# A Novel Sector-Based Algorithm for an Optimized Star-Galaxy Classification

Anuamnchi Agastya Sai Ram Likhit, Divyansh Tripathi, Akshay Agarwal
Department of Physics, Department of Data Science and Engineering
Indian Institute of Science Education and Research, Bhopal
{anumanchi20, divyansh20<EMAIL_ADDRESS>
###### Abstract

This paper introduces a novel sector-based methodology for star-galaxy
classification, leveraging the latest Sloan Digital Sky Survey data (SDSS-
DR18). By strategically segmenting the sky into sectors aligned with SDSS
observational patterns and employing a dedicated convolutional neural network
(CNN), we achieve state-of-the-art performance for star galaxy classification.
Our preliminary results demonstrate a promising pathway for efficient and
precise astronomical analysis, especially in real-time observational settings.

##  1 INTRODUCTION

Today is the age of Data-Driven astronomy, with sky surveys generating large
amounts of data, and many new ones are lining up, such as the large synoptic
survey telescope (LSST). One of the key motives of such surveys is to classify
objects as stars or galaxies. However, manual classification can not be done
for petabytes of data and large intra-class variation, which raises the need
for an automated and robust classification model. Recently, several research
works have been developed to help astronomers by automatically classifying the
galaxies (Soumagnac et al., 2015; Ba Alawi & Al-Roainy, 2021; Chaini et al.,
2022; Kim & Brunner, 2016; Garg et al., 2022). However, these models perform
well but are complex. In contrast to the existing work, due to the complexity
of our star-galaxy system, in this research, we have proposed the development
of a classification approach utilizing a sector-based division of the sky. The
prime motivation for such division can be seen in Figure 1, reflecting the
variation present in different sectors and difficulties in classification. By
utilizing these differences, we have developed a star-galaxy classification
system that surpasses existing algorithms and yields a low computational cost.

Figure 1: A sample image reflecting the challenges in identifying star-
galaxies in different sectors.

##  2 Proposed Methodology

To address the star-galaxy classification challenge, we introduce a sector-
based approach closely aligned with the Sloan Digital Sky Survey (SDSS)Almeida
et al. (2023) observation patterns. For that sky is divided into thirty-six
distinct sectors (Appendix A $\&$ Figure 2) by segmenting Right Ascension (RA)
and Declination (Dec) intervals. Right Ascension (RA) is akin to longitudinal
lines on Earth and ranges from 0 hours to $24$ hours, equivalent to $0^{o}$ to
$360^{o}$ in celestial coordinates. RA is divided into six equal intervals of
$60^{o}$ each, corresponding to $4$-hour segments. Declination (Dec) which
spans from the North celestial pole at $+90^{o}$ to the South celestial pole
at $-90^{o}$ is segmented into six intervals of $30^{o}$ each. Combining the
divisions in RA and Dec, we obtain a total of 36 sectors which are defined by
their specific RA and Dec range111The other necessary details are also
provided in the Appendix..

Once the divided sector images are obtained, we have provided them as input to
the proposed custom convolutional neural network model (Appendix B). As
mentioned above, we asserted that the division of the sky into sectors can
better help in star-galaxy classification even with the use of a simple model;
therefore, a shallow model of $3$ convolutional layers have been developed.
The aim is not only to achieve higher accuracy but at a lower computational
cost. Each convolutional layer is followed by max-pooling and ReLU activation
functions. Two dense layers each containing $64$ neurons are attached at the
end to extract the features. The dropout is also added to reduce the impact of
overfitting. The configuration of the proposed CNN is given in Table 3
(Appendix B).

##  3 EXPERIMENTS AND RESULTS

Table 1: Star-Galaxy Classification performance of the proposed and existing
Algorithms in terms of accuracy (Acc), precision (P), recall (R), and F-1
score (F1).

