# Wespeaker baselines for VoxSRC2023

###### Abstract

This report showcases the results achieved using the wespeaker toolkit for the
VoxSRC2023 Challenge. Our aim is to provide participants, especially those
with limited experience, with clear and straightforward guidelines to develop
their initial systems. Via well-structured recipes and strong results, we hope
to offer an accessible and good enough start point for all interested
individuals. In this report, we describe the results achieved on the
VoxSRC2023 dev set using the pretrained models, you can check the CodaLab
evaluation server for the results on the evaluation set. Any feedback and
contribution are always welcome

Index Terms: wespeaker, voxsrc2023

##  1 The VoxSRC Challenges

The VoxSRC (VoxCeleb Speaker Recognition Challenge) is an annual competition
that focuses on the task of speaker recognition using the VoxCeleb dataset.
Speaker recognition is a field within audio processing that aims to identify
and authenticate individuals based on their unique vocal characteristics.

The VoxSRC Challenge serves as a platform for researchers and practitioners to
showcase their advancements in speaker recognition technology. It provides a
standardized evaluation framework, allowing participants to compare their
methods and algorithms against each other.

VoxSRC 2023 consists of four tracks, which are consistent with the previous
year’s competition. Tracks 1, 2, and 3 are dedicated to speaker verification,
where participants are required to determine whether two speech samples
originate from the same person. The evaluation for Tracks 1 and 2 will be
conducted on the same dataset, with Track 1’s training data restricted to the
VoxCeleb2 dev set, while participants can freely use any data for Track 2.

Track 3 aims to promote domain adaptation research, providing an evaluation
set from another domain (CnCeleb dataset). It includes a large set of
unlabelled data and a small set of labelled data from the target domain to
serve as the adaptation data. The objective is to address the challenges of
adapting speaker verification models to different domains.

On the other hand, Track 4 focuses on speaker diarisation, challenging
participants to accurately segment multi-speaker audio into distinct portions
that correspond to individual speakers. This track addresses the problem of
determining “who spoke when” in a given audio recording.

Table 1:  Table 1 presents the results achieved using different architectures on the VoxCeleb dataset and VoxSRC2023 development set. The ”dev” portion of part 2 is used as the training set. These results serve as a baseline for Track 1 and Track 2 in the VoxSRC challenge. The $p_{target}$ value is set to $0.05$ for the voxsrc23_val dataset, while for other datasets, it is set to $0.01$. Architecture | voxceleb1_O | voxceleb1_E | voxceleb1_H | voxsrc23_val
---|---|---|---|---
EER(%) | minDCF | EER(%) | minDCF | EER(%) | minDCF | EER(%) | minDCF
CAM++ | 0.654 | 0.087 | 0.805 | 0.092 | 1.576 | 0.164 | 3.899 | 0.211
ECAPA-TDNN | 0.728 | 0.099 | 0.929 | 0.100 | 1.721 | 0.169 | 4.392 | 0.228
ResNet34 | 0.723 | 0.069 | 0.867 | 0.097 | 1.532 | 0.146 | 3.660 | 0.213
ResNet221 | 0.505 | 0.045 | 0.676 | 0.067 | 1.213 | 0.111 | 2.991 | 0.168
ResNet293 | 0.447 | 0.043 | 0.657 | 0.066 | 1.183 | 0.111 | 2.867 | 0.169

##  2 WeSpeaker: Speaker Embedding Toolkit for Research & Production

###  2.1 Open-source speech processing toolkits

In the field of speech processing, the research community has made significant
contributions to the open-source domain. Initially, toolkits such as HTK
(Hidden Markov Model Toolkit) [1] and Kaldi [2] played a pivotal role in
enabling researchers and industry applications. However, the emergence of deep
learning toolkits like PyTorch and TensorFlow has brought about a shift in the
landscape.

Recently, PyTorch-based toolkits such as SpeechBrain [3] and ESPnet [4] have
gained popularity due to their user-friendly interfaces and support for rapid
prototyping, making them accessible to new researchers. While these toolkits
serve a broad range of applications, Wenet stands out by focusing specifically
on end-to-end speech recognition. Its primary aim is to bridge the gap between
research advancements and practical deployment in real-world scenarios.

###  2.2 Wespeaker

In [5], we introduced Wespeaker, a speaker embedding learning toolkit designed
for research and production purposes. Wespeaker is characterized by its
lightweight code base and emphasis on high-quality speaker embedding learning,
demonstrating impressive performance on multiple datasets. While prioritizing
accessibility for researchers, Wespeaker also incorporates deployment codes
that are compatible with both CPUs and GPUs, thereby facilitating the
integration of research findings into practical production systems.

###  2.3 Design principles

As mentioned in previous section, there are different speech toolkits which
include speaker embedding learning functions, our proposed wespeaker stands
out for its simpliness, effectiveness and deployment friendliness. THe design
principles are as follows,

  * •

Light-weight: Wespeaker is designed specifically for deep speaker embedding
learning with clean and simple codes111If you are interested in other tasks
such as ASR, KWS, TTS, etc. We have different speficifcly designed toolkits
for different tasks, please visit https://github.com/wenet-e2e for more
details. It is purely built upon PyTorch and its ecosystem, and has no
dependencies on Kaldi [2].

  * •

Production oriented: All models in Wespeaker can be easily exported by torch
Just In Time (JIT) or as the ONNX format, which can be easily adopted in the
deployment environment. Sample deployment codes are also provided.

###  2.4 Supported functionalities

Wespeaker supports different popular speaker embedding learning models, margin
based softmax training objectives and several pooling functions.

Fig. 1: Supported functions in Wespeaker

Model Architectures

  * •

TDNN based x-vector [6], this is a milestone work that leads the following
deep speaker embedding era.

  * •

ResNet based r-vector and its deeper version, this is the best system of
VoxSRC 2019 [7] and CNSRC 2022 [8].

  * •

ECAPA-TDNN, a modified version of TDNN, this is the champion system of VoxSRC
2020 [9].

  * •

RepVGG decouples the training time and inference time architecture, resulting
in good performance and inference speed. This is the best system of VoxSRC
2021 [10].

  * •

CAM++, a modified a densely connected time delay neural network (D-TDNN) that
utilizes a context-aware masking mechanism. It also incorporates a novel
multi-granularity pooling technique to capture contextual information at
various levels.

Pooling functions

Pooling functions aggregate frame-level features into segment-level
representations, where Wespeaker supports the statistics-based and attention-
based ones.

Loss functions

Loss functions play a crucial role in deep speaker embedding learning. We
provide support for various types of loss functions, including the standard
softmax cross-entropy loss, as well as different margin-based variants [11,
12]. These variants include A-softmax [13, 14], AM-softmax [15], and AAM-
softmax [16].

