation. Evaluation of the events produced by Car flash444Car flash events
are induced by the illuminations of car passing by the road near the
telescope. indicates that the maximum rate of 15 kHz (1.875 kHz) provided by
the LST-1 is supported by the analysis servers. A short buffer of 100 events,
equivalent to 80 ms of data, is used.

Following the R0->DL1 computation,
HDF5555https://support.hdfgroup.org/HDF5/666https://gitlab.in2p3.fr/CTA-
LAPP/PHOENIX_LIBS/PhoenixHDF5/ files containing $20\,000$ DL1 events are
generated. For each new file, the DL1->DL2->DL3 chain is immediately executed
using the capabilities of the Slurm manager.

###  2.2 Optimization of data processing

The RTA includes several stages: image calibration, selection, integration,
cleaning, and extraction of Hillas parameters. The data reduction and
processing steps are computationally demanding, therefore, the C++ programming
language was chosen for its fast execution and optimization capabilities.

The calibration step consists in converting electronic charge into the
corresponding number of photoelectrons detected by each camera pixel by
subtracting a baseline (pedestal) and applying a conversion factor (gain) .
The calibration algorithm selects the appropriate gains and pedestals obtained
with calibration runs. The compiler automatically vectorizes this algorithm
for improved performance.

Pixel-wise charge integration involves summing the calibrated signals for each
pixel using a specific time window. The compiler can optimize this process by
effectively vectorizing the integration calculations.

Image cleaning is performed to select relevant pixels and remove those which
triggered due to background noise. A two-thresholds algorithm (thresholding on
pixel and pixel neighbours charge) is applied using a temporary matrix to
ensure efficient memory access for neighboring pixels. Computation is
performed instead of branching, which improves computational time by enabling
vectorization.

Hillas and timing parameters are computed to characterize the shape of the
cleaned images and extract relevant information. High-level intrinsic
functions777https://gitlab.in2p3.fr/CTA-LAPP/PHOENIX_LIBS/IntrinsicsGenerator/
are used to optimize reduction operations and barycenter computations,
resulting in faster execution compared to automatic optimization methods.

Overall, these optimization techniques aim to reduce the computation time and
enhance the performance of the data processing pipeline.

###  2.3 High level parameters computation and DL3 production

The computation of the high-level parameters is not a bottleneck in terms of
computing time, therefore the offline analysis chain of the LST-1 is used in
order to simplify the maintainability of the software. This offline analysis
chain is described in [5] and only a brief description will be provided in
this proceeding. This analysis pipeline is working with a different DL1 format
(hereafter called DL1-lstchain) than the CTAO standard one. Thus to use it a
conversion step is required in the pipeline.

The high-level parameters, disp, energy, and gammaness are obtained using
random forests trained on Monte Carlo simulations of gamma-ray events
generated with a zenith angle of 20°. Similar results with respect to those of
the offline trained random forest [5] are obtained: time gradient is the most
important feature for disp reconstruction, followed by psi, and length Hillas
parameters, while the length is the most important feature for energy. For
gammaness classification, features ranking is more equally distributed.

The last part of the analysis consists of a gammaness selection and a
conversion to a FITS format compliant with gammapy, a high-level analysis
software [6].

##  3 Performance

###  3.1 Computing time

As explained in section 2, the software should support an event rate up to
$15$ kHz. Trigger rate peaks are exploited to test this requirement. An
example is shown in Figure 1: it can be observed that the DL1 reconstruction
was not affected by an event rate peak of $14$ kHz.

Figure 1: Rate of event received by the R0->DL1 process compared to the rate
of events in DL1. The observed difference in normalisation is due to the
cleaning step which rejects some of the events.

The typical time for the production of the different analysis steps, is
estimated using an observation run with an average rate of $\sim 8$ kHz:

  * •

R0->DL1 : $30$ s. Taking that the effective rate per thread before cleaning is
$1$ kHz, and that $20$k events need to be accumulated to create a DL1 file,
the analysis time can be approximated as $10$ seconds while the $20$ remaining
seconds are spend to accumulate events. The offline chain for the DL1
production is running at $0.03$ s/event while the RTA achieve a computing
speed of $0.5$ ms/event.