Algorithm | Sector 10 | Sector 16 | Combined
---|---|---|---
Acc | P | R | F1 | Acc | P | R | F1 | Acc | P | R | F1
Proposed | 0.96 | 0.97 | 0.97 | 0.97 | 0.95 | 0.95 | 0.95 | 0.95 | 0.95 | 0.95 | 0.95 | 0.95
CovNet | 0.91 | 0.91 | 0.91 | 0.91 | 0.94 | 0.94 | 0.94 | 0.94 | 0.88 | 0.89 | 0.89 | 0.89
MargNet | 0.94 | 0.94 | 0.94 | 0.94 | 0.93 | 0.94 | 0.94 | 0.94 | 0.92 | 0.93 | 0.92 | 0.92

Table 2: Confusion matrix reflecting the effectiveness in handling individual sectors and classes. Algorithm |  True $\downarrow$ Predicted $\rightarrow$ | Sector-10 | Sector-16 | Combined
---|---|---|---|---
Star | Galaxy | Star | Galaxy | Star | Galaxy
Proposed | Star | 935 | 53 | 963 | 25 | 1858 | 123
Galaxy | 44 | 968 | 44 | 968 | 67 | 1952
CovNet | Star | 894 | 89 | 918 | 70 | 1742 | 238
Galaxy | 85 | 927 | 42 | 970 | 217 | 1802
MargNet | Star | 952 | 36 | 907 | 81 | 1933 | 48
Galaxy | 76 | 936 | 44 | 968 | 268 | 1751

The primary focus of the evaluation is on sector-$10$ and sector-$16$ 222More
details on the selection of these sectors are provided in the appendix D. Each
sector contributed $10,000$ images and the proposed classification model is
trained separately on each sector to determine their effectiveness in handling
individual sectors. Moreover, to evaluate the scalability of the proposed
algorithm is also evaluated on the large-scale dataset achieved after
combining the images of both sectors. Further, a comparison with existing SOTA
algorithms: CovNet Kim & Brunner (2016) and MargNet Chaini et al. (2022) has
also been performed to demonstrate the efficacy of the proposed approach. For
a fair comparison, the existing models are trained on the same training-
testing setting on which the proposed algorithm is trained.

The comparative results reported in Table 1 demonstrate that the proposed
algorithm surpasses each existing algorithm by a significant margin. Further,
the effectiveness of the proposed algorithm is not only for a single sector
but also for each and in combination. For example, the proposed algorithm on a
large-scale dataset comprising sector-10 and sector-16 yields an accuracy of
$\mathbf{95.25}$% in comparison to $88.62$% and $92.10$% of CovNet and
MargNet, respectively. Further, Table 2 shows the confusion matrix of the
proposed algorithm. It shows that the proposed algorithm is not biased to any
particular class and can effectively identify stars and galaxies with higher
accuracy.

As shown in Table 4 of the Appendix B, the proposed algorithm took
$\mathbf{25}$s/epoch on the data of the combined sectors as compared to
$180$sec and $1610$sec taken by CovNet and MargNet, respectively.

##  4 CONCLUSION

We have proposed a novel and cost-effective algorithm for star-galaxy
classification by handling sector-specific data. The efficacy of the proposed
algorithm surpasses the existing algorithm back our idea of segregating the
sky into sectors for better performance. In the future, we aim to develop an
advanced architecture to tackle other sectors and improve the classification
performance of the proposed approach by incorporating sector-specific
auxiliary information. We believe the proposed research can advance the
astronomical research by precisely identifying the celestial objects.

### URM Statement

The authors acknowledge that the key author of this work meets the URM
criteria of the ICLR 2024 Tiny Papers Track.

