In this work, we utilize Quantum Deep Reinforcement Learning as method to learn navigation tasks
for a simple, wheeled robot in three simulated environments of increasing complexity.  We show
similar performance of a parameterized quantum circuit trained with well established deep
reinforcement learning techniques in a hybrid quantum-classical setup compared to a classical baseline.
To our knowledge this is the first demonstration of quantum machine learning (QML) for robotic
behaviors. Thus, we establish robotics as a viable field of study for QML algorithms and
henceforth quantum computing and quantum machine learning as potential techniques for future
advancements in autonomous robotics.  Beyond that, we discuss current limitations of the
presented approach as well as  future research directions in the field of quantum machine
learning for autonomous robots.

AI-Enabled Robotics, Autonomous Agents, Reinforcement Learning, Quantum Computing, Quantum
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# SPADE: Self-supervised Pretraining for Acoustic DisEntanglement

###### Abstract

Self-supervised representation learning approaches have grown in popularity
due to the ability to train models on large amounts of unlabeled data and have
demonstrated success in diverse fields such as natural language processing,
computer vision, and speech. Previous self-supervised work in the speech
domain has disentangled multiple attributes of speech such as linguistic
content, speaker identity, and rhythm. In this work, we introduce a self-
supervised approach to disentangle room acoustics from speech and use the
acoustic representation on the downstream task of device arbitration. Our
results demonstrate that our proposed approach significantly improves
performance over a baseline when labeled training data is scarce, indicating
that our pretraining scheme learns to encode room acoustic information while
remaining invariant to other attributes of the speech signal.

Index Terms—  keyword spotting, source localization, self-supervised
pretraining, disentanglement, acoustics

##  1 Introduction

Disentanglement of speech into its multiple components is a fundamental
problem in signal processing with applications in voice conversion [1, 2, 3,
4], automatic speech recognition [5, 6, 7, 8], speaker recognition [9], and
privacy preservation in speech [10]. The goal of disentanglement is to
separate different attributes of the speech signal such that the final signal
representation will be invariant to attributes not relevant to the downstream
task. Previous work has focused on invariance towards acoustic content111In
this paper, “acoustic content”, or “acoustics” refers to the information in an
audio signal related to the Room Impulse Response (RIR) at a particular
location in a room when the source audio is played from a different, fixed
location. so that target attributes like speaker identity or linguistic
content will be emphasized. In this paper, we specifically want to preserve
acoustic content and extract representations that are invariant to all other
attributes in a self-supervised fashion. These representations are then used
for the task of device arbitration [11].

The device arbitration task has arisen recently due to the ubiquity of smart
voice assistant-enabled devices, which we refer to as “voice assistants” (VA)
or “devices” interchangeably. Many households now have multiple VAs in the
same room, leading to ambiguity with respect to which device should interact
with the user. When the user wishes to begin interaction with a VA, they must
first utter a wakeword (“Alexa”, “Hey Google”, etc.) which is then recorded by
all devices in the room. For the most natural user experience, only the
intended device from the user should wake up and continue to interact with the
user. This leads to the device arbitration problem: given $N$ recordings of a
source audio, where each recording comes from a VA, determine which VA is the
intended one.

Note that device arbitration is closely related to the well-studied source
localization problem [12]. Time Difference Of Arrival (TDOA) [13] is an
effective technique that solves source localization for audio signals but
unfortunately requires large arrays of microphones not present on VAs. There
are several other constraints imposed by modern VAs that make device
arbitration a non-trivial problem: (1) The positions of each VA are unknown
and could change over time (moving the device from the living room to the
kitchen). (2) The acoustic environment of the VAs is unknown. (3) Clock
synchronization between devices is not always available, making TDOA between
devices infeasible. Given these constraints, the device arbitration task must
be solved by only relying on the room acoustic information present in each
audio recording.

Motivated by these constraints, we propose SPADE: Self-supervised Pretraining
for Acoustic DisEntanglement. SPADE is a pretraining technique that
disentangles acoustic information from speech signals by using multiple views
of a source audio without the need for labels. We find that when used in
combination with previous work on device arbitration [11], SPADE leads to
improved performance when less labeled training data is present. Given that
labeled data is difficult to collect for this task, SPADE is an invaluable
technique for improving performance on device arbitration at zero additional
inference cost.

The remainder of our paper is organized as follows: In Section 2 we discuss
prior work on device arbitration and related tasks. In Section 3, we discuss
the data used in our experiments and the simulation process from which it is
generated. In Section 4, we detail our proposed pretraining approach. In
Section 5, we discuss our experimental setup and in Section 6 we discuss
results. The paper ends with conclusions and suggestions for future work in
Section 7.

##  2 Prior Work

While device arbitration is a relatively new problem, it is closely related to
source localization [12, 13]. The goal of source localization is to determine
the position of the object emitting sound, i.e. the source. Current techniques
rely on large arrays of microphones which are not available given our problem
setup. Previous work [11] also demonstrated that directly predicting source
distances from each device and making arbitration decisions based on those
distances resulted in worse performance.

The goal in arbitration is to select an attended device the user is speaking
to based on distance and direction. Common techniques compare signal
attributes like Signal-to-Noise Ratio (SNR), estimated distance between source
and microphone, cross-correlation, etc. [14]. The goal of device arbitration
is different from channel selection [14, 15] since the optimal device is
simply defined as that which is closest to the user or as the attended device
(defined as the one in the look direction of the user or any other acoustical
or visual relevant cue), and not that which leads to better performance on
another downstream task. The differences in problem setup and objective
between device arbitration and source localization show that device
arbitration should be studied separately and has its own set of unique
challenges that make it an interesting problem.