  * •

DL1->DL1 lstchain : $12$ s are used for this step, which will disappear when
lstchain data format aligns with CTAO’s in future releases.

  * •

DL1 lstchain->DL2->DL3 : $14$ \+ $10$ s.

In total, $66$ seconds are needed to produce a DL3 from $20$k R0 events. It is
important to note that this time scales linearly with the number of events per
file.

###  3.2 Reconstruction

The reconstruction performance is tested using real time data obtained from
observations of Markarian 421. They consist of $14$ observations selected
according to the weather on site, variability of event rate at the DL3 level
and zenith angle lower than $30$°, amounting to $4.2$ hours of observations.
These data were reconstructed with both the RTA and the lstchain pipeline. We
used the same Monte Carlo simulation to train the random forest used by both
pipelines.

The first step consists to optimizing the gammaness threshold. This was done
separately on the two chains, by performing a full multiple OFF analysis of a
single run of the dataset that is then removed for the rest of the analysis.
The size of the ON region is set to $0.2$° and the exclusion region defined as
$0.35$°. The same setup will be used in all this proceeding. A maximum
significance of $15.3\sigma$ with a gammaness threshold of $0.75$ for the RTA,
and $31.2\sigma$ with $0.7$ for the offline analysis are found. The effective
area, energy bias and resolution, computed using the gamma Monte Carlo
simulations, are displayed in figure 2. The energy resolution and bias are
similar between the two analysis chains above $400$ GeV, and a degradation is
observed at the lowest energy for the RTA when compared to the lstchain
reconstruction. The relative value of the effective area is linked to the
leakage of events up to $0.2$° and is thus related to a worse angular
resolution for the RTA analysis chain.

Figure 2: Effective area, energy resolution and bias in function of energy,
computed in the gamma Monte Carlo.

A multiple off analysis is performed on the whole dataset, and the resulting
theta square plot is presented in figure 3. A significance of $43.3\sigma$ for
the RTA and $83.1\sigma$ for the offline analysis are found.

Figure 3: Theta square plot of Markarian 421 data compared between the RTA
(left) and the lstchain (right).

The spectra of the source is reconstructed with the lstchain pipeline, which
has been validated for this purpose, and used in order to derive the
sensitivity of both chains. Differential sensitivity is defined as the minimal
flux needed to have a $5\sigma$ detection in $50$ hours of observations. The
results are presented in figure 4. The sensitivity of the RTA pipeline is
roughly two times worse than the lstchain one above $0.4$ TeV, while at lower
energies it worsens.

Figure 4: Top left : Differential Sensitivity for 50 hours of the RTA compared
with lstchain as a function of true energy. Top right : Sensitivity ratio
between the RTA and lstchain as a function of true energy. Down : Integral
Sensitivity, from $20$ GeV to $10$ TeV, of the RTA compared to a typical flux
of $1$, $0.5$ and $0.1$ Crab, as a function of observation time. The time to
detect the typical fluxes with the RTA is respectively $177$, $517$ and $8580$
seconds. For the offline analysis, we have respectively $50$, $139$ and $2140$
seconds.

Besides the differential sensitivity, an important RTA use case is to detect a
source in a time period shorter than a run ($20$ minutes). To explore this
possibility, we provide the integral sensitivity versus time, supposing a Crab
nebula like spectra, based on the results obtained on the test sample. This is
presented in the figure 4. The RTA is able to detect an integrated flux of
$0.3$ Crab units in a single run.

##  4 Conclusions and outlook

In this proceeding, we presented the software architecture and performance of
the RTA of the LST-1. Production of DL3 files from $20$k events takes $66$
seconds, and a flux equivalent to the one of the Crab Nebula above 20 GeV can
be detected in $177$ seconds. The differential sensitivity of the RTA is two
times worse than the offline analysis chain above $400$ GeV.

The differences with lstchain is mainly due to two main factors. The first one
is the timing parameters computation, particularly useful for the angular
resolution, which is performed using a simple linear regression in the RTA for
time computing reasons. Moreover, the performed calibration is simplified to
gain and pedestal calibration while a full calibration chain imply as well the
so-called DRS4 calibration [8].