## References

  * Almeida et al. (2023) Andrés Almeida, Scott F. Anderson, Maria Argudo-Fernández, Carles Badenes, Kat Barger, et al.  The eighteenth data release of the sloan digital sky surveys: Targeting and first spectra from sdss-v.  _The Astrophysical Journal Supplement Series_ , TBD(TBD):TBD, 2023.  doi: 10.3847/1538-4365/acda98.  URL https://doi.org/10.3847/1538-4365/acda98.  arXiv:2301.07688 [astro-ph.GA].
  * Ba Alawi & Al-Roainy (2021) Abdulfattah Ba Alawi and Ali Al-Roainy.  Deep residual networks model for star-galaxy classification.  pp. 1–4, 07 2021.  doi: 10.1109/ICOTEN52080.2021.9493433.
  * Chaini et al. (2022) Siddharth Chaini, Atharva Bagul, Anish Deshpande, Rishi Gondkar, Kaushal Sharma, M Vivek, and Ajit Kembhavi.  Photometric identification of compact galaxies, stars, and quasars using multiple neural networks.  _Monthly Notices of the Royal Astronomical Society_ , 518(2):3123–3136, nov 2022.  doi: 10.1093/mnras/stac3336.  URL https://doi.org/10.1093%2Fmnras%2Fstac3336.
  * Garg et al. (2022) Prapti Garg, Tarini Chandra, Ritika Ahlawat, Neha Mittal, Ratneshwar Kumar Ratnesh, and Subodh Kumar Tripathi.  Star galaxy image classification via convolutional neural networks.  In _2022 3rd International Conference on Smart Electronics and Communication (ICOSEC)_ , pp. 1156–1161, 2022.  doi: 10.1109/ICOSEC54921.2022.9952065.
  * Kim & Brunner (2016) Edward J. Kim and Robert J. Brunner.  Star–galaxy classification using deep convolutional neural networks.  _Monthly Notices of the Royal Astronomical Society_ , 464(4):4463–4475, October 2016.  ISSN 1365-2966.  doi: 10.1093/mnras/stw2672.  URL http://dx.doi.org/10.1093/mnras/stw2672.
  * Soumagnac et al. (2015) M. T. Soumagnac, F. B. Abdalla, O. Lahav, D. Kirk, I. Sevilla, E. Bertin, B. T. P. Rowe, J. Annis, M. T. Busha, L. N. Da Costa, J. A. Frieman, E. Gaztanaga, M. Jarvis, H. Lin, W. J. Percival, B. X. Santiago, C. G. Sabiu, R. H. Wechsler, L. Wolz, and B. Yanny.  Star/galaxy separation at faint magnitudes: application to a simulated Dark Energy Survey.  _Monthly Notices of the Royal Astronomical Society_ , 450(1):666–680, 04 2015.  ISSN 0035-8711.  doi: 10.1093/mnras/stu1410.  URL https://doi.org/10.1093/mnras/stu1410.
  * The Sloan Digital Sky Survey (2008) The Sloan Digital Sky Survey.  Sdss dr7 coverage.  https://classic.sdss.org/dr7/coverage/404.php, 2008.

##  Appendix A Sky Division

We want to highlight that this division aligns with the inherent structure of
the SDSS, which scans the sky in stripes spaced $2.5^{o}$ apart in survey
latitude. Using a 30° sector in declination, each sector encompasses 12 SDSS
stripes. This alignment ensures data homogeneity within each sector,
containing a complete and consistent set of SDSS stripes.

The 60° x 30° sector division strikes a balance between granularity and
expansive sky coverage. It provides a detailed sky view while maintaining
enough breadth for comprehensive sector-specific analysis. This division
ensures that the data for each sector is substantial yet not overly dense,
allowing computational algorithms to be applied effectively on a per-sector
basis without requiring excessive resources.

Moreover, this sector division is not only tailored for the SDSS data
structure but also remains universally applicable. The equatorial coordinate-
based division offers a flexible foundation to integrate additional survey
data in the future. Additionally, the polar regions, often challenging in sky
segmentation due to projection distortion, are effectively managed in this
division. The sectors that extend from +60° to +90° and -60° to -90° handle
the polar regions without significant distortion.

(a) Circular Representation

(b) Rectangular Representation

Figure 2: 2D Sky Sector Map

We utilized imaging data from SDSS-DR18, for which we crafted a tailored SQL
query to extract metadata aligned with our sector-based methodology. An
automated Python script processed this metadata and constructed URLs to
download compressed FITS files. After downloading, we centered the celestial
objects within 45x45 pixel frames based on their Right Ascension (RA) and
Declination (Dec) and then converted the images into PNG format. To prepare
the data for CNN analysis, we stacked images from all filters to create a
five-channel .npy file and normalized pixel values for uniformity. We applied
data augmentation techniques to increase the dataset’s diversity and enhance
the model’s robustness.

