In modern exploration science, one has to face a major challenge : how to learn something new from analyzing a large dataset collection while taking into account what we already know. If the current knowledge overrides the analysis, the discovery of new elements may be difficult. Usually, in the field of spectroscopy, one can compare laboratory spectra, model and observation spectra. Going back and forth leads to discovery of new lines by identifying unexpected residuals in the observation data (not expected by the model). Sometimes, initial identification of lines can be wrong. As an example, spectroscopic evidence of atmospheric CO2 ice cloud was reported after the discovery of an emission spike at a wavelength of 4.3 μm from Mariner 6 and 7 infrared probings of the bright martian limb (Herr and Pimentel, 1970), but this spectral feature was mistaken for a resonant scattering band of CO2 fluorescence (López-Valverde et al., 2005). For one single spectrum, one can use simulation algorithm (see for instance Faisal et al., 2020). For large datasets, simplest ideas would be to scrutinize average spectra, or potential band depth distribution. Unfortunately, in the case of low signal-to-noise ratio (SNR, defined as signal / standard deviation of noise), such methods fail (as will be illustrated in the toy example). Analyzing residuals after modeling is a good method but it requires a lot of work. Several statistical tools with various approaches have been proposed, such as the Principal Component Analysis (PCA) (Penttilä et al., 2018; Geminale et al., 2015), or Independent Component Analysis (ICA) (Shashilov et al., 2006; Erard et al., 2009), but most of them require a human operator to pick endmembers and trends since those methods are nothing more than a change of representation. Furthermore, none of these methods guarantees positivity of the component (which are sometimes also called source), which can be problematic during the interpretation. Recently, advanced machine learning methods based on non-negative matrix factorization have been proposed (Lee and Seung, 1999; Moussaoui et al., 2008; Dobigeon et al., 2009; Schmidt et al., 2010; Gillis and Glineur, 2012; Hinrich and Mørup, 2018). This approach is completely different from PCA/ICA: each source is positive and represents an endmember / a trend. A source is not one spectrum extracted from the dataset but a statistical reconstruction. By using this approach, the human operator doesn’t have to identify endmembers/trends anymore, since they are automatically picked by the algorithm in form of source. Furthermore when there are statistical / spectral correlations between sources PCA/ICA fails because it assumes orthogonality / independence, which is not the case for non-negative matrix factorization. Based on this new approach, we propose a tool:
• to give an overview and quickly summarize a large and complex spectroscopic dataset with simple variables

• to detect potential new spectroscopic features (unexpected minor species, new absorption lines,...)
• to be performed in a fully blind way (without prior information on neither the spectra, nor the abundances).
The target observation type of this study is solar occultation. This measurement principle has been proposed as early as 1900, an interesting review was published by Smith and Hunten (1990). Several recent instruments used this technique to investigate the composition of the Earth’s (SCIAMACHY/ENVISAT Bovensmann et al. 1999), Mars’ (SPICAM Bertaux et al., 2000) or Venus’ atmospheres (SPICAV Bertaux et al., 2007). Here we will focus on the recent NOMAD instrument (Vandaele et al., 2015), and especially the SO channel, designed to study the Martian atmosphere and its trace gases, such as methane. Indeed the presence of CH4 on Mars is a very hot topic for the planetary science community (Giuranna et al., 2019; Korablev et al., 2019; Moores et al., 2019). In the present article, we propose to apply the tool for potential CH4 detection. Nevertheless, the approach can be extended to other types of spectroscopic measurements.