The RTA will be provided to the Array Control and Data Acquisition System
(ACADA) [9] of CTAO for its first release, with no differences with the
version presented in this proceeding apart the input calibrated stream. The
CTAO speed requirement of $1000$ events/CPU/Tel is already fulfilled. The
adaptation of this software to the full array will require modifications
mostly at the DL1 level, to merge triggered events, and to DL2 production to
take into account the stereoscopic reconstruction. Nevertheless, the
scalability and modularity of this software was designed to fulfill this goal
in the coming years for the construction of the LST2-4 telescopes and the next
releases of ACADA.

## Acknowledgements

The aknowledgements for CTAO and LST can be found here and here.

## References

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  * [7] Rubén López-Coto, et al.. lstchain: An Analysis Pipeline for LST-1, the First Prototype Large-Sized Telescope of CTA. 30th ADASS, Nov 2020, Granada, Spain. pp.357.
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## Full Author list of the CTA-LST Project:

K. Abe1, S. Abe2, A. Aguasca-Cabot3, I. Agudo4, N. Alvarez Crespo5, L. A.
Antonelli6, C. Aramo7, A. Arbet-Engels8, C. Arcaro9, M. Artero10, K. Asano2,
P. Aubert11, A. Baktash12, A. Bamba13, A. Baquero Larriva5,14, L.
Baroncelli15, U. Barres de Almeida16, J. A. Barrio5, I. Batkovic9, J. Baxter2,
J. Becerra González17, E. Bernardini9, M. I. Bernardos4, J. Bernete Medrano18,
A. Berti8, P. Bhattacharjee11, N. Biederbeck19, C. Bigongiari6, E. Bissaldi20,
O. Blanch10, G. Bonnoli21, P. Bordas3, A. Bulgarelli15, I. Burelli22, L.
Burmistrov23, M. Buscemi24, M. Cardillo25, S. Caroff11, A. Carosi6, M. S.
Carrasco26, F. Cassol26, D. Cauz22, D. Cerasole27, G. Ceribella8, Y. Chai8, K.
Cheng2, A. Chiavassa28, M. Chikawa2, L. Chytka29, A. Cifuentes18, J. L.
Contreras5, J. Cortina18, H. Costantini26, M. Dalchenko23, F. Dazzi6, A. De
Angelis9, M. de Bony de Lavergne11, B. De Lotto22, M. De Lucia7, R. de
Menezes28, L. Del Peral30, G. Deleglise11, C. Delgado18, J. Delgado Mengual31,
D. della Volpe23, M. Dellaiera11, A. Di Piano15, F. Di Pierro28, A. Di
Pilato23, R. Di Tria27, L. Di Venere27, C. Díaz18, R. M. Dominik19, D. Dominis
Prester32, A. Donini6, D. Dorner33, M. Doro9, L. Eisenberger33, D. Elsässer19,
G. Emery26, J. Escudero4, V. Fallah Ramazani34, G. Ferrara24, F. Ferrarotto35,
A. Fiasson11,36, L. Foffano25, L. Freixas Coromina18, S. Fröse19, S. Fukami2,
Y. Fukazawa37, E. Garcia11, R. Garcia López17, C. Gasbarra38, D. Gasparrini38,
D. Geyer19, J. Giesbrecht Paiva16, N. Giglietto20, F. Giordano27, P. Gliwny39,
N. Godinovic40, R. Grau10, J. Green8, D. Green8, S. Gunji41, P. Günther33, J.
Hackfeld34, D. Hadasch2, A. Hahn8, K. Hashiyama2, T. Hassan18, K. Hayashi2, L.
Heckmann8, M. Heller23, J. Herrera Llorente17, K. Hirotani2, D. Hoffmann26, D.
Horns12, J. Houles26, M. Hrabovsky29, D. Hrupec42, D. Hui2, M. Hütten2, M.
Iarlori43, R. Imazawa37, T. Inada2, Y. Inome2, K. Ioka44, M. Iori35, K.
Ishio39, I. Jimenez Martinez18, J. Jurysek45, M. Kagaya2, V. Karas46, H.
Katagiri47, J. Kataoka48, D. Kerszberg10, Y. Kobayashi2, K. Kohri49, A. Kong2,
H. Kubo2, J. Kushida1, M. Lainez5, G. Lamanna11, A. Lamastra6, T. Le Flour11,
