The high performance parameterization for deep learning in pulse shaping.

Area of Science:

• High Energy Physics
• Particle Detection
• Data Acquisition Systems

Background:

• Front-end data acquisition systems in high energy physics utilize analog-to-digital converters (ADCs) to capture particle event data, including time, energy, and position.
• Multi-layer neural networks (deep learning) offer high accuracy and real-time processing capabilities for shaped semi-Gaussian pulses from ADCs.
• Optimizing performance and cost-effectiveness is challenging due to factors like sampling rate, precision, quantization bits, and intrinsic noise.

Purpose of the Study:

• To systematically analyze the impact of key factors on neural network performance in particle detection data acquisition.
• To identify optimal configurations for cost-effective, high-performance deep learning solutions.
• To develop a network architecture capable of extracting both time and energy information from single particle pulses.

Main Methods:

• Controlled analysis of individual factors affecting neural network performance, including sampling rate, sampling precision, and neural network quantization bits.
• Development and testing of a novel neural network architecture designed for simultaneous time and energy information extraction.
• Evaluation of network performance under various conditions, focusing on a specific configuration (N2) with an 8-bit encoder and 16-bit decoder.

Main Results:

• The study systematically quantifies the influence of sampling rate, precision, and quantization on deep learning model accuracy for particle pulse analysis.
• The proposed network architecture successfully extracts both time and energy information from individual particle pulses.
• The N2 network configuration, utilizing a 5-bit sampling precision and 2.5 MHz sampling rate, demonstrated superior comprehensive performance across tested conditions.

Conclusions:

• Systematic analysis provides crucial insights into optimizing deep learning models for high energy physics data acquisition.
• The developed neural network architecture offers a promising solution for efficient and accurate time and energy measurement.
• The N2 configuration represents a cost-effective and high-performance choice for advanced particle detection systems.