In addition to supporting different loss functions, we also provide support
for commonly used techniques such as the inter-topk and sub-center algorithms.
These techniques aim to enhance the discriminative ability of the learned
embeddings by considering specific subsets of samples within a mini-batch or
using sub-centers to improve intra-class compactness.

Scoring back-ends

In the toolkit, we offer a basic implementation of the two-covariance
Probabilistic Linear Discriminant Analysis (PLDA). We encourage users to
explore different adaptation methods [2, 17, 18, 19] with PLDA for the Track 3
cross-domain evaluation condition.

###  2.5 Easy hands-on

We have included pretrained models in the toolkit to assist users in quickly
verifying results on relevant datasets. However, we would like to emphasize
that we DO NOT recommend users to solely submit results based on the provided
single systems. We encourage users to explore different methods of combining
systems, either among the models we provide or with ones trained by
themselves.

We provide the python binding for wespeaker for users to quickly try the
pretrained models, further details could be found on the project webpage
https://github.com/wenet-e2e/wespeaker/tree/master/runtime/binding/python

With the wespeakeruntime package installed, you can easily extract embeddings
from WAV files specified in the wav.scp file and save them into embed.ark
using the following code:

⬇

1import wespeakerruntime as wespeaker

2wav_scp_path = "path/to/wav.scp"

3embed_ark_path = "embed.ark"

4

5speaker = wespeaker.Speaker(lang=’chs’)

6speaker.extract_embedding_kaldiio(

7 wav_scp_path, embed_ark_path

8 )

Moreover, we released several pretrained models as dipicted in Table 2, both
in pytorch “.pt” format and runtime “.onnx” format, check
https://github.com/wenet-e2e/wespeaker/blob/master/docs/pretrained.md for
details on how to use it.

Table 2:  Pretrained models provided Datasets | Languages | Pretrained model
---|---|---
VoxCeleb | EN | CAM++ / CAM++_LM
VoxCeleb | EN | ResNet34 / ResNet34_LM
VoxCeleb | EN | ResNet152_LM
VoxCeleb | EN | ResNet221_LM
VoxCeleb | EN | ResNet293_LM

##  3 Results

###  3.1 Track 1 & 2

The results on the VoxCeleb1 evaluation dataset and the VoxSRC 2023
development set are presented in Table 1. The models employed for these
evaluations are specified in Table 2.

###  3.2 Track 3

There are various technology roadmap for unsupervised domain adaptation, and
we only provide the results of the voxceleb pre-trained model in Table 3.

Table 3:  Results on the validation set of Track3. The $p_{target}$ value is set to $0.01$. Architecture | Mean Normalization | EER(%) | minDCF
---|---|---|---
ResNet34 | N | 14.570 | 0.617
Y | 11.395 | 0.594

###  3.3 Track 4

We used the open-source pyannote [20] toolkit as our Voice Activity Detection
(VAD) system 222This is different from the silero VAD used in [5]. The
ResNet34_LM model was adopted as the speaker embedding extractor. For speaker
clustering, we implemented the spectral clustering algorithm and adapted it
specifically for the diarization task. The results on the VoxConverse dev and
test sets are shown in Table 4333Dev and test set of VoxConverse are used as
the validate set for the VoxSRC23 Track 4.

Table 4:  Results on the VoxConverse dev and test sets Test set | MISS(%) | FA(%) | SC(%) | DER(%)
---|---|---|---|---
VoxConverse dev | 2.7 | 0.2 | 1.8 | 4.8
VoxConverse test | 3.2 | 0.7 | 3.0 | 7.0

##  4 Suggestions for performance improvement

Our primary objective is to offer a robust initial model that serves as a
strong starting point for further improvement. We aim to provide researchers
with a solid foundation from which they can develop and enhance new
algorithms. By supplying a sufficiently good initial model, we aspire to
facilitate the development of novel methodologies within the research
community. We didn’t specifically do optimizations for Track 2, 3, and 4.
Instead, we would like to provide some suggestions to provide several
potential directions to work on:

###  4.1 Track 2

  * •

Increase Data Volume: Expand the training dataset by adding more data.

  * •

Explore Large Pretrained Models [21]: Consider utilizing large pretrained
models like WavLM [22] to leverage their extensive knowledge learned from vast
amounts of audio data.

  * •

Pretrained ASR Model Initialization: Phoneme information has been proven to be
beneficial for building speaker verification systems [23]. Consider
initializing your speaker embedding model with pretrained Automatic Speech
Recognition (ASR) models. Several papers presented during ICASSP 2023 verified
the effectiveness [24, 25].

  * •

Hard Mining Strategy: Find confused speakers and add an extra inter-topK
penalty on them is an effective way to improve performance in challenges [26,
27, 28]. Some of them have already been supported in
Wespeaker444https://github.com/wenet-e2e/wespeaker/pull/115.

###  4.2 Track 3

  * •

Distribution Alignment: Employ adversarial training or other strategies to
align the distributions of the source and target domains.

  * •

Pseudo Label Learning: Utilize clustering algorithms or other methods to
assign pseudo labels to unlabeled data from the target domain. It is important
to note that these pseudo labels may contain noise, and exploring techniques
[28, 29, 30] for training robust systems with noisy labels is a crucial topic.

  * •

Unsupervised PLDA Adaptation: Building upon the implemented PLDA codes, you
can incorporate PLDA adaptation mechanisms, such as the Kaldi version [2] and
the CORAL series [17, 18], to further enhance performance555A good reference
on PLDA adaptation can be found as [31].

###  4.3 Track 4

  * •

VAD Tuning. Currently, the errors caused by VAD is still quite high, improving
the VAD might be a good choice.

  * •

We only include the basic clustering algorithms here, you can try more
alogrithms and reclustering methods such as VBx [32, 33].

###  4.4 Results on the evaluation set

We submit the best single system (ResNet293) to the CodaLab evaluation server,
check the following links for the numbers and rankings.

  * •

Track 1 https://zeus.robots.ox.ac.uk/competitions/competitions/17#results

  * •

Track 2: https://zeus.robots.ox.ac.uk/competitions/competitions/16#results

  * •

Track 3: https://zeus.robots.ox.ac.uk/competitions/competitions/14#results

  * •

Track 4: https://zeus.robots.ox.ac.uk/competitions/competitions/18#results

##  5 The story

The voxceleb dataset is the largest opensource and high-quality dataset for
speaker recognition and the wespeaker team has a long history of supporting
the voxceleb dataset and voxsrc challenges, the core members have achieved top
rankings in previous VoxSRC competitions [7, 34, 35]666VoxSRC2019: 1st place,
VoxSRC2020: 2nd place, VoxSRC2022: 3rd place.