M. Linhoff19, F. Longo50, R. López-Coto4, A. López-Oramas17, S. Loporchio27,
A. Lorini51, J. Lozano Bahilo30, P. L. Luque-Escamilla52, P. Majumdar53,2, M.
Makariev54, D. Mandat45, M. Manganaro32, G. Manicò24, K. Mannheim33, M.
Mariotti9, P. Marquez10, G. Marsella24,55, J. Martí52, O. Martinez56, G.
Martínez18, M. Martínez10, A. Mas-Aguilar5, G. Maurin11, D. Mazin2,8, E.
Mestre Guillen52, S. Micanovic32, D. Miceli9, T. Miener5, J. M. Miranda56, R.
Mirzoyan8, T. Mizuno57, M. Molero Gonzalez17, E. Molina3, T. Montaruli23, I.
Monteiro11, A. Moralejo10, D. Morcuende4, A. Morselli38, V. Moya5, H.
Muraishi58, K. Murase2, S. Nagataki59, T. Nakamori41, A. Neronov60, L.
Nickel19, M. Nievas Rosillo17, K. Nishijima1, K. Noda2, D. Nosek61, S.
Nozaki8, M. Ohishi2, Y. Ohtani2, T. Oka62, A. Okumura63,64, R. Orito65, J.
Otero-Santos17, M. Palatiello22, D. Paneque8, F. R. Pantaleo20, R. Paoletti51,
J. M. Paredes3, M. Pech45,29, M. Pecimotika32, M. Peresano28, F. Pfeiffle33,
E. Pietropaolo66, G. Pirola8, C. Plard11, F. Podobnik51, V. Poireau11, V.
Pollet11, M. Polo18, E. Pons11, E. Prandini9, J. Prast11, G. Principe50, C.
Priyadarshi10, M. Prouza45, R. Rando9, W. Rhode19, M. Ribó3, C. Righi21, V.
Rizi66, G. Rodriguez Fernandez38, M. D. Rodríguez Frías30, T. Saito2, S.
Sakurai2, D. A. Sanchez11, T. Šarić40, Y. Sato67, F. G. Saturni6, V.
Savchenko60, B. Schleicher33, F. Schmuckermaier8, J. L. Schubert19, F.
Schussler68, T. Schweizer8, M. Seglar Arroyo11, T. Siegert33, R. Silvia27, J.
Sitarek39, V. Sliusar69, A. Spolon9, J. Strišković42, M. Strzys2, Y. Suda37,
H. Tajima63, M. Takahashi63, H. Takahashi37, J. Takata2, R. Takeishi2, P. H.
T. Tam2, S. J. Tanaka67, D. Tateishi70, P. Temnikov54, Y. Terada70, K.
Terauchi62, T. Terzic32, M. Teshima8,2, M. Tluczykont12, F. Tokanai41, D. F.
Torres71, P. Travnicek45, S. Truzzi51, A. Tutone6, M. Vacula29, P. Vallania28,
J. van Scherpenberg8, M. Vázquez Acosta17, I. Viale9, A. Vigliano22, C. F.
Vigorito28,72, V. Vitale38, G. Voutsinas23, I. Vovk2, T. Vuillaume11, R.
Walter69, Z. Wei71, M. Will8, T. Yamamoto73, R. Yamazaki67, T. Yoshida47, T.
Yoshikoshi2, N. Zywucka39, 1Department of Physics, Tokai University.
2Institute for Cosmic Ray Research, University of Tokyo. 3Departament de
Física Quàntica i Astrofísica, Institut de Ciències del Cosmos, Universitat de
Barcelona, IEEC-UB. 4Instituto de Astrofísica de Andalucía-CSIC. 5EMFTEL
department and IPARCOS, Universidad Complutense de Madrid. 6INAF -
Osservatorio Astronomico di Roma. 7INFN Sezione di Napoli. 8Max-Planck-
Institut für Physik. 9INFN Sezione di Padova and Università degli Studi di
Padova. 10Institut de Fisica d’Altes Energies (IFAE), The Barcelona Institute
of Science and Technology. 11LAPP, Univ. Grenoble Alpes, Univ. Savoie Mont
Blanc, CNRS-IN2P3, Annecy. 12Universität Hamburg, Institut für
Experimentalphysik. 13Graduate School of Science, University of Tokyo.
14Universidad del Azuay. 15INAF - Osservatorio di Astrofisica e Scienza dello
spazio di Bologna. 16Centro Brasileiro de Pesquisas Físicas. 17Instituto de
Astrofísica de Canarias and Departamento de Astrofísica, Universidad de La
Laguna. 18CIEMAT. 19Department of Physics, TU Dortmund University. 20INFN
Sezione di Bari and Politecnico di Bari. 21INAF - Osservatorio Astronomico di
Brera. 22INFN Sezione di Trieste and Università degli Studi di Udine.
