  This work describes the investigation of neuromorphic computing-based spiking
neural network (SNN) models used to filter data from sensor electronics in high
energy physics experiments conducted at the High Luminosity Large Hadron
Collider. We present our approach for developing a compact neuromorphic model
that filters out the sensor data based on the particle's transverse momentum
with the goal of reducing the amount of data being sent to the downstream
electronics. The incoming charge waveforms are converted to streams of
binary-valued events, which are then processed by the SNN. We present our
insights on the various system design choices - from data encoding to optimal
hyperparameters of the training algorithm - for an accurate and compact SNN
optimized for hardware deployment. Our results show that an SNN trained with an
evolutionary algorithm and an optimized set of hyperparameters obtains a signal
efficiency of about 91% with nearly half as many parameters as a deep neural
network.