We have observed that there is often a disparity between the results reported
in current research papers and the performance achieved in system reports for
challenges, even when the training and evaluation data are the same. In order
to provide a reliable starting point for researchers, we initiated wespeaker,
aimed at delivering a reliable baseline system and a user-friendly toolkit.
Moreover, contributers from the Wenet opensource community helped with the
efficient data management techniques that enable scaling to industrial-sized
datasets, as well as deployment codes for rapid prototyping in production
environments.

Knowing that this would be the final VoxSRC challenge, the Wespeaker team is
eager to contribute and support the event by providing an easy-to-use toolkit
and baseline systems. We hope more participants can enjoy the challenge and
focus on the algorithm improvement, without struggling with basic experimental
setups.

##  6 Acknowledgement

We would like to extend our sincere appreciation to the VoxSRC challenge
organizers for their invaluable contribution in open-sourcing this remarkable
dataset and organizing such meaningful challenges. We would also like to
express our gratitude to the wenet open-source community, whose dedication and
collective efforts have played a pivotal role in the success and growth of
wespeaker. Enjoy the Challenge and Welcome to contribute.

## References

  * [1] S. Young, G. Evermann, M. Gales, T. Hain, D. Kershaw, X. Liu, G. Moore, J. Odell, D. Ollason, D. Povey _et al._ , “The htk book,” _Cambridge university engineering department_ , vol. 3, no. 175, p. 12, 2002\.
  * [2] D. Povey, A. Ghoshal, G. Boulianne, L. Burget, O. Glembek, N. Goel, M. Hannemann, P. Motlicek, Y. Qian, P. Schwarz _et al._ , “The kaldi speech recognition toolkit,” in _IEEE 2011 workshop on automatic speech recognition and understanding_. IEEE Signal Processing Society, 2011.
  * [3] M. Ravanelli, T. Parcollet, P. Plantinga, A. Rouhe, S. Cornell, L. Lugosch, C. Subakan, N. Dawalatabad, A. Heba, J. Zhong _et al._ , “Speechbrain: A general-purpose speech toolkit,” _arXiv preprint arXiv:2106.04624_ , 2021\.
  * [4] S. Watanabe, T. Hori, S. Karita, T. Hayashi, J. Nishitoba, Y. Unno, N.-E. Y. Soplin, J. Heymann, M. Wiesner, N. Chen _et al._ , “Espnet: End-to-end speech processing toolkit,” _Proc. Interspeech_ , pp. 2207–2211, 2018.
  * [5] H. Wang, C. Liang, S. Wang, Z. Chen, B. Zhang, X. Xiang, Y. Deng, and Y. Qian, “Wespeaker: A research and production oriented speaker embedding learning toolkit,” in _ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)_. IEEE, 2023, pp. 1–5.
  * [6] D. Snyder, D. Garcia-Romero, G. Sell, D. Povey, and S. Khudanpur, “X-vectors: Robust dnn embeddings for speaker recognition,” in _2018 IEEE international conference on acoustics, speech and signal processing (ICASSP)_. IEEE, 2018, pp. 5329–5333.
  * [7] H. Zeinali, S. Wang, A. Silnova, P. Matějka, and O. Plchot, “But system description to voxceleb speaker recognition challenge 2019,” _arXiv preprint arXiv:1910.12592_ , 2019.
  * [8] Z. Chen, B. Liu, B. Han, L. Zhang, and Y. Qian, “The sjtu x-lance lab system for cnsrc 2022,” _arXiv preprint arXiv:2206.11699_ , 2022.
  * [9] B. Desplanques, J. Thienpondt, and K. Demuynck, “Ecapa-tdnn: Emphasized channel attention, propagation and aggregation in tdnn based speaker verification,” _Proc. Interspeech_ , pp. 3830–3834, 2020.
  * [10] M. Zhao, Y. Ma, M. Liu, and M. Xu, “The speakin system for voxceleb speaker recognition challange 2021,” _arXiv preprint arXiv:2109.01989_ , 2021.
  * [11] M. Hajibabaei and D. Dai, “Unified hypersphere embedding for speaker recognition,” _arXiv preprint arXiv:1807.08312_ , 2018.
  * [12] X. Xiang, S. Wang, H. Huang, Y. Qian, and K. Yu, “Margin matters: Towards more discriminative deep neural network embeddings for speaker recognition,” in _2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)_. IEEE, 2019, pp. 1652–1656.
  * [13] W. Liu, Y. Wen, Z. Yu, M. Li, B. Raj, and L. Song, “Sphereface: Deep hypersphere embedding for face recognition,” in _Proceedings of the IEEE conference on computer vision and pattern recognition_ , 2017, pp. 212–220.
  * [14] Z. Huang, S. Wang, and K. Yu, “Angular softmax for short-duration text-independent speaker verification,” _Proc. Interspeech_ , pp. 3623–3627, 2018.
  * [15] F. Wang, J. Cheng, W. Liu, and H. Liu, “Additive margin softmax for face verification,” _IEEE Signal Processing Letters_ , vol. 25, no. 7, pp. 926–930, 2018.
  * [16] J. Deng, J. Guo, X. Niannan, and S. Zafeiriou, “Arcface: Additive angular margin loss for deep face recognition,” in _The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)_ , 2019.
  * [17] M. J. Alam, G. Bhattacharya, and P. Kenny, “Speaker verification in mismatched conditions with frustratingly easy domain adaptation.” in _Odyssey_ , vol. 2018, 2018, pp. 176–180.
  * [18] K. A. Lee, Q. Wang, and T. Koshinaka, “The coral+ algorithm for unsupervised domain adaptation of plda,” in _ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)_. IEEE, 2019, pp. 5821–5825.
  * [19] P.-M. Bousquet and M. Rouvier, “On robustness of unsupervised domain adaptation for speaker recognition,” in _Interspeech_ , 2019.
  * [20] H. Bredin, R. Yin, J. M. Coria, G. Gelly, P. Korshunov, M. Lavechin, D. Fustes, H. Titeux, W. Bouaziz, and M.-P. Gill, “pyannote.audio: neural building blocks for speaker diarization,” in _ICASSP 2020, IEEE International Conference on Acoustics, Speech, and Signal Processing_ , 2020.
  * [21] Z. Chen, S. Chen, Y. Wu, Y. Qian, C. Wang, S. Liu, Y. Qian, and M. Zeng, “Large-scale self-supervised speech representation learning for automatic speaker verification,” in _ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)_. IEEE, 2022, pp. 6147–6151.
  * [22] S. Chen, C. Wang, Z. Chen, Y. Wu, S. Liu, Z. Chen, J. Li, N. Kanda, T. Yoshioka, X. Xiao _et al._ , “Wavlm: Large-scale self-supervised pre-training for full stack speech processing,” _IEEE Journal of Selected Topics in Signal Processing_ , vol. 16, no. 6, pp. 1505–1518, 2022.
  * [23] S. Wang, J. Rohdin, L. Burget, O. Plchot, Y. Qian, K. Yu, and J. Cernockỳ, “On the usage of phonetic information for text-independent speaker embedding extraction.” in _Interspeech_ , 2019, pp. 1148–1152.
  * [24] D. Liao, T. Jiang, F. Wang, L. Li, and Q. Hong, “Towards a unified conformer structure: from asr to asv task,” in _ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)_. IEEE, 2023, pp. 1–5.
  * [25] D. Cai, W. Wang, M. Li, R. Xia, and C. Huang, “Pretraining conformer with asr for speaker verification,” in _ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)_. IEEE, 2023, pp. 1–5.
  * [26] Z. Chen, B. Han, X. Xiang, H. Huang, B. Liu, and Y. Qian, “Build a sre challenge system: Lessons from voxsrc 2022 and cnsrc 2022,” _arXiv preprint arXiv:2211.00815_ , 2022.
  * [27] M. Zhao, Y. Ma, Y. Ding, Y. Zheng, M. Liu, and M. Xu, “Multi-query multi-head attention pooling and inter-topk penalty for speaker verification,” in _ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)_. IEEE, 2022, pp. 6737–6741.
  * [28] B. Han, Z. Chen, and Y. Qian, “Exploring binary classification loss for speaker verification,” in _ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)_. IEEE, 2023, pp. 1–5.
  * [29] R. Tao, K. A. Lee, R. K. Das, V. Hautamäki, and H. Li, “Self-supervised speaker recognition with loss-gated learning,” in _ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)_. IEEE, 2022, pp. 6142–6146.
  * [30] B. Han, Z. Chen, and Y. Qian, “Self-supervised speaker verification using dynamic loss-gate and label correction,” in _Interspeech_ , 2022.
  * [31] Q. Wang, K. Okabe, K. A. Lee, and T. Koshinaka, “Generalized domain adaptation framework for parametric back-end in speaker recognition,” _arXiv preprint arXiv:2305.15567_ , 2023.
  * [32] M. Diez, S. Wang, and J. Rohdin, “Bayesian hmm based x-vector clustering for speaker diarization.” in _Interspeech_ , 2019, pp. 346–350.
  * [33] F. Landini, J. Profant, M. Diez, and L. Burget, “Bayesian hmm clustering of x-vector sequences (vbx) in speaker diarization: theory, implementation and analysis on standard tasks,” _Computer Speech & Language_, vol. 71, p. 101254, 2022.
  * [34] X. Xiang, “The xx205 system for the voxceleb speaker recognition challenge 2020,” _arXiv preprint arXiv:2011.00200_ , 2020.
  * [35] Z. Chen, B. Han, X. Xiang, H. Huang, B. Liu, and Y. Qian, “Sjtu-aispeech system for voxceleb speaker recognition challenge 2022,” _arXiv preprint arXiv:2209.09076_ , 2022.