Figure 3: Data Workflow Diagram

##  Appendix B Proposed CNN Architecture

Layer 1 | Conv2D(32, 3$\times$3 $\times$5), ReLU , MaxPool
---|---
Layer 2 | Conv2D(64, 3$\times$3 $\times$5), ReLU , MaxPool
Layer 3 | Conv2D(128, 3$\times$3 $\times$5), ReLU , MaxPool
Layer 4 | Flatten
Layer 5 | Dense (64) , Dropout(0.5)
Layer 6 | Dense (1)
Table 3: Configuration of the proposed CNN for star-galaxy classification.

All the networks are trained with a batch size of 32 and an “Adam” optimizer
with a default learning rate of 0.001. The loss function used for the work is
binary cross-entropy keeping in mind the binary nature of the problem.

Computational Cost Comparison
---
Models | Sector-10 | Sector-16 | Combined
Proposed | 15s/epoch | 13s/epoch | 25s/epoch
CovNet | 80s/epoch | 80s/epo | 180s/epoch
MargNet | 1000s/epoch | 570s/epoch | 1610s/epoch
Table 4: Comparison of Running Time per Epoch of Proposed and Existing Models

##  Appendix C Data Splitting for Model Training and Testing

Our dataset consists of 20,000 augmented images, equally distributed across
Sector-10 and Sector-16, with each sector containing 10,000 images (5,000
stars and 5,000 galaxies). We applied a train-test split of 0.2.

  * •

Individual Sector Analysis: In each sector, 8,000 images are used for
training, and 2,000 images are used for testing.

  * •

Combined Sector Analysis: When sectors are combined, the total dataset
comprises 20,000 images. Here, 16,000 images are used for training, and 4,000
images are used for testing.

Figure 4: SDSS Sky Coverage (The Sloan Digital Sky Survey, 2008) Table 5: Available SDSS Data Across Sectors Sector | RA_Range | Dec_Range | No_of_STARS | No_of_GALAXIES
---|---|---|---|---
1 | 0° to 60° | 60° to 90° | 273 | 159
2 | 60° to 120° | 60° to 90° | 5454 | 262
3 | 120° to 180° | 60° to 90° | 13133 | 31054
4 | 180° to 240° | 60° to 90° | 8239 | 33973
5 | 240° to 300° | 60° to 90° | 6628 | 2980
6 | 300° to 360° | 60° to 90° | 4771 | 381
7 | 0° to 60° | 30° to 60° | 21537 | 31868
8 | 60° to 120° | 30° to 60° | 26896 | 45414
9 | 120° to 180° | 30° to 60° | 125641 | 418122
10 | 180° to 240° | 30° to 60° | 113805 | 37085
11 | 240° to 300° | 30° to 60° | 49199 | 102023
12 | 300° to 360° | 30° to 60° | 20660 | 20119
13 | 0° to 60° | 0° to 30° | 102823 | 306967
14 | 60° to 120° | 0° to 30° | 41578 | 30375
15 | 120° to 180° | 0° to 30° | 128893 | 498274
16 | 180° to 240° | 0° to 30° | 104402 | 11901
17 | 240° to 300° | 0° to 30° | 39779 | 77124
18 | 300° to 360° | 0° to 30° | 94264 | 247358
19 | 0° to 60° | -30° to 0° | 58354 | 185367
20 | 60° to 120° | -30° to 0° | 7828 | 1920
21 | 120° to 180° | -30° to 0° | 18351 | 28538
22 | 180° to 240° | -30° to 0° | 16538 | 41186
23 | 240° to 300° | -30° to 0° | 896 | 1144
24 | 300° to 360° | -30° to 0° | 31461 | 79353
25 | 0° to 60° | -60° to -30° | 0 | 0
26 | 60° to 120° | -60° to -30° | 0 | 0
27 | 120° to 180° | -60° to -30° | 0 | 0
28 | 180° to 240° | -60° to -30° | 0 | 0
29 | 240° to 300° | -60° to -30° | 0 | 0
30 | 300° to 360° | -60° to -30° | 0 | 0
31 | 0° to 60° | -90° to -60° | 0 | 0
32 | 60° to 120° | -90° to -60° | 0 | 0
33 | 120° to 180° | -90° to -60° | 0 | 0
34 | 180° to 240° | -90° to -60° | 0 | 0
35 | 240° to 300° | -90° to -60° | 0 | 0
36 | 300° to 360° | -90° to -60° | 0 | 0