We used as a baseline, the machine learning-based device arbitration proposed
by Barber et al. [11]. The authors proposed an end-to-end approach to train a
neural network to perform device arbitration. Their model consists of a small
convolutional feature extractor that runs locally on each device, and a larger
arbitration network that runs in the cloud to make the final decision. Results
demonstrated significantly improved performance across a variety of room
conditions over a simple energy-based approach.

##  3 Dataset Simulation

Currently, there is no large-scale dataset for device arbitration with known
ground truth labels, so we run our experiments with simulated data following
the three main steps described in Barber et al. [11]:

  * •

Sample scenario: Sample a room from a variety of acoustic settings (room
length/width, noise sources) as well as device/speaker positions within the
room. Given device/speaker positions, generate the arbitration label based on
smallest Euclidean distance from device to speaker.

  * •

Generate RIR: Given the sampled scenario, generate a Room Impulse Response
(RIR) for each device in the scenario using an acoustic simulator.

  * •

Generate audio: Convolve speech utterances with generated RIRs and mix with
noise for each device. After this step, we have an artificial dataset of
device arbitration audio and corresponding ground truth labels.

We use the Image Source Method (ISM) [16] for data simulation and source audio
from [11], but have updated the scene sampling hyperparameters to those in
Table 1.

Parameter | Distribution
---|---
Room length/width (m) | Uniform(3.0, 10.0)
Room height (m) | Uniform(2.5, 6.0)
Reverberation time (s) | Beta(1.1, 3.0)
Number of devices | ShiftedPoisson(m=3,l=2,h=15)
Number of noise sources | ShiftedPoisson(m=2,l=1,h=5)
Speech level (dB SPL) | Uniform(55.0, 70.0)
Noise level (dB SPL) | Uniform(50.0, 70.0)
Table 1: Hyperparameters for scene sampling. For the ShiftedPoisson
distribution we denote mean with “m”, low with “l” and high with “h.”

##  4 Method

Our arbitration model is based on that proposed by Barber et al. [11] and is
composed of two components: the feature encoder and the classifier that makes
the arbitration decision. Prior to end-to-end training of the encoder and
classifier, we pretrain the encoder using two schemes: contrastive and
reconstructive pretraining. Our preprocessing pipeline and pretraining schemes
are discussed in the following subsections.

###  4.1 Audio Preprocessing

Audio is first transformed to log-filterbank energy (LFBE) features, where the
spectrogram is computed using a 25ms frame size and a frame skip of 10ms. The
Mel transform is applied to the spectrogram with 64 Mel bands, followed by the
log transform. LFBE features are mean and variance normalized before
downstream processing by neural models.

###  4.2 Encoder Architecture

The encoder architecture is a residual convolutional model composed of 18
convolutional layers, with batch normalization and the ReLU activation
function applied to each layer. The encoder model is the same for all
approaches discussed in this paper (pretraining, baseline). This network
produces a sequential feature representation of the input with much smaller
temporal resolution than the input LFBE features. During pretraining we use a
small Transformer [17] network to map the sequence of vectors output by the
encoder to a single vector. This network is discarded after pretraining but is
implicit as part of the encoder in the following pretraining discussions and
Figs. 1 and 2.

Fig. 1: Contrastive pretraining scheme: Acoustic encoder has shared weights.

###  4.3 Contrastive Pretraining

Contrastive loss functions have been shown to create high-quality
representations across a variety of domains like speech and natural language
processing [18, 19]. This family of loss functions operates by assuming data
are similar or dissimilar with respect to a particular attribute and
encouraging embeddings of the data to reflect these relationships via distance
in an embedding space. In a device arbitration scene, audio from the same
device has the same room acoustic properties, while audio from different
devices will have different acoustic properties.222 This is due to the
assumption that the devices are in different locations. Since we want to
encode room acoustic information, we can encourage embeddings of audio from
the same device to be similar and simultaneously encourage embeddings of audio
from different devices to be orthogonal (see Fig. 1). Since speech content
will be the same across all $N$ recordings from a given room, the embedding
should be invariant to speech content and only encode acoustic information.

Let us denote the audio recorded by device $a$ as $x^{a}$. For this
pretraining approach, we create a fixed-length embedding $z^{a}$ of $x^{a}$ by
passing through the encoder333The Transformer network discussed in Section 4.2
is implied here to follow the encoder to create the fixed-length
representation., i.e. $z^{a}=\text{encoder}(\text{LFBE}(x^{a}))$. Then $z^{a}$
is normalized to have magnitude one. We can denote a continuous slice of
$x^{a}$ from time $t_{1}$ to $t_{2}$ as $x^{a}_{t_{1}:t_{2}}$ where
$t_{2}>t_{1}$ and its corresponding fixed-length embedding as
$z^{a}_{t_{1}:t_{2}}$. Given $N$ recordings $x^{1},x^{2},...,x^{N}$ that are
zero-padded to the same length $T$ and a splitting index $t_{split}$ such that
$0<\frac{T}{2}-\epsilon<t_{split}<\frac{T}{2}+\epsilon<T$, where $\epsilon$ is
a small random jitter, we arrive at our loss function $\mathcal{L}_{C}$ for
one arbitration scenario:

$\mathcal{L}_{1}=\sum_{i=1}^{N}\sum_{j=1}^{N}|\langle
z^{i}_{0:t_{split}},z^{j}_{t_{split}:T}\rangle-\delta_{ij}|$ (1)

$\mathcal{L}_{2}=\sum_{i=1}^{N}\sum_{j\neq i}|\langle
z^{i}_{0:t_{split}},z^{j}_{0:t_{split}}\rangle|+|\langle
z^{i}_{t_{split}:T},z^{j}_{t_{split}:T}\rangle|$ (2)

$\mathcal{L}_{C}=\mathcal{L}_{1}+\mathcal{L}_{2}$ (3)

where $\delta_{ij}$ is the Kronecker delta function. Note that
$\mathcal{L}_{1}$ encourages the two halves of the same audio recording to map
to the same embedding. The $\mathcal{L}_{2}$ term provides stronger
supervision for invariance to speech content by encouraging different
recordings of the same respective halves of the audio to be orthogonal.

