In high energy physics, front-end data acquisition systems based on analog-to-digital converters (ADC) can provide multiple aspects (time, energy, position) of information when an incident particle is detected. To process the shaped semi-Gaussian pulses from ADCs, multi-layer neural networks (aka. deep learning recently) show excellent accuracy and promising real-time capability. However, several factors, such as sampling rate and precision, neural network quantization bits, and intrinsic noise, complicate the problem and make it hard to find a...