# An Alternative Design for Large Scale Liquid Scintillator Detectors

Iwan Morton-Blake  Steven D. Biller  Department of physics, University of
Oxford, Oxford OX1 3RH, United Kingdom

###### Abstract

The construction of large-scale liquid scintillator detectors is complicated
by the need to separate the scintillation region from photomultiplier tubes
(PMTs) due to their intrinsic radioactivity. This is generally done using
acrylic or nylon barriers, whose own activity can also lead to substantial
reduction of the fiducial detection volume for a number of low energy
($\sim$MeV) studies, and whose construction becomes increasingly difficult and
expensive for larger detector volumes. Here we present an initial simulation
study of an alternative geometric construction known as SLiPS (Stratified
Liquid Plane Scintillator), which aims to avoid such physical barriers
entirely by instead using layers of lipophobic liquids to separate PMTs from
the scintillation region.

Liquid Scintillator, Neutrino Detectors

###### pacs:

95.55.Vj, 29.40.Mc

††preprint: APS/123-QED

##  I Introduction

Large scale liquid scintillation (LS) detectors have played a central role in
non-accelerator particle physics, including experiments such as KamLAND [1],
Borexino [2], Daya Bay [3], RENO [4], Double Chooz [5], and with several more
in early operation, construction or planning, such as SNO+ [6], JUNO [7] and
THEIA [8]. Common to all large-scale liquid scintillator detectors is the
requirement for a boundary separating the scintillation region from the
photomultiplier tubes (PMTs), due to their intrinsic radioactivity. This is
generally accomplished using acrylic or nylon barriers, whose own
radioactivity can also lead to substantial cuts to the fiducial detection
volume for a number of low energy ($\sim$MeV) studies. Such barrier
constructions become increasingly difficult and expensive with increasing
detector volumes. This paper uses simulation to explore an alternative
geometric design for larger LS detectors aimed at simplifying such
constructions while also making more economic use of PMTs to maintain high
photocathode coverage. The approach opts for a planar design using lipophobic
liquids to separate detection and scintillation regions without additional
physical barriers, and reflective surfaces to increase light collection and
reconstruct event using the time-separated wave fronts. The result is a
potentially more scalable approach that may permit clean construction of
larger scale instruments in the future.

##  II SLiPS Detector Design

(a)

(b)

Figure 1: Rough schematic cross sectional views of (a) single-sided and (b)
double-sided SLiPS configurations. Scintillation light paths from a
hypothetical interaction are represented by dashed lines. Figure 2: Simplified
analytical model of the fraction of produced light detected vs the ratio of
absorption length to scintillator depth. Thus, a typical $\sim$40m absorption
length and the 10m scintillator depth considered here would correspond to a
ratio of 4 on this plot.

Figure 1 shows a sketch of the Stratified Liquid Plane Scintillator (SLiPS)
detector for two configurations. The surrounding rock cavity depicted is only
notional, as it would more realistically be engineered with a curved roof etc.
to provide structural integrity, but this is not relevant for the current
study. PMTs are mounted on the bottom of a wide cavity and submerged in a
distillable, lipophobic liquid, above which a less dense scintillator is
layered [9]. In many ways, this layering is similar to the “partial fill”
configuration of the SNO+ detector, which ran very successfully for nearly a
year with scintillator floating above a water interface [9]. During this time,
there was no evidence of instabilities or mixing near the interface, even
following re-circulation or filling activities, no evidence of fluor (PPO)
transfer from scintillator to water, and no evidence of U/Th transfer from
water to scintillator. Event reconstruction was accomplished using fitting
algorithms specially adapted to account for the two media, and the calibration
and analysis of data in both scintillator and water regions was successfully
performed. Small scale tests with LAB and ethylene glycol also confirm no
significant transfer of fluors between these liquids, even when agitated and
allowed to settle.