###  4.4 Reconstructive Pretraining

Disentanglement of different attributes in speech has been accomplished
previously using autoencoding with an information bottleneck [1, 2]. We take a
similar approach to disentangle room acoustic information from speech in a
self-supervised fashion, making the assumption that the room acoustic
properties are constant444This stationarity assumption may not be true in all
cases (people/pets moving around), but it is reasonable given that audio is
only recorded over a two-second interval. for the duration of the wakeword
audio ($\sim$2s).

Fig. 2: Reconstruction pretraining scheme: Speech encoder, envelope, and
reconstruction network have same time axis dimension as the LFBE time axis.
Acoustic encoder creates fixed-length embedding that is copied along time axis
of speech encoder and envelope output. Diagonally-shaded units are from non-
learnable modules.

Our model consists of a speech encoder, an acoustic encoder, and a
reconstruction decoder. The speech encoder $S(\cdot)$ and reconstruction
decoder $R(\cdot)$ are Transformers [17] and each produce a sequence of
vectors. We design the acoustic encoder $A(\cdot)$ to produce a fixed-
dimensional embedding due to the stationarity assumption of the acoustics and
the need to create an information bottleneck. Given that $N$ recordings of a
source audio all contain identical speech content, the speech representation
$s_{i}=S(x^{i})$ should be the same for each recording, i.e. $s^{1}\approx
s^{2}\approx...\approx s^{N}$. To encourage this, we reconstruct one audio
recording’s LFBE features using its acoustic embedding and the speech
embedding from another audio recording in the room. The information bottleneck
enforced by creating a small fixed-dimensional embedding encourages the
acoustic encoder only to represent information not common to all signals, i.e.
room acoustics. Given that the $N$ recordings are not time-aligned, we provide
alignment information to the decoder by extracting the envelope $env(x^{i})$
of the LFBE features for $x^{i}$, which is the mean of the feature vector at
each timestep:

$env(x^{i})_{k}=\frac{1}{N}\sum_{q=1}^{N}\text{LFBE}(x^{i})_{kq}$ (4)

where $k$ denotes the time axis, $q$ denotes the feature axis of the LFBE
feature matrix, and $N=64$. Note that the envelope extractor is not a
learnable module but rather a simple mean operation at each timestep to
produce a low-dimensional representation of the LFBE features (feature vector
$\rightarrow$ scalar). We can formally write our loss function
$\mathcal{L}_{R}$ for one arbitration scenario as:

$\mathcal{L}_{R}=\frac{1}{N}\sum_{i=1}^{N}||\text{LFBE}(x^{i})-R(S(x^{\neq
i}),A(x^{i}),env(x^{i}))||_{2}^{2}$ (5)

where $x^{\neq i}$ denotes a randomly-selected audio recording from
$\\{x^{1},x^{2},...,x^{N}\\}$ other than $x^{i}$. We form the input to
$R(\cdot)$ by copying $A(x^{i})$ along the time axis and concatenating it to
$S(x^{\neq i})$ and $env(x^{i})$. See Fig. 2 for a diagram detailing this
process.

###  4.5 Arbitration Classifier Architecture

The arbitration classifier was implemented previously [11] as a Multilayer
Perceptron (MLP) network, but we found further improvement using a self-
attention network like the Transformer [17]. For each device $i$, the encoder
outputs a sequence of hidden states $h^{i}_{1},h^{i}_{2},...,h^{i}_{K}$. For
$N$ devices, the hidden states are concatenated along the time axis to form
the sequence:

$H=h^{1}_{1},h^{1}_{2},...,h^{1}_{K},h^{2}_{1},h^{2}_{2},...h^{2}_{K},...,h^{N}_{1},h^{N}_{2},...h^{N}_{K}$
(6)

which is then passed through a network of self-attention layers555Positional
encodings are added to the input. to produce the sequence:

$G=g^{1}_{1},g^{1}_{2},...,g^{1}_{K},g^{2}_{1},g^{2}_{2},...g^{2}_{K},...,g^{N}_{1},g^{N}_{2},...g^{N}_{K}$
(7)

Each sequence $g^{i}_{1},g^{i}_{2},...,g^{i}_{K}$ is then passed through a
second network of self-attention to create a summary $G_{i}$ over time. Each
$G_{i}$ is then passed through a two-layer feedforward neural network,
outputting a scalar logit for device $i$. The logits are then passed through a
softmax layer to produce the arbitration probabilities. The entire
classification network is optimized using the crossentropy loss between the
arbitration probabilities and the ground truth label distribution.

###  4.6 Baseline

The contributions of this paper are the self-supervised pretraining
approaches, so as a baseline, we use the encoder and classifier networks
discussed previously but do not pretrain the encoder.

##  5 Experiments

The training dataset consists of 300k arbitration scenarios. To demonstrate
the effectiveness of pretraining for learning representations useful for
device arbitration, we create datasets of exponentially decreasing size. For
dataset $i$ the training set size $s_{i}=\lfloor S/4^{i}\rfloor$, where
$S=$300k (full training set size). We choose $i\in\\{0,1,2,3\\}$ such that the
smallest training set consists of $\sim$ 4.7k scenarios.

###  5.1 Experimental Procedure

For each experiment, we choose the final arbitration model based on the
checkpoint with the lowest validation loss and evaluate on a held-out test
set. We have four experiment setups:

  * •

Baseline: Train encoder-classifier model end-to-end on each of the training
data subsets of size $s_{i}$ for $i\in\\{0,1,2,3\\}$.