23University of Geneva - Département de physique nucléaire et corpusculaire.
24INFN Sezione di Catania. 25INAF - Istituto di Astrofisica e Planetologia
Spaziali (IAPS). 26Aix Marseille Univ, CNRS/IN2P3, CPPM. 27INFN Sezione di
Bari and Università di Bari. 28INFN Sezione di Torino. 29Palacky University
Olomouc, Faculty of Science. 30University of Alcalá UAH. 31Port d’Informació
Científica. 32University of Rijeka, Department of Physics. 33Institute for
Theoretical Physics and Astrophysics, Universität Würzburg. 34Institut für
Theoretische Physik, Lehrstuhl IV: Plasma-Astroteilchenphysik, Ruhr-
Universität Bochum. 35INFN Sezione di Roma La Sapienza. 36ILANCE, CNRS .
37Physics Program, Graduate School of Advanced Science and Engineering,
Hiroshima University. 38INFN Sezione di Roma Tor Vergata. 39Faculty of Physics
and Applied Informatics, University of Lodz. 40University of Split, FESB.
41Department of Physics, Yamagata University. 42Josip Juraj Strossmayer
University of Osijek, Department of Physics. 43INFN Dipartimento di Scienze
Fisiche e Chimiche - Università degli Studi dell’Aquila and Gran Sasso Science
Institute. 44Yukawa Institute for Theoretical Physics, Kyoto University. 45FZU
- Institute of Physics of the Czech Academy of Sciences. 46Astronomical
Institute of the Czech Academy of Sciences. 47Faculty of Science, Ibaraki
University. 48Faculty of Science and Engineering, Waseda University.
49Institute of Particle and Nuclear Studies, KEK (High Energy Accelerator
Research Organization). 50INFN Sezione di Trieste and Università degli Studi
di Trieste. 51INFN and Università degli Studi di Siena, Dipartimento di
Scienze Fisiche, della Terra e dell’Ambiente (DSFTA). 52Escuela Politécnica
Superior de Jaén, Universidad de Jaén. 53Saha Institute of Nuclear Physics.
54Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of
Sciences. 55Dipartimento di Fisica e Chimica ’E. Segrè’ Università degli Studi
di Palermo. 56Grupo de Electronica, Universidad Complutense de Madrid.
57Hiroshima Astrophysical Science Center, Hiroshima University. 58School of
Allied Health Sciences, Kitasato University. 59RIKEN, Institute of Physical
and Chemical Research. 60Laboratory for High Energy Physics, École
Polytechnique Fédérale. 61Charles University, Institute of Particle and
Nuclear Physics. 62Division of Physics and Astronomy, Graduate School of
Science, Kyoto University. 63Institute for Space-Earth Environmental Research,
Nagoya University. 64Kobayashi-Maskawa Institute (KMI) for the Origin of
Particles and the Universe, Nagoya University. 65Graduate School of
Technology, Industrial and Social Sciences, Tokushima University. 66INFN
Dipartimento di Scienze Fisiche e Chimiche - Università degli Studi
dell’Aquila and Gran Sasso Science Institute. 67Department of Physical
Sciences, Aoyama Gakuin University. 68IRFU, CEA, Université Paris-Saclay.
69Department of Astronomy, University of Geneva. 70Graduate School of Science
and Engineering, Saitama University. 71Institute of Space Sciences (ICE-CSIC),
and Institut d’Estudis Espacials de Catalunya (IEEC), and Institució Catalana
de Recerca I Estudis Avançats (ICREA). 72Dipartimento di Fisica - Universitá
degli Studi di Torino. 73Department of Physics, Konan University.