##  Appendix D Choosing Sectors: An Overview

We strategically chose sectors 10 and 16 for our analysis due to their high
star and galaxy counts as seen in below Table 5. These sectors are not only
dense but also centrally located within the SDSS sky coverage, as seen in
Figure 4, making them ideal for developing predictive models. By starting with
these sectors, we aim to establish a strong framework that can be implemented
in other sectors as well.

Figure 5: A sample image reflecting the challenges in identifying star-
galaxies in sectors 7 and 13. Figure 6: Star-Galaxy Classification performance
of the proposed and the MargNet in terms of accuracy (Acc) on Sectors 7 and
13.

##  Appendix E Other Sectors and Zero-Shot Resiliency

We have also performed experiments in the following manners to evaluate the
effectiveness and resiliency of the proposed approach.

Evaluation of Sectors 7 and 13: In this setting, we have downloaded the stars
and galaxies images of two new sectors (7 and 13). Similar to setting on
sectors 10 and 16, the images of these sectors are divided into training: used
to train the model, and testing: used for evaluation. The proposed algorithm
yields an accuracy rate of $0.95$ and $0.93$ in comparison to best best-
performing existing algorithm namely MargNet, which yields an accuracy of
$0.93$ and $0.78$ on sector 7 and 13 images, respectively. The samples of
stars and galaxies concerning sectors 7 and 13 are shown in Figure 5 and the
classification results are reported in Figure 6.

Zero-shot Sector Resiliency: In this setting, we have performed a 4-way zero
setting experimental evaluation of the proposed algorithm and the MargNet. In
other words, we have performed 4 fold cross-validation experiments, where in
every fold one unseen sector is used for evaluation, and the remaining three
sectors are used for training the classifier. It is observed that the proposed
algorithm yields an average accuracy of $0.89$ in comparison to an accuracy of
$0.78$ achieved by the MargNet. Further, the standard deviation of the
proposed algorithm and the MargNet are $0.04$ and $0.17$, respectively. We
believe the reliability against unseen sectors of the proposed algorithm
further establishes the claim of its effectiveness in performing stars and
galaxies classification.


# Training a spiking neural network on an event-based label-free flow
cytometry dataset

Muhammed Gouda<EMAIL_ADDRESS>Ghent University -
imecGentBelgium ,  Steven Abreu<EMAIL_ADDRESS>University of
GroningenGroningenNetherlands ,  Alessio Lugnan<EMAIL_ADDRESS>Ghent
University - imecGentBelgium and  Peter Bienstman<EMAIL_ADDRESS>Ghent University - imecGentBelgium

(2022)

###### Abstract.

Imaging flow cytometry systems aim to analyze a huge number of cells or micro-
particles based on their physical characteristics. The vast majority of
current systems acquire a large amount of images which are used to train deep
artificial neural networks. However, this approach increases both the latency
and power consumption of the final apparatus. In this work-in-progress, we
combine an event-based camera with a free-space optical setup to obtain spikes
for each particle passing in a microfluidic channel. A spiking neural network
is trained on the collected dataset, resulting in 97.7% mean training accuracy
and 93.5% mean testing accuracy for the fully event-based classification
pipeline.

††copyright: acmcopyright††journalyear: 2022††conference: ; April 11–14, 2023;
San Antonio, TX, USA

##  1\. Introduction

Flow cytometry is a technique used to classify different types of cells based
on characteristics such as size, shape, or fluorescence. Often, the cells used
in flow cytometry are labeled with different biomarkers. Such biomarkers can
lead to chemical interactions with the cells that impair the experimental data
(Doan et al., 2018). Label-free, or stain-free, imaging flow cytometry collect
images from flowing cells which have not been labelled. These images are used
to train a machine learning model for classification.

