# Reconstruction of Ultra-High Vacuum Mass Spectra Using Genetic Algorithms
## 1 Introduction  
Residual gas analysis aims at identifying which gas species are present in vacuum systems and serves the purpose of finding the level and nature of contamination in those systems. The process of generating an ultra-high vacuum (UHV) may be affected by the presence of contaminants of different origins, such as aromatics, paint, oil, alcohols and cleaning agents. Such contaminants are deposited (mainly during the manufacturing process) on the inner surface of the vacuum chambers and hinder the pump-down process and, consequently, the generation of the required pressure. In particular, the presence of some contaminants may have effects on many other surface properties, such as the surface energy and secondary electron yield [1, 2]. Residual gas analyzers (RGAs) are the devices used to provide a measurement of the traces of contamination present in vacuum systems by generating a mass spectrum (MS). A MS consists of a two-dimensional graph that represents relative intensity peaks as a function of mass/charge ratio (\(m/z\), where \(m\) refers to the ion mass and \(z\) refers to the charge state) [3]. Most commercial RGAs used in UHV applications are mobile quadrupole mass spectrometers (QMS) [4] using an electron-impact ion source with a limited mass range of 1-100 amu, sometimes up to 200 amu. The mass resolution of these instruments is, in general, in the range of 0.5 amu full width half maximum (FWHM), and 0.2 amu (FWHM) at best. In the MS obtained by the analyzer, several gas species might be present. Usually, the obtained MS is analyzed and compared with the existing profiles in a standard library [5,6], such as the National Institute for Standards and Technology (NIST) MS database: [[https://chemdata.nist.gov/](https://chemdata.nist.gov/)]([https://chemdata.nist.gov/](https://chemdata.nist.gov/)) [7], (accessed on 30 November 2021). The relative contribution of each species is quantified by its partial pressure, which is commonly measured in mbar. Those contributions are combined linearly and weighted by their partial pressures to obtain the final MS. In a real scenario, the effects of the offset level, intensity cut-off limits and noise may be considered as well. The identification or recognition process of gas species may be time consuming, especially when their quantitative contribution to the mass spectrum is required (MS reconstruction). This is because the partial pressures of the residual gases cover several orders of magnitudes, and fragmentation patterns are, in general, convoluted (sharing content at different \(m/z\) ratios). Noise, offset, and the limited mass ranges of the analyzers used in UHV applications further reduce the sensitivity of a spectrum [8]. Consequently, a thorough interpretation of the mass spectrum requires its deconvolution. Deconvolution procedures for MS coupled to liquid chromatography (LC-MS) or gas chromatography (GC-MS) are typically used in literature related to proteomics and metabolomics [9,10]. In chromatography, deconvolution is the process of computationally separating co-eluting components and creating a pure spectrum for each component. In these fields, many techniques have already addressed this problem, namely: peak shape modeling techniques, feature selection algorithms and blind source separation algorithms, such as the family of band-target entropy minimization (BTEM) algorithms [11]. In UHV systems, MS identification is, in general, manually done by human experts, which is both time consuming and prone to errors. The vacuum community has recently directed their efforts to devise intelligent, automatic methods to aid humans in the task of MS identification. Early works have already explored the potential of neural networks for automatic MS recognition [12]. However, reconstruction is a more complicated task than recognition.