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This work describes the investigation of neuromorphic computing-based spikingneural network (SNN) models used to filter data from sensor electronics in highenergy physics experiments conducted at the High Luminosity Large HadronCollider. We present our approach for developing a compact neuromorphic modelthat filters out the sensor data based on the particle's transverse momentumwith the goal of reducing the amount of data being sent to the downstreamelectronics. The incoming charge waveforms are converted to streams ofbinary-valued events, which are then processed by the SNN. We present ourinsights on the various system design choices - from data encoding to optimalhyperparameters of the training algorithm - for an accurate and compact SNNoptimized for hardware deployment. Our results show that an SNN trained with anevolutionary algorithm and an optimized set of hyperparameters obtains a signalefficiency of about 91% with nearly half as many parameters as a deep neuralnetwork.

On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments