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

¹ Department of Physics, University of Rome “La Sapienza”, Piazzale Aldo Moro 2, 00185 Rome, Italy
² INAF – Istituto di Astrofisica e Planetologia Spaziali (INAF-IAPS), Via Fosso del Cavaliere 100, I-00133 Rome, Italy
³ School of Physics and Astronomy, University of Leicester, University Rd, Leicester LE1 7RH, UK
⁴ Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, 8170121 Santiago, Chile
⁵ Department of Astronomy, University of California, Berkeley, CA 94720, USA
⁶ LIRA, Observatoire de Paris, Université PSL, CNRS, Sorbonne Université, Université Paris-Cité, Meudon, France
⁷ Space Sciences Laboratory, University of California, Berkeley CA 94720-7450, USA
⁸ Carl Sagan Center for Research, SETI Institute, Mountain View, CA 94043, USA
⁹ Escuela de Ingeniería de Bilbao, Universidad del País Vasco, UPV/EHU, Bilbao, Spain
¹⁰ Department of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle upon Tyne, UK
¹¹ Atmospheric, Oceanic and Planetary Physics, Department of Physics, University of Oxford, Oxford, UK
¹² Agenzia Spaziale Italiana, Via del Politecnico, snc, 00133 Roma, Italy
¹³ Max Planck Institute for Solar System Research, Justus-von-Liebig-Weg 3, 37077 Göttingen, Germany
¹⁴ Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr, Pasadena, CA 91109, USA
¹⁵ NASA Goddard Space Flight Center, Code 693, Greenbelt, MD 20771, USA
¹⁶ Department of Climate and Space Sciences and Engineering, Univ. of Michigan, 2455 Hayward St, Ann Arbor, MI 48109, USA
¹⁷ Southwest Research Institute, 6220 Culebra Rd, San Antonio, TX 78238, USA

^★ Corresponding author.

Received: 15 March 2025
Accepted: 24 July 2025

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.