  * •

Contrastive: Pretrain (acoustic) encoder using contrastive approach on all
available training data (300k scenarios, no labels involved). Pick best
validation checkpoint as initialization for encoder and then finetune encoder-
classifier model end-to-end on each training data subset of size $s_{i}$ for
$i\in\\{0,1,2,3\\}$.

  * •

Reconstructive: Same as contrastive setup except that we use reconstructive
pretraining.

  * •

Combo: Pretrain using both contrastive and reconstructive pretraining. Loss
function becomes
$\mathcal{L}=\lambda\mathcal{L}_{R}+(1-\lambda)\mathcal{L}_{C}$ where we set
$\lambda=0.5$. Finetune encoder-classifier model as in previous setups.

##  6 Results

Results are presented here as relative error rate with respect to the
performance of the 4.7k baseline setting. Denoting the accuracy of the target
method $m$ as $\text{acc}_{m}$ and the accuracy of the 4.7k baseline setting
as $\text{acc}_{\text{base}}$, we compute relative error rate as:

$\text{err}_{\text{rel}}=\frac{1-\text{acc}_{m}}{1-\text{acc}_{\text{base}}}$
(8)

Our results are presented in Figure 3. The most important trend is that the
pretraining approaches outperform the baseline by a larger margin when the
training dataset is small. This demonstrates that our proposed pretraining
schemes preserve acoustic information, learning features relevant to the
device arbitration problem and are most beneficial when labeled training data
is scarce. Initialization of the encoder with a pretrained checkpoint helps
combat overfitting during the device arbitration training process. We also
find that the combination of contrastive and reconstructive pretraining does
not lead to any noticeable improvement over either approach in isolation,
indicating that both approaches may encode similar information.

Fig. 3: Relative Error Rate with respect to worst case.

##  7 Conclusion

In this paper we propose constrastive and reconstructive pretraining, two
forms of self-supervised representation learning, that disentangle acoustic
content from speech. Unlike previous work that has aimed to create
representations that are invariant to acoustic content, we aim to encode
acoustic content only and demonstrate its usefulness through the device
arbitration problem. We find that both of our proposed pretraining approaches
lead to improvement over the baseline, and that improvement is more
significant when the labeled training dataset is small. This provides
empirical evidence that our pretraining objectives lead to representations of
acoustic content that can be useful for the device arbitration task even in
the absence of a large training corpus.

Given that self-supervised techniques require no human annotations, it may be
possible to apply our proposed approaches to other research problems. For
example, our disentangled acoustic representations may be used for other
acoustic tasks like room acoustic property estimation or acoustic adaptation
for home theater. While not studied in this paper, the speech content
representations learned from reconstructive pretraining may be valuable for
acoustic-invariant applications like speaker recognition or ASR since they are
designed to encode all information except acoustics.

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[1]Slawomir Mianowski [2]Nicolas De Angelis

[1]National Centre for Nuclear Research, A. Soltana 7 Street, Otwock,
PL-05400, Poland

[2]DPNC, University of Geneva, 24 Quai Ernest-Ansermet, Geneva, CH-1205,
Switzerland

3]Institute of Nuclear Physics Polish Academy of Sciences, Radzikowskiego 152
Street, Krakow, PL-31342, Poland

# Proton irradiation of plastic scintillator bars for POLAR-2

<EMAIL_ADDRESS><EMAIL_ADDRESS>Kamil Brylew
Johannes Hulsman  Tomasz Kowalski  Sebastian Kusyk  Zuzanna Mianowska  Jerzy
Mietelski  Dominik Rybka  Jan Swakon  Damian Wrobel  *  *  [

###### Abstract

POLAR-2, a plastic scintillator based Compton polarimeter, is currently under
development and planned for a launch to the China Space Station in 2025. It is
intended to shed a new light on our understanding of Gamma-Ray Bursts by
performing high precision polarization measurements of their prompt emission.
The instrument will be orbiting at an average altitude of
$383\text{\,}\mathrm{k}\mathrm{m}$ with an inclination of
$42\text{\,}{}^{\circ}$ and will be subject to background radiation from
cosmic rays and solar events.

In this work, we tested the performance of plastic scintillation bars, EJ-200
and EJ-248M from Eljen Technology, under space-like conditions, that were
chosen as possible candidates for POLAR-2. Both scintillator types were
irradiated with $58\text{\,}\mathrm{M}\mathrm{e}\mathrm{V}$ protons at several
doses from $1.89\text{\,}\mathrm{G}\mathrm{y}$(corresponding to about 13 years
in space for POLAR-2) up to $18.7\text{\,}\mathrm{G}\mathrm{y}$, that goes far
beyond the expected POLAR-2 life time. Their respective properties, expressed
in terms of light yield, emission and absorption spectra, and activation
analysis due to proton irradiation are discussed. Scintillators activation
analyses showed a dominant contribution of $\beta^{+}$ decay with a typical
for this process gamma-ray energy line of
$511\text{\,}\mathrm{k}\mathrm{e}\mathrm{V}$.

###### keywords:

POLAR-2, plastic scintillator, radiation, protons, cosmic rays

##  1 Introduction

POLAR-2 is a space-borne polarimeter, that will be launched to the China Space
Station (CSS) in 2025 for a mission of at least 2 years. The CSS (and hence
POLAR-2) is orbiting at a typical altitude of
$383\text{\,}\mathrm{k}\mathrm{m}$ with an inclination of
$42\text{\,}{}^{\circ}$. As a result, it is exposed to radiation from cosmic
rays of galactic, solar and trapped origin [1].

