# PMT Fluorescence Signal Denoising Processing Based on Wavelet Transform and BP Neural Network
## 1 Introduction  
With the rapid development of modern industry, the harmful gases produced in chemical industry production have attracted people's attention. Among them, sulfur dioxide is the main component that causes sulfuric acid rain. Usually, based on different detection principles and implementation methods, gas detection techniques can be divided into two categories: optical detection methods and non-optical detection methods. The development and application of non-optical detection methods occurred earlier; they directly detect and quantitatively analyze the measured gas by detecting the physical and chemical reactions between substances. There exist electrochemical methods, chemical fluorescence methods, gas chromatography, and mass spectrometry methods. However, the above-mentioned detection methods often fail to meet the requirements of real-time monitoring of air pollution and harsh environmental conditions in some application scenarios due to poorsensitivity, selectivity, and linearity, as well as long measurement times and the need for offline detection.  
In recent years, optical-based trace gas detection methods have made significant progress. According to different detection principles, absorption spectroscopy is mainly divided into direct absorption spectroscopy technology and indirect absorption spectroscopy technology. There are various types of direct absorption spectroscopy techniques, and there are many mature ones that have been studied and applied, such as tunable diode laser absorption spectroscopy (TDLAS) [1; 2], cavity ring down absorption spectroscopy (CRDS) [3; 4], and cavity enhancement technology [5; 6]. Typical indirect absorption spectroscopy techniques include fluorescence spectroscopy [7], photothermal spectroscopy (PTS) [8; 9; 10], and photoacoustic spectroscopy (PAS).  
The ultraviolet fluorescence method based on the principle of photoluminescence has become the mainstream solution due to its advantages of high accuracy, high reliability, and no other pollution. Regarding UV fluorescence detection technology for sulfur dioxide gas concentration, [PERSON] analyzed and studied the relationship between excitation wavelength and fluorescence intensity in the spectral range of 200 nm-230 nm. The experiment showed that under appropriate excitation wavelength, the fluorescence intensity detected by the detector can be used to measure the content of sulfur dioxide in the gas. This experiment also became the basic experiment for sulfur dioxide ultraviolet fluorescence detection, and it was later verified through experimental methods that 213.8 nm is the optimal excitation wavelength for detecting sulfur dioxide fluorescence [11]. Afterwards, [PERSON] first used an Nd: YAG laser to induce fluorescence in sulfur dioxide [12]. [PERSON] et al. achieved a detection sensitivity of 20 ppm for SO\({}_{2}\) gas using a UV diode laser [13]. [PERSON] used a broadband optical parametric oscillator to generate double frequency light in a laser-induced fluorophore, achieving a detection limit of 5 ppt for SO\({}_{2}\) in the instrument [14]. However, due to the extremely low content of sulfur dioxide gas in the air, the excited fluorescence signal was very weak. Meanwhile, the process of photoelectric signal detection is inevitably affected by temperature drift [15; 16] and electronic noise, which poses high requirements for the acquisition circuit and subsequent data processing. Therefore, it is of great significance to develop new gas detection technologies and methods with high sensitivity, good selectivity, high detection efficiency, and real-time online monitoring. This paper achieves the effective capture and amplification of weak fluorescence signals by combining appropriate optical paths and acquisition circuits. At the same time, wavelet transform [17] is used to denoise the PMT signal, and a BP neural network [18] is used to process the temperature drift of PMT. After verification, it is found the detection device [19] has a high accuracy and strong anti-interference ability.