Ideally, the refractive indices of the two liquids should be matched as
closely as possible in the wavelength range of emission, which is typically
$\sim$400-450nm. For this reason, buffer liquids such as ethylene glycol are
good candidates. For the single-sided configuration, reflective surfaces near
the top and sides of the detector are used to direct scintillation light down
to the bottom PMT array. These reflective sheets might also be used to provide
optical shielding from light emitted by radiation outside the fiducial volume.
PMTs may also be placed in the region between the reflective surfaces and the
container to serve as a muon veto. In principle, it is also possible to
construct a detector with PMT planes on both top and bottom, as in figure 1b,
by using a solvent with a high enough density to allow for a secondary buffer
such that $\rho$(buffer 1)$>\rho$(solvent)$>\rho$(buffer 2). However, the
studies here will largely concentrate on exploring the use of reflections in
the two-liquid geometry, schematically depicted in figure 1a.

Due to the long path lengths of scintillation light created by reflections,
the detected light levels can become limited by the extinction length of
photons in the scintillator. Figure 2 shows results of a simple analytical
calculation for the relative light levels observed in an idealised geometry
involving either one or two infinite detection planes as a function of the
ratio of the scintillator absorption length to scintillator plane thickness.
It is therefore advantageous for the detector height to be much less (by a
factor of $\sim$10) than the extinction length, generally leading to a short
and wide detector configuration. This also tends to simplify the nature of the
reflections for the single plane case. While alternative detector shapes are
explored later, the baseline configuration for this study will be an
azimuthally symmetric circular ‘Pancake’ detector with reflective top and side
surfaces shown in figure 3.

Figure 3: SLiPS scintillator and buffer dimensions used in simulations tests,
and the defined z-axis direction.

##  III Detector Simulation

The propagation of radiation in the detector was carried out using GEANT4 [10]
based simulation. The GLG4Sim package was used to generate energy deposition
in the scintillator and the subsequent light emission [11]. PMT and data
acquisition response was also simulated assuming that the full wave-train was
recorded to take advantage of information from multiple hits on individual
PMTs. In practice, this only has modest advantage over using the earliest hit
times for the energy range explored here, as PMT occupancies tend to be below
10%, but may be more useful for higher energy events.

###  III.1 PMTs

Two PMT types were simulated in the performance testing presented in this
work: the 20” R12860 PMT from Hamamatsu Photonics and the 8” R5912-100
Hamamatsu model. The 20” PMTs were modeled in simulation based on the
specifications according to [12], and the 8” PMTs based on [13] and [14],
where the timing measurements of an un-modified R5912-100 were taken from the
latter (which yielded a faster TTS than the standard data sheet). GEANT4
geometry models of the PMT’s dimensions were created and the measured quantum
efficiency spectra was attributed to the photocathode surface. A collection
efficiency of 90% was assumed. The measured single photoelecton (SPE) charge
spectrum and the transit time spread (TTS) were also included in the
simulation. The charge spectrum was modeled as a Gaussian distribution, from
which PMT hit charges were sampled for the triggering of PMTs and the global
trigger. Table 1 summarises the key parameters used to characterise each PMT.

Table 1: Key parameters assumed in the simulation of the two types of PMTs presented in this work. Parameter | r12860 (20”) | r5912-100 (8”)
---|---|---
Peak to Valley Ratio | 4.75 | 4.2
TTS ($\upsigma$) | 1.3ns | 0.87ns
QE (400nm) | 31$\%$ | 32$\%$

Channel discriminator thresholds were set such that, for the r12860 PMT, a
photon that produced a single p.e. on the photocathode would have a trigger
inefficiency of 3$\%$ (4$\%$ for the r5912-100 PMT). A trigger gate event
width of 400ns was assumed. The PMTs were distributed in a hexagonal packing
planar arrangement, with 54cm or 20.8cm center-to-center between adjacent
tubes for 20” and 8” tubes, respectively. In all simulations, the PMTs were
positioned such that the top of the photocathode cap was 2m below the
scintillator-buffer interface so as to shield the scintillator from the PMT’s
intrinsic radioactivity.

###  III.2 Simulated Liquid Scintillator and Buffer Liquid

The detector’s design principle requires the use of immiscible liquids with
similar refractive indices. Since most organic scintillator solvents tend to
be hydrophobic, these can therefore be paired with hydrophilic liquids, such
as glycols, which tend to be more dense. This then more naturally leads to a
design with scintillator on top and PMTs in a bottom buffer region. The
scintillator mixture adopted for simulation was composed of linear
alkylbenzene (LAB) as the solvent plus 2g/L of 2,5-diphenyloxazole (PPO) as
the primary fluor and 15mg/L of 1,4-bis-methylstyryl-benzene (bis-MSB) as the
secondary fluor [15]. Figure 4 shows the assumed absorption and emission
spectra of the contributing components present in the scintillator.

Ethylene glycol was chosen as the buffer liquid to pair with LAB for this
study. Ethylene glycol can be purified through distillation as well as
filtration and has a similar refractive index to LAB in the wavelength region
of interest, as shown in figure 4(b). Matching refractive indices in the
buffer and scintillator liquids minimises the refraction effects for light
crossing their interface, improving light detection efficiency and simplifying
event reconstruction. The scintillator solution and ethylene glycol were
assumed to be 20∘C, with densities 0.858 and 1.114g/cm3, respectively.

(a)

(b)

Figure 4: (a) Absorption and Emission spectra for the LAB, PPO and bisMSB
components in the scintillator cocktail. The left y-axis shows probability of
emission. The black dashed line shows the r12860 Hamamatsu 20” PMT measured
total detection efficiency divided by 10. The right y-axis corresponds to the
sparse dotted graphs showing absorption in units of mm-1 (b) Refractive index
as a function of wavelength for LAB and ethylene glycol [15][16].

##  IV Performance Tests

The ’Pancake’ design explored here consists of a cylindrical scintillator
volume of thickness 10m and 25m in radius. The glycol buffer region extends 3m
below the scintillation layer, as shown in figure 3. The walls and ceiling
surrounding the scintillator region are assumed to have a specular reflection
efficiency of 90$\%$. The center of the cylindrical scintillator volume is
defined as the origin and the x-y plane was aligned with the plane of the
PMTs, which resides at the bottom of the glycol buffer with the photocathode
surfaces facing upwards, 2m below the scintillator interface.