The POLAR-2 detection principle is based on its predecessor mission POLAR [2].
Some improvements have been made in order to lower the low energy threshold
down to a few keV111This is the detection for a single bar event. The energy
threshold for polarization measurements is reduced from
$50\text{\,}\mathrm{k}\mathrm{e}\mathrm{V}$ down to
$30\text{\,}\mathrm{k}\mathrm{e}\mathrm{V}$, higher than the single bar
threshold since the photon need to deposit energy in at least two bars for
measuring polarization., like reducing the dead-space between channels with
wider bars and optimized mechanics as well as upgrading the photosensors used
to read out the scintillators from PhotoMultiplier Tubes (PMTs) to Silicon
PhotoMultipliers (SiPMs) in order to improve the light yield of the overall
system. The scintillator bars length has also been optimized to reduce the
background contribution while still having decent statistics for typical
Gamma-Ray Bursts (GRBs). The full polarimeter is composed of 100 modules, each
made of 64 plastic scintillators with dimensions222Note that the POLAR
scintillator bars had dimensions of
$5.8\text{\,}\mathrm{m}\mathrm{m}$$\times$$5.8\text{\,}\mathrm{m}\mathrm{m}$$\times$$176\text{\,}\mathrm{m}\mathrm{m}$.
$5.9\text{\,}\mathrm{m}\mathrm{m}$$\times$$5.9\text{\,}\mathrm{m}\mathrm{m}$$\times$$125\text{\,}\mathrm{m}\mathrm{m}$
(see Figure 1), resulting in 6400 plastic bars in total.

In the case of the POLAR-2 polarimeter, segmented into elongated scintillator
bars, we want the $\gamma$ photons to Compton scatter in a first bar, and
ideally be completely absorbed in a second bar. As depicted in Figure 1, the
scattering angle is linked to the polarization vector, as the photon
preferentially scatters orthogonal to that vector. Detecting many scattering
events will therefore lead to a scattering angle distribution which will
provide information on the polarization parameters (fraction and angle). In
order to optimize the instrument for Compton scattering down to a few keV, the
polarimeter requires a low-Z material, which explains the choice of plastic
scintillators.

Figure 1: Left - Compton scattering of an incoming $\gamma$-ray between two
scintillator bars [3]. Right - Assembled POLAR-2 target made of sixty four
EJ-248M bars wrapped in 3M™ Vikuiti and Toray Claryl®reflective foils.

POLAR-2’s predecessor successfully used EJ-248M scintillator bars. In this
paper we are comparing the optical properties of this scintillator with a new
EJ-200, which as manufacturer specification says [4], is characterized by a
higher light yield and a longer light attenuation length. Space-like
conditions were reproduced in the laboratory by dedicated 58 MeV proton
irradiation sessions.

##  2 Samples and irradiation setup

As it was presented in [3, 5] and shown in Figure 1, each of the 100 POLAR-2
detector modules consists of a target of 64 plastic scintillators. Figure 2
shows an example of two EJ-200 scintillators, which are visually
indistinguishable from EJ-248M. The left one, a plastic bar with size
$5.9\text{\,}\mathrm{m}\mathrm{m}$$\times$$5.9\text{\,}\mathrm{m}\mathrm{m}$$\times$$125\text{\,}\mathrm{m}\mathrm{m}$,
was chosen as a candidate for POLAR-2, while the one on the right, with a
cylindrical shape and dimensions of
$\Phi$$12.7\text{\,}\mathrm{m}\mathrm{m}$$\times$$25.4\text{\,}\mathrm{m}\mathrm{m}$,
is used as our reference point since the elongated shape of the bar may affect
the light yield.

Figure 2: Two EJ-200 plastic scintillators. Left - scintillator bar chosen for
POLAR-2. Right - cylindrical plastic as our reference point (see details in
the text).

EJ-200 plastic scintillator combines two important properties of long optical
attenuation length and high scintillation efficiency, whereas the softening
point is around 70∘C. EJ-248M has very similar properties except for a shorter
attenuation length and a lower scintillation efficiency, while being
sustaining higher temperatures. The higher softening temperature has been
achieved by using a specially modified variant of the conventional PVT base
plastic. Table 1 summarises basic properties of both types of scintillator
based on information provided by the manufacturer [4].

Properties | EJ-200 | EJ-248M
---|---|---
Density (g/cm3) | 1.023 | 1.023
Max. emission (nm) | 425 | 425
Scint. efficiency (photons/keV) | 10 | 9.2
Rise/decay time (ns) | 0.9/2.1 | 0.9/2.1
Light Attenuation Length (cm) | 380 | 250
Softening point (∘C) | 75 | 99
Table 1: Basic properties of EJ-200 and EJ-248M plastic scintillators,
considered as candidates for the POLAR-2 polarimeter [4].

###  2.1 Scintillators proton irradiation

All planned irradiation sessions were performed at the proton radiotherapy
facility [6] at the Institute of Nuclear Physics Polish Academy of Sciences
(IFJ PAN) in Krakow. The irradiation campaign was planned in order to
reproduce the integrated dose the POLAR-2 instrument will face during its
lifetime in orbit (which includes cosmic radiation background and solar events
as well as passages through the South Atlantic Anomaly). The total dose,
activation and optical performances of the plastic bars are of interest. This
analysis completes our previous studies, that were focused on silicon
photomultipliers properties in radiation environments [1]. Those
photodetectors were chosen to read the light from the plastic scintillator
bars, that are the subject of this paper.

A schematic of this facility, a photo of the exit of the beam line and proton
beam parameters were presented and more detailed described in [1, 7]. The dose
rate at which the scintillators are exposed in space, averaged over the entire
polarimeter, were computed using LEOBackground [8] and SPENVIS [9] for
modelling the radiation environment and Geant4 [10] for simulating the
radiation deposition in the bars. A detailed description of the simulation
setup is given in [1]. Figure 3 shows the dose distribution among the POLAR-2
modules, the outside channels being more exposed to cosmic radiations. Two
scenarios were considered to estimate the annual dose at which the plastic
scintillators are exposed: the so-called ’Bare scintillator’, where the
scintillator is placed directly in space without any shielding, and a more
realistic scenario referred as ’Full Instrument + CSS’, where scintillator
bars are shielded by the China Space Station (CSS) and other polarimeter
elements (the entire POLAR-2 design is therefore implemented in this latter
scenario). A dose rate of $2.38\times 10^{-1}$ Gy/yr is expected for the ’Bare
scintillator’ scenario while the ’Full Instrument + CSS’ scenario leads to a
yearly dose of $1.48\times 10^{-1}$ Gy. Table 2 shows the list of irradiated
plastic scintillator bar samples, their corresponding dose and equivalent time
in space for a both scenarios.