The goal of a flow cytometry setup is to classify with high accuracy and low
latency (or, equivalently, high throughput). To achieve this, it is necessary
to use a sophisticated high-speed camera and a lot of computing power to
process large amounts of data.

Recent work has shown that a neuromorphic event-based camera can achieve low
latency with ¿1,000 frames per second (He et al., 2022). Importantly, the
event-based camera does not record full frames at each time step, but rather
records only changes in the scene at each pixel. This operating principle
inherently filters out temporally redundant information.

However, in order to use conventional computer vision algorithms on event-
based data, it is usually necessary to convert the data into frames. A more
natural approach is to process the event-based imaging data using event-based
image processing algorithms. We propose the use of spiking neural networks
(SNNs) to process the imaging data in a fully event-based and asynchronous
way. In this work-in-progress, we show first results that we obtained by
training a spiking neural network on event-based flow cytometry data on a GPU.
To the best of our knowledge, this are the first results of event-based flow
cytometry using spiking neural networks in the literature (Ref. (He et al.,
2022) uses an artificial neural network, and Ref. (Zhang et al., 2022) does
not report any performance results).

##  2\. Methods

We proceed by describing the experimental setup in Section 2.1 and the spiking
neural network we trained in Section 2.2.

###  2.1. Imaging flow cytometry

Figure 1. The free-space optical setup built in this work. Light emitted by
the 1550 nm He-Ne laser is focused on a PMMA microfluidic channel.
Diffraction, scattering and interference of light due to moving particles
cause changes in the intensity of light incident on the event sensor.

The experimental setup is shown in Figure 1. Here, we send artificial PMMA
microbeads of different sizes to a narrow microfluidic channel (of width 200
$\mu m$). The intensity of the light detected by the event-camera stays
constant as long as no particle is moving across the field of view. This does
not trigger any pixel to fire events. However, once particles enter the field
of view,diffraction, scattering and interference of the light trigger firing
events which we use later to train our spiking neural network. The fact that
we obtain events only in the presence of particles significantly improves the
memory efficiency of the overall system. The size of the dataset we get with
our system is on the order of tens of gigabytes, whereas in a previous work
using a traditional frame-based camera, the dataset was on the order of
hundreds of gigabytes (Lugnan et al., 2020).

We used two different classes of spherical microparticles (class A of diameter
16 µm and class B with a diameter of 20 µm). We ran four separate experiments
for each class of microparticles, where each experiment ran for
$T_{exp}\approx 60s$ for a total of 480 seconds of data. The accumulation time
for a single particle is $T_{acc}\approx 10ms$. Therefore, we have around
6,000 samples per experiment, or 24,000 samples in total per class. We train
the network for four different train-test splits, each split using a different
experiment as testing data, and the remaining experiments as training data.

###  2.2. Spiking neural network

We pre-process our event-based imaging data using the Tonic library (Lenz et
al., 2021). The pre-processing involved a spatial downsampling from $640\times
480\times 2$ to $32\times 24\times 2$ (event polarity is left unchanged), and
a temporal downsampling by passing the events for each neuron corresponding to
a downsampled pixel of fixed polarity through a discretized leaky-integrate-
and-fire (LIF) neuron:

$\displaystyle s^{out}_{i}(n+1)$ $\displaystyle=1\text{ if }\left(\beta
u_{i}(n)+ws^{in}_{i}(n+1)\right)\geq u_{thr}\text{ else }0$ $\displaystyle
r_{i}(n+1)$ $\displaystyle=\max_{t\in\\{0,\ldots,t_{rf}\\}}s^{out}_{i}(n+1-t)$
$\displaystyle u_{i}(n+1)$ $\displaystyle=0\text{ if
}\left(r_{i}(n+1)\right)\text{ else }\left(\beta
u_{i}(n)+ws^{in}_{i}(n+1)\right)$

where $u_{i}(n)$, $s^{out}_{i}(n)$, $r_{i}(n)$ denote the membrane potential,
binary spike output, and binary refractory period of neuron $i$ at timestep
$n$, respectively. Further, $\beta=0.9$ is the membrane decay rate, $w=1.0$ is
the synaptic weight, $u_{thr}=3.0$ is the threshold voltage, $t_{rf}=2$ is the
refractory period, $s^{in}_{i}(n)$ equals unity if there is an input event
from the event-based camera in the $i$th $20\times 20\times 1$ patch of pixels
at timestep $n$ (and zero otherwise).