###  IV.1 Light Collection

Energy resolution is an important component in many relevant physics analyses
and is ultimately limited by Poisson fluctuations on the total number of
photoelectrons recorded by PMTs in a physics event. Efficient light collection
is therefore crucial. Figure 5 shows the average detected number of
photoelectrons observed in the simulation for 1MeV electrons generated at
different z and radial positions in the detector. It can be seen that a high
level of light detection are possible with in a SLiPS configuration with a
relatively homogeneous response: the variation (rms) in the bulk of the volume
is 4$\%$ of the mean and the most extreme light yields lie within 15$\%$.
Table 2 shows comparisons of the typical projected light collection for the
SLiPS design with the Borexino, KamLAND, SNO+ and JUNO detectors. The JUNO
experiment, also 20kt in mass, will be equipped with $\sim$20,000 large PMTs
to achieve their energy resolution goal of 3$\%$ per MeV. This would be a
world-leading accomplishment for a kilotonne-scale optical detector. It can be
seen that the SLiPS detector could potentially achieve a comparable level of
resolution using less than half the number of PMTs and a much simpler
construction.

Figure 5: Light yield (total p.e.s per MeV) as a function of true $\upbeta$-particle position in the SLiPS detector - of dimensions shown in figure 3. The volume-weighted mean and rms were calculated as 1081 and 43 p.e.s per MeV, respectively. Table 2: Table summarising detector size, PMT coverage and the light collection for the Borexino, KamLAND, SNO+ and JUNO detectors compared to SLiPS. | Target | PMT | Light coll. | LC/Cov.
---|---|---|---|---
| Mass | Coverage | (pe/MeV) | (pe/MeV/%)
Borexino [2][17] | 300t |  $\sim$30% | 450 | 15
KamLAND [1][18] | 1kT |  $\sim$34% | 200 | 6
SNO+ [19] | 780 |  $\sim$50% |  $\sim$520 | 10
JUNO [20][21] | 20kT |  $\sim$80% |  $\sim$1200 | 15
SLiPS | 20kT |  $\sim$30% |  $\sim$1100 | 37

###  IV.2 Position Reconstruction

High quality position reconstruction is an important part of many physics
analyses in order to achieve good signal-background separation. Event
positions can be reconstructed using PMT hit times and hit patterns. In the
SLiPS detector, the methods of reconstructing vertical and horizontal event
positions effectively separate: the compressed and flattened geometry is
particularly well suited to reconstruct x and y positions from the PMT hit
density, while z-position is better determined from the time separation
between direct and reflected wave fronts.

####  IV.2.1 Reconstruction Using PMT Hit Densities

Figure 6 shows PMT hit density plots for multiple 3MeV electron events
generated at different y-positions in the ’Pancake’ SLiPS detector for x=z=0.
These plots serve to illustrate that consideration of the PMT hit
distributions alone should allow x and y event positions to be well
determined. This capability is also of potential benefit for the use of slow-
fluor scintillators used for Cherenkov separation [22], where position
reconstruction via timing may be compromised to some extent.

(a)

(b)

(c)

Figure 6: PMT hit density vs x,y position made with many 3MeV electron events
in the Pancake SLiPS detector. Electron events were generated at (a) (0,0,0)
(b) (0,12,0)m (c) (0,24.5,0)m. The z-axis reflects arbitrary units.

To assess this more quantitatively, a likelihood fit was performed to
determine event-by-event positions in the x-y plane based on PMT hit
densities, using triggered PMT hits occurring within a 400ns time window.
Events with z-positions closer to the PMT plane are expected to yield a better
x-y resolution owing to a more peaked density profile compared to events
closer to the top of the detector. For simplicity, the ’typical’ resolution
was explored using probability density functions (PDFs) that were generated at
various radial displacements for z-positions in the middle of the
scintillator. Fits were then performed for 500 3MeV electron events generated
at the center of the detector. Results indicate a rms x and y position
resolution of between 7cm and 22cm for the 8” PMT configuration, depending on
the z-position (with z positions closer to the PMTs having better resolution).
This is close to intrinsic expectations based on simple geometric arguments.
While information from PMT hit times could also be included to improve the
estimation of x-y event positions further, these density-based reconstructions
are already approaching a comparable precision to what is typically achieved
based on timing in conventional kilotonne-scale LS detectors.

Figure 7: Pdf of PMT hit density vs PMT y-position for 3MeV $\upbeta$ events,
for $\upbeta$s generated at z-positions separated by 40cm (x = y = 0). The
slight asymmetry in the center of the distribution is due to the binning of
hexagonally packed PMT positions in one dimension.

As previously indicated, hit densities can also be used to estimate the
z-position based on the extent to which the density profile is peaked. While
less accurate than x-y localisation, useful constraints can still be obtained.
To explore this aspect, a similar procedure as before was followed, but with
PDFs of the expected PMT hit density distributions now generated for different
z-positions for x=y=0. Figure 7 shows representative expected hit density
distributions versus PMT y-position (shown in one dimension for clarity). The
three PDFs show the expected density distributions for 3MeV electrons at three
different z positions separated by 40cm. Differences in the PDF peaks appear
subtle, but can be distinguished to a reasonable extent owing to the large
photon statistics of each event. The resulting likelihood fits of 500 3MeV
events generated at the center of the scintillator yielded a typical standard
deviation of 37cm in the z location for 20” PMTs and 30cm for the 8” PMT
array. This is more comparable to the vertex resolution of water Cherenkov
detectors, but the incorporation of timing information for this axis is
expected to lead to a notable improvement in resolution.

####  IV.2.2 Vertical Position Determination with PMT Hit Times

A strong constraint on the z-position arises from the time differences between
direct and reflected scintillation light. Figure 8 shows triggered PMT hit
times vs PMT $\uprho$ (radius in x-y plane) for many electron events generated
at an assortment z-positions.

(a)

(b)

(c)

(d)

(e)

(f)

Figure 8: PMT hit times vs PMT radial position $\uprho$. Top row: zev = +4.5m,
middle row: zev = 0m, bottom row: zev = -4.5m. Left column: Many electron
events with analytical calculations of the direct and reflected light
wavefronts, right column: an example, single 3MeV electron event.

The red dashed lines overlaying the hit times in the first column indicate
analytic time-of-flight calculations for direct and reflected light paths of
photons traveling in the SLiPS detector. Calculations assumed straight line
paths and a single average effective refractive index between that of LAB and
ethylene glycol. The calculated light paths include single and double
reflections from the ceiling and side walls. The right column of plots
demonstrates that the direct and reflected wavefronts are clearly visible on
an event-by-event basis. As expected, wavefront separation becomes more
difficult for events approaching the reflective sheets.