Sample | Fluence | Total Dose | Equivalent Years in Space for POLAR-2
---|---|---|---
Type | ($\frac{protons}{cm^{2}}$) | (Gy) | ’Full Instr. + CSS’ | ’Bare scintillator’
EJ-200-1 and EJ-248M-1 | $1.00\times 10^{9}$ | 1.89 | 12.8 | 7.94
EJ-200-2 and EJ-248M-2 | $3.00\times 10^{9}$ | 5.66 | 38.2 | 23.8
EJ-200-3 and EJ-248M-3 | $5.92\times 10^{9}$ | 11.2 | 75.7 | 47.1
EJ-200-4 and EJ-248M-4 | $9.93\times 10^{9}$ | 18.7 | 126 | 78.6
Table 2: An overview of irradiated plastic bars, their corresponding dose and
equivalent time in space for a ’Full Instrument + CSS’ and ’Bare plastic bar’
scenarios. Figure 3: Scintillators dose rate distribution among the 100
POLAR-2 polarimeter modules. The white marker on the colorbar shows the dose
rate averaged over the full instrument.

##  3 Radioluminescence emission and absorption spectra characterization

As a first comparison point, radioluminescence emission (RL) spectra were
measured for EJ-200 and EJ-248M scintillators before and after proton
irradiation.

The RL excitation was performed using a MiniX Amp-Tek X-ray tube operating at
20 $\mu$A and 20 kV. The luminescence spectrum was registered using a
Hamamatsu PMA-12 Photonic ccd analyzer measured in the range of 200 nm to 900
nm. The detector has a resolution lower than 2 nm. The emission spectrum was
read using optical fiber from the front scintillator surface (5.9$\times$5.9
mm2), while the X-ray tube and emitted X-ray photons were located
perpendicular to that surface. The experimental set-up is presented in figure
4. The integration time was 60 ms and the spectra have been averaged after 10
repetitions. The RL spectra obtained for three different samples of
EJ-200333One sample of each scintillator type was delivered later, since it
was used for activation measurements right after irradiation (see Section 5).
and averaged spectra for EJ-200 and EJ-248M before proton irradiation are
presented in figure 5. No significant variations of the emission spectrum are
observed between samples of the same material, and neither between EJ-200 and
EJ-248M, which agrees the manufacturer specification. Measurement
uncertainties are up to 5$\%$ in the 350-550 nm range.

Figure 4: Experimental set-up for radioluminescence emission of plastic bars.
Figure 5: Comparison of radioluminescence emission spectrum of plastic bars.
Left - three RL spectra for EJ-200 measured before irradiation. Right -
averaged after three samples for each type of scintillator RL spectra for
EJ-200 and EJ-248M before irradiation.

Data obtained for irradiated samples with doses presented in table 2 are shown
in figure 6. No significant changes in the RL spectra were observed after
irradiation, even for the highest dose. Statistical uncertainties on the
measurement intensity are less than 5%. A spectral deepening is observed after
irradiation in the 400-410 nm interval.

Figure 6: Comparison of radioluminescence emission spectrum of plastic bars.
Left - RL spectra for EJ-200 measured after irradiation for samples 1-4. Right
- RL spectra for EJ-248M measured after irradiation for samples 1-4. Black
lines show averaged signals before irradiation (see Figure 5)

As a second comparison point, the absorption spectra were measured after
proton irradiation for both materials. A Spectral Products ASB-XE-175EX xenon
lamp was used as a source of white light (Figure 7), while the same Hamamatsu
PMA-12 ccd detector previously mentioned was employed. The spectrum of the
xenon lamp was measured unobstructed by the investigated sample as a reference
measurement. Samples were then placed in the light beam and the spectrum was
recorded once again.

Figure 7: Experimental set-up for absorption measurements, where the light was
transmitted along scintillator length.

The absorption was measured both along shorter scintillator edge for all
samples (with the samples placed perpendicularly to the light source), and
along the scintillator length for samples 1 and 4 (the lowest and the highest
dose). In each case, the wavelength-dependent absorbance (A) was calculated
using the formula:

$\textnormal{A}(\lambda)=-\textnormal{ln}\frac{\textnormal{I}(\lambda)}{\textnormal{I}_{0}(\lambda)},$
(1)

where I0 is the initial beam intensity at a given wavelength $\lambda$ and I
is the intensity of the beam after passing through the sample. The integration
time was 100 ms and the measurements were repeated 60 times and then averaged.
Because we could not ensure exactly the same geometry for each sample during
the measurements, all spectra were normalized to unity, to enhance the
potential the spectral shape changes. Figure 8 shows the obtained results. As
in the case of the radioluminescence measurements, we did not observe any
significant changes between irradiated samples, and the highest absorption
range is between about 250 nm and 400 nm (or up to 420 nm when measuring along
the length of the bar).

Figure 8: Absorbance measured for irradiated sampled of EJ-200 (left) and
EJ-248M (right) scintillators. Solid lines correspond to the cases, where
measured was done along short scintillator edge (5.9 mm) and dashed lines -
along the longest scintillator edge.

In conclusion, both scintillator types show similar spectral properties.
Emission and absorption spectra did not show significant changes due to
irradiation with different doses. This confirms good radiation hardness
properties of EJ-200 and EJ-248M for the expected POLAR-2 dose ranges.

