Using Artificial Intelligence in the Reconstruction of Signals from the PADME Electromagnetic Calorimeter

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Citation: Dimitrova, K.; on behalf of

the PADME Collaboration. Using

Artiﬁcial Intelligence in the

Reconstruction of Signals from the

PADME Electromagnetic Calorimeter.

Preprints 2022,6, 46. https://doi.

org/10.3390/instruments6040046

Academic Editors: Fabrizio Salvatore,

Alessandro Cerri, Antonella De Santo

and Iacopo Vivarelli

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Attribution (CC BY) license (https://

creativecommons.org/licenses/by/

4.0/).

Article

Using Artiﬁcial Intelligence in the Reconstruction of Signals from

the PADME Electromagnetic Calorimeter

Kalina Dimitrova * and on behalf of the PADME collaboration †

Faculty of Physics, Soﬁa University “St. Kliment Ohridski”, 5 J. Bourchier Blvd., 1164 Soﬁa, Bulgaria

*Correspondence: kalina@phys.uni-soﬁa.bg

† The PADME collaboration: A.P. Caricato, M. Martino, I. Oceano, F. Oliva, S. Spagnolo (INFN Lecce and

Salento Univ.), G. Chiodini (INFN Lecce), F. Bossi, R. De Sangro, C. Di Giulio, D. Domenici, G. Finocchiaro,

L.G. Foggetta, M. Garattini, A. Ghigo, P. Gianotti, I. Sarra, T. Spadaro, E. Spiriti, C. Taruggi, E. Vilucchi

(INFN Laboratori Nazionali di Frascati), V. Kozhuharov (Faculty of Physics, Soﬁa Univ. “St. Kl. Ohridski” and

INFN Laboratori Nazionali di Frascati), S. Ivanov, Sv. Ivanov, R. Simeonov (Faculty of Physics, Soﬁa Univ.

“St. Kl. Ohridski”), G. Georgiev (Soﬁa Univ. “St. Kl. Ohridski” and INRNE Bulgarian Academy of Science),

F. Ferrarotto, E. Leonardi, P. Valente, A. Variola (INFN Roma1), E. Long, G.C. Organtini, G. Piperno, M. Raggi (INFN

Roma1 and “Sapienza” Univ. Roma), S. Fiore (ENEA Frascati and INFN Roma1), V. Capirossi, F. Iazzi, F. Pinna

(Politecnico of Torino and INFN Torino), A. Frankenthal (Princeton University).

Abstract:

The PADME apparatus was built at the Frascati National Laboratory of INFN to search for a

dark photon (

) produced via the process

e+e−→A0γ

. The central component of the PADME detector

is an electromagnetic calorimeter composed of 616 BGO crystals dedicated to the measurement of the

energy and position of the ﬁnal state photons. The high beam particle multiplicity over a short bunch

duration requires reliable identiﬁcation and measurement of overlapping signals. A regression machine-

learning-based algorithm has been developed to disentangle with high efﬁciency close-in-time events

and precisely reconstruct the amplitude of the hits and the time with sub-nanosecond resolution. The

performance of the algorithm and the sequence of improvements leading to the achieved results are

presented and discussed.

Keywords: dark photon; calorimetry; signal reconstruction; machine learning

1. Introduction

In recent years, the search for an explanation of the Dark Matter phenomenon has led to

the development of various hypotheses for an extension of the Standard Model, e.g., Weakly

Interacting Massive Particles (WIMPs) [

]. However, the non-observation of new states with

mass in the order of 100 GeV led scientists to explore other Dark Matter explanations. The

main goal of PADME (Positron Annihilation into Dark Matter Experiment) [

] is to search for

the dark photon

, a hypothetical gauge boson connecting the dark and the visible sector.

In the case of non-vanishing interaction strength

α0

with the electrons,

can be produced in

the annihilation process of beam positrons with electrons from the target:

e+e−→A0γ. (1)

Knowing the four-momenta of the beam’s positrons, the electrons at rest and the photon

produced in the process, the missing mass of the dark photon can be calculated:

miss = (Pe++Pe−−Pγ)2. (2)

The positron beam provided by the DA

NE LINAC [

] can reach energies up to 550 MeV,

providing a limit for the missing mass of 23.7 MeV, and is composed of bunches with a 50 Hz

rate. Each bunch contains about 2

particles and its length can be varied with typical

values of 200–300 ns.

arXiv:2210.00811v1 [hep-ex] 3 Oct 2022

2 of 9

The two main processes contributing to the background, are the annihilation

e+e−→

γγ(γ)and Bremsstrahlung events e+N→e+Nγ.

To suppress the background from

e+e−→γγ(γ)

, the PADME experiment should have

high photon detection efﬁciency, while for the rejection of

e+N→e+Nγ

, the radiating positron

should be detected and a reliable matching in time between the positron and the emitted

photon should be assured.

The initial studies based on a full Geant4 [

] simulation indicate that the PADME experi-

ment can reach sensitivies in

α0

down to 10

−8

[

] with high-efficiency detectors (greater than

99%) and time resolution better than 1 ns. In addition, due to the high bunch multiplicity,

double-pulse separation capabilities are required for each of the chosen detectors.

2. The Padme Experiment

A sketch of the PADME experiment is shown in Figure 1. A short description of its major

detector components [6] follows.

Figure 1. Outline of the PADME experiment.

2.1. Active Target

The target [

] is composed of polycrystalline diamond (Z = 6) since low Z is required

to increase the annihilation to bremsstrahlung cross-section ratio. The target has a 100

thickness and 20 mm width and length. Apart from providing the target for the annihilation

process, it also measures the beam’s multiplicity and XY proﬁle. For this reason 16 horizontal

and 16 vertical graphite electrodes of 1 mm width are engraved onto the target using an

excimer laser.

2.2. Charged Particle Detectors

Three sets of detectors register the charged particles. The beam positrons may lose energy

in the target and produce Bremsstrahlung photons, detected by the electromagnetic calorimeter,

which need to be rejected. This is achieved by coinciding these photons with the particles that

produced them. These particles are detected by the positron and high energy positron vetoes.

In case the

decays into an

e+e−

pair, the electron will be registered by the electron veto. All

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Citation:Dimitrova,K.;onbehalfofthePADMECollaboration.UsingArticialIntelligenceintheReconstructionofSignalsfromthePADMEElectromagneticCalorimeter.Preprints2022,6,46.https://doi.org/10.3390/instruments6040046AcademicEditors:FabrizioSalvatore,AlessandroCerri,AntonellaDeSantoandIacopoVivarelliPublishe...

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Using Artificial Intelligence in the Reconstruction of Signals from the PADME Electromagnetic Calorimeter

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