AI Spectroscopy & Composition

NEW LITERATURE ADDED REGULARLY

2023:

A robust, agnostic molecular biosignature based on machine learning. 2023, Cleaves H. J. et al., PNAS

https://doi.org/10.1073/pnas.2307149120

Keywords: Random Forest, Biosignatures

A machine learning classification of meteorite spectra applied to understanding asteroids. 2023, Dyar, M. D. Icarus.

https://doi.org/10.1016/j.icarus.2023.115718

Keywords: Spectroscopy, Classification, Support Vector Machines, Logistic Regression

Machine learning methods applied to combined Raman and LIBS spectra: Implications for mineral discrimination in planetary missions. 2023,Julve-Gonzalez, S. et al.  J Raman Spectrosc. 

https://doi.org/10.1002/jrs.6611

Keywords: Spectroscopy, Planetary Science, Support Vector Machines, Neural Nets, Random Forest

Exploratory data analysis (EDA) machine learning approaches for ocean world analog mass spectrometry. 2023, Da Poian, V. et al. Front. Astron. Space Sci.

https://doi.org/10.3389/fspas.2023.1134141

Keywords: Cluster Analysis, Dimension Reduction

2022:

Convolutional neural networks as a tool for Raman spectral mineral classification under low signal, dusty Mars conditions. 2022, Berlanga, G. et al.  Earth and Space Science.

https://doi.org/10.1029/2021EA002125

Keywords: Raman spectroscopy, CNN, Neural Nets, Classification

2018:

Supervised machine learning for analysing spectra of exoplanetary atmospheres. 2018, Márquez-Neila, P. et al. Nat Astron 

https://doi.org/10.1038/s41550-018-0504-2

Keywords: Exoplanets, Classification, Random Forest