##  4 Position dependent scintillators’ light yield measurement

In this section we focused on optical light yield measurements as a function
of the distance from the photodetector surface for both types of
scintillators. For this purpose each scintillator bar was wrapped by high
reflectivity 3M Vikuiti foil, that is planned to be used in the final POLAR-2
detector design. To optimize the light collection, the plastic bars were
coupled to the photodetector using optical grease.

Figure 9: Experimental set-up scheme. Left - PMT, scintillator and collimator
configuration. Right - analog electronic readout scheme.

During those studies an electronic set-up based on analog readout was used. A
calibrated classical photomultiplier (PMT) Photonis XP2020Q was chosen for
light readout. This detector is characterized by very fast single photo-
electron (p.e.) response (FWHM about 2.4 ns) and average quantum efficiency of
about 25$\%$. The light-induced electrical pulse is sent from the voltage
divider to a fast charge preamplifier followed by a spectroscopic amplifier.
The shaped signal is finally recorded by a TUKAN 8K multichannel analyzer.
This set-up allowed us to improve the signal-to-noise ratio and optimize the
gain for measurements with a 137Cs $\gamma$-ray source. The same set-up was
chosen to determine the dark count PMT spectrum and the position of the single
p.e. peak, needed to determine the light yield. An example of dark count
spectrum is presented in Figure 10, where the single p.e. peak position is
computed performing a two components function fit. The first component of the
fit is a Gaussian function that characterizes the single p.e. peak, and the
second component is an exponential function, describing the falling edge of
the noise contribution, as can be seen at low ADC counts in the spectrum.

Figure 10: Example of dark count spectrum measured for PMT XP2020Q with single
photo-electron peak. Red curve shows the Gaussian fit used to determine the
peak position.

To determine the gamma-rays interaction point in a plastic scintillator, the
137Cs source was placed in a lead collimator (see Figure 9). The collimator
radius is $3\text{\,}\mathrm{m}\mathrm{m}$, value which is used as the
uncertainty on the height H above the PMT glass window.

The light yield as a function of distance along the z-axis in the units of
photo-electrons was calculated using following equation:

$\textnormal{N}_{p.e.}(z)=\frac{\textnormal{X}_{\gamma}(z)}{\textnormal{X}_{s.p.e.}}\cdot\frac{\textnormal{G}_{s.p.e}}{\textnormal{G}_{\gamma}},$
(2)

where X${}_{\gamma}(z)$ is the Compton edge position in the 137Cs $\gamma$-ray
energy spectrum, Xs.p.e. is a single photo-electron peak position, both in
channels units (see figure 10) and Gs.p.e, Gγ are the respective gain values
for the $\gamma$-ray energy spectrum and dark count spectrum. Figure 11-left
presents the $\gamma$-ray energy spectra measured for 662 keV gamma line from
137Cs and for EJ-200 plastic bar before proton irradiation for seven distances
ranging from 11 mm to 118 mm from the PMT window. Due to the low scintillator
density, only Compton edge region was clearly visible (the photo-peak at 662
keV was not detected). The Compton edge position, whose energy is around 476
keV, was determined each time by fitting with a sum of linear background and
complementary error function (Erfc):

$\textnormal{A}(ch,z)=\textnormal{A}_{0}+\textnormal{A}_{1}\cdot
ch+\textnormal{A}_{2}\cdot\textnormal{Erfc}((ch-\textnormal{X}_{\gamma}(z))/\textnormal{A}_{3}),$
(3)

where A0-3 are free fitting parameters. This parametrization determines the
Compton edge position at half of the Compton valley height444Other
parametrizations are also known, see for example [11], but they do not change
the main conclusions.. Figure 11-right shows the Compton edge position based
on this assumption.

Figure 11: Energy spectrum of 137Cs measured for EJ-200 plastic bar: left - as
a function of distance from PMT window, right - the position of Compton edge
(blue dashed line) determined by the fitting equation 3 (red line).

Finally, figure 12 shows the light yield as a function of the height over the
PMT window for EJ-200 and EJ-248M scintillator bars before and after proton
irradiation with doses up to 18.7 Gy. It can be seen that both types of
scintillator have similar light yield characteristics. In general, the number
of emitted photo-electrons is in the range of 500-600 p.e. for 476 keV at
distance of 11 mm, and these values are reduced to about 30$\%$-40$\%$ at 118
mm. As it was showed for emission and absorption spectrum, we did not observe
significant degradation with scintillator dose increase. Observed differences
may be explained, in our opinion, by calculated uncertainties (6$\%$-7$\%$)
and by more technical aspects like scintillators wrapping and coupling to the
PMT, and different techniques of surface polishing 555Manufacturer private
communication. The relation of the light yield with the scintillator polishing
quality will be investigated in a future publication about POLAR-2 module
optical simulations and characterization [12].. To confirm this observation,
the same scintillators type but with cylindrical shape ($\phi$ 0.5
inch$\times$1 inch) were measured in the same way. This time the 137Cs source
was placed directly on the sample top. Obtained values: (970$\pm$48) p.e. for
non-irradiated EJ-200 and (913$\pm$56) p.e. for non-irradiated EJ-248M seem to
confirm our expectations and obtained uncertainty ranges. At the same time,
these results show, that geometrical shape expressed by the scintillator
volume to surface area ratio (cylindrical/bar = 3.18/1.44) affects the light
yield value in a significant way. Number of photo-electrons is lower for
plastic bars by about 10$\%$-30$\%$.

Figure 12: Light yield of EJ-200 (top) and EJ-248M (bottom) expressed in
photo-electron units as a function of distance from PMT window for non-
irradiated and irradiated samples. The light yield change normalization point
was chosen as a value for non-irradiated scintillator at distance of 11 mm.