For classification, we use a feedforward network of LIF neurons with an input
layer of size $32\times 24\times 2=1536$, a hidden layer of size $100$, and an
output layer of size $20$ (using population coding with $10$ neurons per
output class). The neuron parameters are constant for all neurons in the
network, with the membrane decay rate $\beta=0.9$ and membrane threshold
$U_{thr}=0.5$. We use the Adam optimizer to optimize the mean squared error on
the output spike rate, with a desired population spike rate of $20\%$
(correct) and $80\%$ (incorrect). Backpropagation is applied through a shifted
Heaviside function in the forward pass and a fast sigmoid

$\displaystyle S=\frac{U}{1+k\|U\|}$

with slope $k=75$ in the backward pass (Zenke and Ganguli, 2018). We use
snnTorch (Eshraghian et al., 2021) for our implementation.

##  3\. Results

We trained the spiking neural network described in Section 2.2 for 10 epochs
on a NVIDIA GeForce RTX 2080 Ti GPU with 11 GB of memory. Figure 2 show the
training and testing performance for this experiment. The figure shows that
the accuracy exceeds 98.5% for all experiments, with the exception of the
testing accuracy for the network trained on experiments 1, 3, and 4. More work
is needed to analyze the anomalous performance on this data split.

Figure 2. Performance of the spiking neural network trained with
backpropagation for ten epochs. Dashed lines show testing, solid lines show
training. Different lines indicate which experiments were used for training,
the remaining experiment was used for testing only.

##  4\. Outlook

As next steps, we are investigating the use of exact timing information for a
time-to-first-spike classification that should yield even faster
classification. In this work, we fixed the hyperparameters use to the values
mentioned in Section 2.2. For further performance gains, we will optimize the
hyperparameters and network architecture through a cross-validation procedure.
Finally, we will transfer the SNN to a neuromorphic chip for a fully
neuromorphic processing pipeline.

###### Acknowledgements.

This work was performed in the context of the European projects Neoteric
(grant agreement 871330), Prometheus (grant agreement 101070195) and Post-
Digital project (grant agreement 860360).

## References

  * (1)
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  * Eshraghian et al. (2021) Jason K. Eshraghian, Max Ward, Emre Neftci, Xinxin Wang, Gregor Lenz, Girish Dwivedi, Mohammed Bennamoun, Doo Seok Jeong, and Wei D. Lu. 2021.  Training Spiking Neural Networks Using Lessons From Deep Learning.  (2021).  arXiv:2109.12894
  * He et al. (2022) Weihua He, Yongxiang Feng, Junwen Zhu, Huichao Chai, and Wenhui Wang. 2022.  Neuromorphic-Enabled Event-Based Deep Imaging Flow Cytometry. In _26th International Conference on Miniaturized Systems for Chemistry and Life Sciences_.
  * Lenz et al. (2021) Gregor Lenz, Kenneth Chaney, Sumit Bam Shrestha, Omar Oubari, Serge Picaud, and Guido Zarrella. 2021\.  _Tonic: event-based datasets and transformations._ Documentation available under https://tonic.readthedocs.io.
  * Lugnan et al. (2020) Alessio Lugnan, Emmanuel Gooskens, Jeremy Vatin, Joni Dambre, and Peter Bienstman. 2020.  Machine learning issues and opportunities in ultrafast particle classification for label-free microflow cytometry.  _Scientific Reports_ 10, 1 (2020), 1–13.
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  * Zhang et al. (2022) Ziyao Zhang, Maria Sabrina Ma, Jason K. Eshraghian, Daniele Vigolo, Ken-Tye Yong, and Omid Kavehei. 2022\.  Work in Progress: Neuromorphic Cytometry, High-throughput Event-based flow Flow-Imaging. In _2022 8th International Conference on Event-Based Control, Communication, and Signal Processing (EBCCSP)_. IEEE.