Machine learning spectral clustering techniques: Application to Jovian clouds from Juno/JIRAM and JWST/NIRSpec

We present a new method, based on a joint application of a principal component analysis (PCA) and Gaussian mixture models (GMM), to automatically find similar groups of spectra in a collection. We applied the method (condensed in the public code chopper.py ) to archival Jupiter spectral data in the 2–5 µm range collected by NASA Juno/JIRAM in its first perijove passage (August 2016) and to mosaics of the great red spot (GRS) acquired by JWST/NIRSpec (July 2022). Using JIRAM data analyzed in previous work, we show that using a PCA+GMM clustering can increase the efficiency of the retrieval stage without any loss of accuracy in terms of the retrieved parameters. We show that a PCA+GMM approach is able to automatically identify spectra of known regions of interest (e.g., belts, zones, GRS) belonging to different clusters. The application of the method to the NIRSpec data leads to detection of substructures inside the GRS, which appears to be composed of an outer halo characterized by low reflectivity and an inner brighter main oval. By applying these techniques to JIRAM data, we were able to identify the same substructure. We remark that these new structures have not been seen before at visible wavelengths. In both cases, the spectra belonging to the inner oval have solar and thermal signals comparable to those belonging to the halo, but they present broadened 2.73 µm solar-reflected peaks. Performing forward simulations with the NEMESIS radiative transfer suite, we propose that the broadening may be caused by differences in the vertical extension of the main cloud layer. This finding is consistent with recent 3D fluid dynamics simulations.

Authors

Publication Details

Journal
Astronomy and Astrophysics
Published
2025-08-14
DOI
https://doi.org/10.1051/0004-6361/202554552
Primary Topic
Astro and Planetary Science
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Machine learning spectral clustering techniques: Application to Jovian clouds from Juno/JIRAM and JWST/NIRSpec

M.H. Wong, R. Sordini, D. Grassi, A. Mura et al.
Astronomy and Astrophysics
Astro and Planetary Science
article

Machine learning spectral clustering techniques: Application to Jovian clouds from Juno/JIRAM and JWST/NIRSpec

M.H. Wong, R. Sordini, D. Grassi, A. Mura, Francesco Biagiotti, G. Piccioni, A. Cicchetti, S. Levin, F. Tosi, R. Hueso, R. Noschese, Giuseppe Sindoni, P. G. J. Irwin, Thierry Fouchet, Christina Plainaki, Leigh N. Fletcher, Pablo Rodríguez‐Ovalle, I. de Pater, G. L. Bjoraker, S. J. Bolton, Jake Harkett, Oliver D. King, Michael T. Roman, Henrik Melin, Cheng Li, A. Adriani, G. S. Orton, Simon Toogood
article en

Abstract

We present a new method, based on a joint application of a principal component analysis (PCA) and Gaussian mixture models (GMM), to automatically find similar groups of spectra in a collection. We applied the method (condensed in the public code chopper.py ) to archival Jupiter spectral data in the 2–5 µm range collected by NASA Juno/JIRAM in its first perijove passage (August 2016) and to mosaics of the great red spot (GRS) acquired by JWST/NIRSpec (July 2022). Using JIRAM data analyzed in previous work, we show that using a PCA+GMM clustering can increase the efficiency of the retrieval stage without any loss of accuracy in terms of the retrieved parameters. We show that a PCA+GMM approach is able to automatically identify spectra of known regions of interest (e.g., belts, zones, GRS) belonging to different clusters. The application of the method to the NIRSpec data leads to detection of substructures inside the GRS, which appears to be composed of an outer halo characterized by low reflectivity and an inner brighter main oval. By applying these techniques to JIRAM data, we were able to identify the same substructure. We remark that these new structures have not been seen before at visible wavelengths. In both cases, the spectra belonging to the inner oval have solar and thermal signals comparable to those belonging to the halo, but they present broadened 2.73 µm solar-reflected peaks. Performing forward simulations with the NEMESIS radiative transfer suite, we propose that the broadening may be caused by differences in the vertical extension of the main cloud layer. This finding is consistent with recent 3D fluid dynamics simulations.

Astronomy and AstrophysicsVol. 701
National Aeronautics and Space Administration, California Institute of Technology, Northumbria University, Space Telescope Science Institute, Eusko Jaurlaritza, Nuclear Safety and Security Commission, Jet Propulsion Laboratory, Science and Technology Facilities Council, Agencia Estatal de Investigación
Openalex Percentile: Top 85%
Astro and Planetary Science
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.