##  5 Activation analysis

The activation analysis performed in this section is similar to that performed
for silicon photomultiplier arrays (SiPMs) in our previous publication [1]. In
analog way, two High Purity Germanium detectors (HPGe) with very good energy
resolution were used to measure proton activation products for EJ-200 and
EJ-248M. Only samples irradiated with the highest dose of 18.7 Gy were
measured in this case. Both HPGe detectors were placed inside a low-background
lead protected chambers to decrease background radiation. About fifteen
minutes were required to move the irradiated samples from the experimental
hall and place them into the HPGe setups (as for the SiPMs). This introduced a
non-negligible time delay between the irradiation process and the start of
data acquisitions, that limits our detection ability of decay products with
decay times shorter than 3-4 minutes.

Figure 13 shows protons induced gamma-ray spectra in the energy range up to
2.5 MeV for both scintillators. Only one 511 keV gamma-line from 12C(p,n)11C
reaction was observed, where daughter nuclei mostly decay into $\beta^{+}$.
This identification was confirmed by computing the decay time corresponding to
this line. For this reason gamma-ray energy spectra were saved in a series (5
min intervals), where the number of counts for 511 keV line was determined for
each file. Figure 14 presents experimental data and fitted exponential curves
of the 511 keV line decay. The obtained decay time values are respectively
(1275$\pm$18) s and (1207$\pm$9) s for EJ-200 and EJ-248M. They are in good
agreement (within 3$\sigma$) with the $\beta^{+}$ decay time of 1221.83 s from
11C [13].

Figure 13: Gamma-ray energy spectrum measured with HPGe detectors after 58 MeV
proton scintillators irradiation. Black lines show experimental data. Red
lines present measured background normalized to the same live time for each
HPGe detector. Dashed blue lines show the identified 511 keV peaks. The pile-
up of this line was also observed at 1022 keV for one detector. Figure 14:
Decay time distributions of the 511 keV line (black points) measured for 58
MeV proton irradiated plastic scintillators with fitted exponential curves
(red lines). The experimental uncertainties are smaller than the bullet marker
size.

##  6 Summary and outlook

The POLAR-2 gamma-ray polarimeter exploits the low density of plastic
scintillators in order to maximise the Compton scattering cross section down
to a few keV. The Compton scattering angle distribution provide information
about the polarization parameters. In this paper, we compare the performances
of two types of plastic scintillators, namely EJ-200 and EJ-248M from Eljen
Technology, under space-like radiation conditions.

Our results show similar spectral properties for the two scintillator types,
including emission and absorption spectra as well as light yield. These
properties were not significantly affected by the 58 MeV proton irradiation
with several doses up to 18.7 Gy, that corresponds to about 80 years in low-
Earth orbit (383 km altitude, 42∘ inclination) for a non-shielded
scintillator. The starting value of emitted photo-electrons for all samples
(500-600 p.e./476 keV) at a distance of 11 mm was reduced to about 30-40$\%$
at 118 mm, which is related to the light yield attenuation with increasing
light distance inside the scintillator material. Obtained photo-electron
numbers for a plastic bars are also lower comparing to the smaller cylindrical
scintillator size.

Furthermore, a proton activation analysis was performed. The results show the
presence of the 511 keV line for both scintillators in the energy spectrum
measured with the HPGe detector. Observed line corresponds to $\beta^{+}$
decay of 11C populated in proton reaction, for which the decay time is about
1222 s. This time being shorter than the CSS orbital period, we do not expect
the activation products from the scintillators to provide a significant
contribution in the polarimeter response degradation.

The radiation hardness of their optical properties makes both EJ-200 and
EJ-248M well suited to be used for the POLAR-2 GRB polarimeter. Since the
optical quality of the scintillators were tested to doses much higher than
what we expect for POLAR-2, this conclusion also stands for longer life-time
experiments, or for experiments with higher inclination/altitude whose orbit
would be more exposed to space radiation.

Since both types of scintillators show similar degradation behavior under
harsh radiation conditions, the choice between EJ-200 and EJ-248M for the
final POLAR-2 design is mainly driven by their physical properties, like
scintillator softening point and its surface polishing quality. The first
factor suggests an advantage of EJ-248M, but the final decision will be made
based on light transport simulations, that will take the second factor into
consideration as well as the scintillator shape. Based on preliminary optical
simulation results and optical light yield measurement in the lab, the EJ-248M
type shows better performances for the POLAR-2 scintillator shape. This will
be further studied in a future publication about POLAR-2’s module optical
characterization and simulation.

In this work, we have shown that the amount of light collected by the
photodetector may be affected by the size and shape of the scintillators, and
that plastic scintillators are highly resistant to space-like radiation
conditions. The obtained data can be used to estimate the performance
degradation of the two types of studied plastic scintillators after a given
exposed dose. This can be useful to define the life-time of future experiments
wanting to employ such scintillators in radiation environment. Performed
activation analysis will also allow to determine the main contribution of
induced radiation, which may affect the detector (as a whole system) and the
data quality.

Acknowledgments We gratefully acknowledge the Swiss Space Office of the State
Secretariat for Education, Research and Innovation (ESA PRODEX Programme)
which supported the development and production of the POLAR-2 detector. N.D.A.
acknowledges the support of the Swiss National Science Foundation.

## Declarations

### Funding

The (co-)authors are funded by the funding the agencies described in the
acknowledgment section.

### Conflicts of Interest

The authors declare that the research was conducted in the absence of any
commercial or financial relationships that could be construed as a potential
conflict of interest.

### Consent to participate

Not applicable.

### Consent for publication

Not applicable.

### Code availability

Not applicable.

### Author’s Contribution

The main author is Slawomir Mianowski. The first draft of the manuscript was
written by Slawomir Mianowski and Nicolas De Angelis, and all authors
commented on previous versions of the manuscript.

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