Title: Data from: Noisy spectra to Particle properties: A machine learning analysis of Enceladus plume spectral data using Cassini-VIMS observations DOI:10.7923/8phz-dr49 RCDS:258268e3-7437-4333-8190-777fab84e304 Data, code and/or products within this dataset support the following manuscript: Manuscript Title: Noisy spectra to Particle properties: A machine learning analysis of Enceladus plume spectral data using Cassini-VIMS observations Journal: JGR-Planets DOI: ###Pending Description/Abstract: The physical properties of Enceladus plume particles can shed light on the processes responsible for driving the moon's geological activity. Cassini's Visual and Infrared Mapping Spectrometer (VIMS) recorded near-infrared spectra of the plume for three Enceladus orbits around Saturn in 2017 that show variations reflecting changes in the typical plume particle size over various timescales. We translate these spectra into information about the plume-particle's size distribution using a machine learning model trained on Mie-theory predictions for the light scattered by various tenuous particle populations. This algorithm considers multiple realizations of random noise on top of both the observed and predicted spectra in order to obtain more stable estimates of the size distribution parameters. These models reveal that the typical particle size may decrease with increasing altitude, but this stratification is only detectable when Enceladus is far from its orbital apocenter and the plume is less active. The average particle size also appears to increase as the orbital phase increases after Enceladus passed through its apocenter during the observations on Jun 18th and Aug 2nd, but not during the observation on Aug 28th. Secondly, the maximum particle size in the plume appears to be elevated on Aug 2nd, which may be due to a highly collimated jet that was active only on that date. These patterns might indicate that there are different sources for the particles and their intensity may be changing over time. **Data Use** *License*: Creative Commons Attribution 4.0 International ([CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/)) *Recommended Citation*: Sharma H, Hedman MM. 2024. Data from: Noisy spectra to Particle properties: A machine learning analysis of Enceladus plume spectral data using Cassini-VIMS observations [Dataset]. University of Idaho. https://doi.org/10.7923/8phz-dr49 **Funding** NASA Cassini Data Analysis Program: 80NSSC18K1071 **Ancillary Raw Data** [Cassini VIMS: Visual and Infrared Mapping Spectrometer](https://pds-atmospheres.nmsu.edu/data_and_services/atmospheres_data/Cassini/inst-vims.html) Resource URL: https://data.nkn.uidaho.edu/dataset/data-noisy-spectra-particle-properties-machine-learning-analysis-enceladus-plume-spectral Creator(s): 1. Himanshi Sharma Unique identifier: https://orcid.org/0000-0002-0803-2678 Affiliation(s): University of Idaho, USA 2. Matthew M. Hedman Unique identifier: https://orcid.org/0000-0002-8592-0812 Affiliation(s): University of Idaho, USA Other Contributor(s): 1. Sanaz Vahidinia Unique identifier: Affiliation(s): National Aeronautics and Space Administration, Ames Research Center Role: Sponsor Publisher: University of Idaho Publication Year: 2024 Language(s): American English Subject(s): 1. NATURAL SCIENCES 1.03 Physical sciences and astronomy Planetary science Keywords/Tags: Cassini-VIMS, Enceladus Plume, Machine Learning Resource Type General: Dataset Dates: NULL Date available for the public: 2024-09-12 Sizes: 2.4 GB Format(s): .ipynb, .txt, .csv, .png, .jpeg, .cub, .erpj, .cmat, .sav Version: NULL Funding References: Funder Name: National Aeronautics and Space Administration Award Number: 80NSSC18K1071 Award Title: INVESTIGATING THE ENCELADUS PLUME WITH CASSINI-VIMS REMOTE-SENSING DATA Award URL: NULL Spatial/Geographical Coverage Location: NULL Temporal Coverage: Start Date: 2017-06-18 End Date: 2017-08-28 Granularity of the Data: NULL Contact Info: Contact Name: Himanshi Sharma Contact Email: hbbhardwaj02@gmail.com Related Content: Peer Reviewed Manuscript - JGR-Planets | https://doi.org/###Pending Ancillary Raw PDS Data | https://pds-atmospheres.nmsu.edu/data_and_services/atmospheres_data/Cassini/inst-vims.html Data/Code Files: The repository contains 3 major folders corresponding to the three orbits of Cassini: - Enceladus Plume_279 - Enceladus Plume_286 - Enceladus Plume_290 Each of these folders contain a subfolder with the calibrated files for their respective orbits: - calibrated_S100_279 - calibrated_S101_286 - calibrated_S101_290 The calibrated .erpj files contain the I/F data we use in our analysis. The repo contains wavelength_vims.txt file that containts the wavelengths used in this analysis. The jupyter notebooks in the repo: - Enceladus plume background removal.ipynb creates and saved the following files for each orbit. The below file names are just example for orbit no 279 - outlier_cube_indices_279.txt saves the index of outliers cubes/images in orbit no 279 - images_binned_indices_279.txt saves the index of images/cubes that have been averaged/binned together in multiple of 10 each. For example first 10 cubes that are binned together have indices [4, 5, 6, ...] of size 10 - inten_before_alt_279.txt plume spectra intensity across altitude before background removal in the shape of 240 spectral channels, 29 orbital phases, 30 altitudes. Values are stored with their respective standard deviation. - inten_after_alt_279.txt plume spectra intensity across altitude after background removal in the shape of 240 spectral channels, 29 orbital phases, 30 altitudes. Values are stored with their respective standard deviation. - Mie Code_100.ipynb generates mie spectra at the scattering angles of all 3 orbits and uses the folliwing: - 100.txt contains the refractive index at 100K for all the relevant wavelength channels. - encplume_filelist_geom_S100_279_062420 contains the parameters for the corresponding orbit. (SImilary for the other 2 orbits) And creates the following files: - File wavelength_index.txt stores the index at which the wavelength for which refractive indices (used to generate mie spectra) are defined coincides with the wavelength corresponding to plume observations. - File mie_wavelength.txt contains the wavelength for mie spectra - File plume_wavelength.txt contains the wavelength for plume spectra - File max_radius.txt saves the maximum radius for each of the 676 combinations of size parameters - File power_index.txt saves the power law index for each of the 676 combinations of size parameters - Folder Enceladus Plume_279 (similarly for orbit 286 and 290) - File binned_orbital_phase_279.txt that stores binned orbital phase values for orbit no. 279 - File binned_scattering_angle_279.txt that stores binned scattering angle values for orbit no. 279 - File mie_spectra_100.txt stores the spectra generated using mie scattering theory. - File mie_scatt_angle.txt save the 32 scattering angles for which mie spectra is generated. - Plume and Mie binning_100_50 real.ipynb creates the following important files (these are replicated for the other 2 orbits): - plume_spectra_279_100_50 - scatt_index_279: these files save the index of the scattering angles - mie_spectra_279_100_50: contacins mie spectra with augmentations - plume_spectra_279_100_50_err - plume_spectra_279_orb_100_50 - ML model_100_50 calculates the fianl results : - plume_spectra_results_279_100_50 and similarly for the other 2 orbits - EW_mie_augmented - EW_plume_augmented - mie_spectra_rad - mie_spectra_pow - max_rad_50_aug_man - pow_50_aug_man - size_orb_50_aug_man