Late scintillation emission times produce the hit times seen to lag behind the
wavefront regions, where the SNO+ scintillator cocktail of LAB + 2g/L PPO
scintillator was assumed to have a leading time decay constant of 4.8ns [15].
It will be shown in section IV.3 how the use of a faster scintillator mixture
and/or faster PMTs aids in the reconstruction of event positions.

Figure 9: Likelihood fit results of the calculated z-position for 500 single
3MeV electron events. The standard deviation of the histogram is 8cm.

In order to study time-based z-position resolution, a likelihood fit was
performed using PDFs based on triggered PMT hit times and positions. In order
to compare PMT hit times, a global event trigger time was first estimated.
This was approximated by the mean of the 50 earliest PMT hit times, which
yielded a standard deviation of $\sim$0.3ns in the trigger times calculated
for many events. This global event trigger time was subtracted from all
individual PMT trigger times in the following plots. PDFs were generated by
simulating many 3MeV electron events and binning at z-position intervals
separated by 2.5cm. The reconstructed z-position for an event was then taken
as that which maximised the likelihood for that event’s PMT hit time
distribution. In order to account for uncertainties in the previously
described event trigger time estimation, the trigger time in each fit was
allowed to freely float up to $\pm$0.3ns around the estimated event time.

Figure 9 shows the fit results for 500 3MeV electron events, all generated at
the origin. The standard deviation of the best fit z-positions is found to be
8cm. This is similar to the best position resolution of current LS
experiments, despite using fewer PMTs placed in single plane arrangement.

(a)

(b)

Figure 10: Binned PDFs of PMT hit times vs PMT radial position $\uprho$, for a
3MeV electron event in the center of SLiPS. The plots demonstrate the
increased resolution of the direct and reflected wavefronts when both faster
PMTs and scintillators are used. (a) 20” r12860 Hamamatsu PMTs (1.3ns TTS) and
a 4.8ns leading scintillator emission time constant (b) 8” r5912-100 Hamamatsu
PMTs (0.87ns TTS) and a 3ns leading scintillator emission time constant.

###  IV.3 Faster Scintillators & PMTs

Figure 8 shows the wavefront structure that allow for the reconstruction of
event position. The resolution of the these wavefront characteristics can be
improved by decreasing the spread in PMT hit times. This can be achieved by
using faster PMTs and/or scintillator mixtures with faster emission times.

Figure 10 illustrates the combined impact that PMT TTS and scintillator
emission times have on the wavefront resolutions. The pair of plots show
wavefront hit time distributions using both faster PMTs as well as faster
scintillator emission times, compared to the original SLiPS setup outlined in
the introduction. Hamamatsu 8” r5912-100 PMTs were modeled, with the PMT TTS
specified in table 1. These PMTs were placed in a hexagonal close packing
configuration as before, with adjacent PMTs spaced by 20.82cm center-to-center
(0.5cm glass-to-glass). A total of $\sim$50,000 PMTs were used in the 25m
radius Pancake configuration SLiPS detector. The LAB+PPO+bisMSB scintillator
cocktail was also altered in simulation such that the leading exponential
decay constant for scintillation emission times was changed from 4.8ns to 3ns
(achievable through PPO addition [23]).

The side by side comparison shows a noticeable improvement in the wavefront
separation. Repeating the time-based likelihood fit on this faster
configuration, it was found that z-position resolution was improved from 8cm
to 5cm for 3MeV electron events.

(a)

(b)

Figure 11: HepRep renderings [24] of the concentrators tested in simulations
of the SLiPS detector. (a) Side-on view of a single 8” r5912-100 PMT [14],
showing the photocathode (green) and truncated light concentrators (red). (b)
Top-down view of a few hexagonally packed PMTs with concentrators masking the
regions of the PMT which do not contain a photocathode surface.

(a)

(b)

Figure 12: Binned PDFs of PMT hit times vs PMT position for 3MeV electrons at
the center of the Pancake SLiPS detector, equipped with fast 8” PMTs and fast
3ns scintillator (a) No light concentrators (b) with light concentrators.

###  IV.4 Light Concentrators

In order to increase the light collection and avoid the cost of installing
additional PMTs, some scintillator detectors employ light concentrators [25].
Generally, the light-detecting photocathode surface does not cover the full
width of the PMT itself. Even for very densely packed spherical PMTs, this
leaves insensitive regions between PMTs, which reduces the effective
photocathode coverage of the detector.

A significant difference between the planar SLiPS detector compared to the
common spherical design is that the PMT angular acceptance needs to be as
large as possible, since a much higher proportion of the scintillation light
in SLiPS arrives at steep angles of incidence. This limits the application of
concentrators. However, modest scale concentrators can still achieve some
useful improvement to the photocathode coverage for close-packed hemispherical
PMTs by filling in the gaps between active photocathode surfaces.

To investigate this more quantitatively in SLiPS, short light concentrators
were modeled in simulation. Figure 11 shows the shape of the reflectors used
to surround each PMT (assuming the 8” r5912-100 PMT). The PMTs were arranged,
as before, in a hexagonal close packing arrangement, with a nearest glass-to-
glass distance of 0.5cm. To allow for the packed arrangement of PMTs, the
concentrators were appropriately truncated. The concentrator base width was
made equal to the 9.51cm photocathode radius of the r5912-100 model.
Simulations assumed a 90$\%$ specular reflection efficiency for the
concentrator surface.

Figure 12 shows PDFs of PMT hit times vs PMT position for 8” PMTs both with
and without concentrators. It can be seen that the concentrators increase the
light yield for small $\rho$ (PMTs directly below the event), while the
wavefronts become less intense at larger $\rho$. This is predominantly due the
steep angles of incidence light arriving at distant PMTs, where there is an
increased probability of reflecting back up into the scintillator. For the
Pancake SLiPS detector equipped with 8” PMTs, a $\sim$9% light yield increase
from 1180 to 1290 p.e./MeV is observed when concentrators were added.

Figure 13: Generic box configuration of the SLiPS Detector.

##  V Alternative SLiPS Detector Shape

The aim of the SLiPS design is to provide a relatively low-cost, large-scale
detector of simple construction. It is common for low energy neutrino
experiments to be placed in deep underground mines where tunnels are typically
excavated for the extraction of geological materials. Therefore, an
alternative SLiPS detector shape to consider is a narrow and long box shape.
Figure 13 shows such a generic SLiPS box configuration.

