# Timing and characterization of shaped pulses with MHz ADCs in a detector system: a comparative study and deep learning approach

## TL;DR

This paper introduces a deep learning method for extracting timing information from ADC samples in detector systems, demonstrating significant improvements over traditional curve fitting in both simulations and real experiments.

## Contribution

It presents a novel deep learning approach for timing extraction from ADC data, outperforming traditional methods in accuracy and noise suppression.

## Key findings

- Deep learning greatly reduces noise RMS in timing measurements.
- The method improves timing resolution by over 20% in experiments.
- Simulations confirm the robustness of the neural network approach under various conditions.

## Abstract

Timing systems based on Analog-to-Digital Converters are widely used in the design of previous high energy physics detectors. In this paper, we propose a new method based on deep learning to extract the time information from a finite set of ADC samples. Firstly, a quantitative analysis of the traditional curve fitting method regarding three kinds of variations (long-term drift, short-term change and random noise) is presented with simulation illustrations. Next, a comparative study between curve fitting and the neural networks is made to demonstrate the potential of deep learning in this problem. Simulations show that the dedicated network architecture can greatly suppress the noise RMS and improve timing resolution in non-ideal conditions. Finally, experiments are performed with the ALICE PHOS FEE card. The performance of our method is more than 20% better than curve fitting in the experimental condition.