While the box configuration could potentially allow for a simpler and cheaper
construction, the narrowness of the detector can complicate position
reconstruction due to the increased number of reflections, as well as
increasing the radioactive backgrounds from the cavity rock due to the reduced
self-shielding. This paper only briefly explores the optical properties of
such configurations by looking at the characteristics wavefronts for 3 MeV
electrons generated in the middle of such detectors.

(a)

(b)

(c)

Figure 14: Binned PDFs of PMT hit times vs position, generated from many 3MeV
electron events at the center of the detector. Plots are for the box
configuration of length 100m and width (a) 40m (b) 20m (c) 10m.

Figure 14 displays direct and reflected wavefronts for several box
configurations. In each case, the length $L$ was chosen to be 100m, the
scintillation region height, $S$, was fixed at 10m and the buffer region
height, $B$, was fixed at 2m. The width of the box detector was then varied
from 10m to 40m for different test cases. For the purposes of demonstration,
the wavefronts have again been plotted as PMT hit times vs radial position
$\rho$. While more detailed quantitative studies would need to be done for a
given configuration, the plots serve to illustrate the potential complications
that reflections may have on position reconstruction. As expected, the
wavefronts become more spread out and overlap one another as the width of the
detector decreases. However, reasonable wavefront separation can still be seen
for box widths greater than $\sim$20m (or $W/S>2$ and $L\gg W$).

##  VI Conclusion

The SLiPS design offers a highly scalable and simple construction for large
scale liquid scintillator detectors, with many potential applications. The use
of layered liquids avoids the difficult construction of large transparent
barriers to separate scintillation and light detection regions and potentially
reduces background contamination from such boundary layers. The studies here
also suggest that the use of reflective sheets may yield highly efficient
light collection with good position resolution while using far fewer PMTs that
those employed in traditional spherical detectors.

This work has been supported by the Science and Technology Facilities Council
of the United Kingdom.

## References

  * [1] A. Gando and others, “Measurement of the double-$\beta$ decay half-life of 136Xe with the KamLAND-Zen experiment,” Phys. Rev. C, vol. 85, p. 045504, Apr 2012\.
  * [2] G. Alimonti et al., “The Borexino detector at the Laboratori Nazionali del Gran Sasso,” Nucl. Instrum. Meth. A, vol. 600, pp. 568–593, 2009\.
  * [3] F. P. An et al., “A side-by-side comparison of Daya Bay antineutrino detectors,” Nucl. Instrum. Meth. A, vol. 685, pp. 78–97, 2012.
  * [4] J. K. Ahn et al., “RENO: An Experiment for Neutrino Oscillation Parameter $\theta_{13}$ Using Reactor Neutrinos at Yonggwang,” arXiv preprint arXiv:1003.1391, 2010.
  * [5] H. de Kerret et al., “Double Chooz $\theta_{13}$ measurement via total neutron capture detection,” Nature Phys., vol. 16, no. 5, pp. 558–564, 2020.
  * [6] V. Albanese et al., “The SNO+ experiment,” Journal of Instrumentation, vol. 16, no. 08, p. P08059, 2021.
  * [7] A. Abusleme et al., “JUNO Physics and Detector,” arXiv preprint arXiv:2104.02565, 2021.
  * [8] M. Askins et al., “THEIA: An advanced optical neutrino detector,” Eur. Phys. J. C, vol. 80, no. 5, pp. 1–31, 2020.
  * [9] I. Morton-Blake, “First measurement of reactor antineutrinos in scintillator at SNO+ and study of alternative designs for large-scale liquid scintillator detectors,” vol. University of Oxford, 2021.
  * [10] S. A. et al., “Geant4 - a simulation toolkit,” Nucl. Instrum. Meth. A, vol. 506, no. 3, pp. 250–303, 2003.
  * [11] G. Horton-Smith, “GLG4sim page.” https://www.phys.ksu.edu/personal/gahs/GLG4sim/.
  * [12] X. C. Lei et al., “Evaluation of new large area pmt with high quantum efficiency,” Chinese Physics C, vol. 40, no. 2, p. 026002, 2016.
  * [13] “Photomultiplier Tubes and Assemblies.” https://www.hamamatsu.com/resources/pdf/etd/High_energy_PMT_TPMZ0003E.pdf.
  * [14] T. Kaptanoglu, “Characterization of the hamamatsu 8” r5912-mod photomultiplier tube,” Nucl. Instrum. Meth. A, vol. 889, p. 69–77, 2018\.
  * [15] M. Anderson et al., “Development, characterisation, and deployment of the SNO+ liquid scintillator,” Journal of Instrumentation, vol. 16, no. 05, p. P05009, 2021.
  * [16] H. El-Kashef, “Necessary requirements imposed on polar dielectric laser dye solvents,” Physica B: Condensed Matter, vol. 279, pp. 295–301, 05 2000\.
  * [17] H. Back et al., “Current status of the BOREXINO experiment,” Nuclear Physics B-Proceedings Supplements, vol. 143, no. 1-3 SPEC. ISS., pp. 21–24, 2005.
  * [18] F. Suekane, T. Iwamoto, H. Ogawa, O. Tajima, and H. Watanabe, “An overview of the KamLAND 1-kiloton liquid scintillator,” arXiv preprint physics/0404071, 2004.
  * [19] V. Lozza, “The SNO+ experiment: current status and future prospects,” in Magellan Workshop: Connecting Neutrino Physics and Astronomy, pp. 87–94, 2016.
  * [20] H. Miao, “Double Calorimetry System in JUNO,” Radiation Detection Technology and Methods, vol. 1, no. 2, pp. 1–3, 2017.
  * [21] T. Adam et al., “JUNO Conceptual Design Report,” arXiv preprint arXiv:1508.07166, 2015.
  * [22] S. D. Biller, E. J. Leming, and J. L. Paton, “Slow fluors for effective separation of cherenkov light in liquid scintillators,” Nucl. Instrum. Meth. A, vol. 972, p. 164106, 2020.
  * [23] Z. Guo, M. Yeh, R. Zhang, D. Cao, M. Qi, Z. Wang, and S. Chen, “Slow liquid scintillator candidates for MeV-scale neutrino experiments,” Astroparticle Physics, vol. 109, pp. 33–40, 2019.
  * [24] J. Perl, “A Generic Interface Definition for HEP Event Display Representables.” https://www.slac.stanford.edu/~perl/heprep/index.html.
  * [25] G. Doucas, N. A. Jelley, M. E. Moorhead, N. W. Tanner, S. Gil, L. McGarry, and C. E. Waltham, “Light concentrators for the Sudbury Neutrino Observatory,” Nucl. Instrum. Meth. A, vol. 370, pp. 579–596, 1996.