Neural network for determining an asteroid mineral composition from
reflectance spectra
- URL: http://arxiv.org/abs/2210.01006v1
- Date: Mon, 3 Oct 2022 15:14:05 GMT
- Title: Neural network for determining an asteroid mineral composition from
reflectance spectra
- Authors: David Korda, Antti Penttil\"a, Arto Klami, Tom\'a\v{s} Kohout
- Abstract summary: Chemical and mineral compositions of asteroids reflect the formation and history of our Solar System.
We aim to develop a fast and robust neural-network-based method for deriving the mineral modal and chemical compositions of silicate materials from their visible and near-infrared spectra.
- Score: 4.282159812965446
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Chemical and mineral compositions of asteroids reflect the formation and
history of our Solar System. This knowledge is also important for planetary
defence and in-space resource utilisation. We aim to develop a fast and robust
neural-network-based method for deriving the mineral modal and chemical
compositions of silicate materials from their visible and near-infrared
spectra. The method should be able to process raw spectra without significant
pre-processing. We designed a convolutional neural network with two hidden
layers for the analysis of the spectra, and trained it using labelled
reflectance spectra. For the training, we used a dataset that consisted of
reflectance spectra of real silicate samples stored in the RELAB and C-Tape
databases, namely olivine, orthopyroxene, clinopyroxene, their mixtures, and
olivine-pyroxene-rich meteorites. We used the model on two datasets. First, we
evaluated the model reliability on a test dataset where we compared the model
classification with known compositional reference values. The individual
classification results are mostly within 10 percentage-point intervals around
the correct values. Second, we classified the reflectance spectra of S-complex
(Q-type and V-type, also including A-type) asteroids with known Bus-DeMeo
taxonomy classes. The predicted mineral chemical composition of S-type and
Q-type asteroids agree with the chemical composition of ordinary chondrites.
The modal abundances of V-type and A-type asteroids show a dominant
contribution of orthopyroxene and olivine, respectively. Additionally, our
predictions of the mineral modal composition of S-type and Q-type asteroids
show an apparent depletion of olivine related to the attenuation of its
diagnostic absorptions with space weathering. This trend is consistent with
previous results of the slower pyroxene response to space weathering relative
to olivine.
Related papers
- Unsupervised Machine Learning for the Classification of Astrophysical
X-ray Sources [44.99833362998488]
We develop an unsupervised machine learning approach to provide probabilistic classes to Chandra Source Catalog sources.
We provide a catalog of probabilistic classes for 8,756 sources, comprising a total of 14,507 detections.
We investigate the consistency between the distribution of features among classified objects and well-established astrophysical hypotheses.
arXiv Detail & Related papers (2024-01-22T18:42:31Z) - Taxonomic analysis of asteroids with artificial neural networks [7.274273862904249]
In the near future, the Chinese Space Survey telescope (CSST) will provide multiple colors and spectroscopic data for asteroids of apparent magnitude brighter than 25 mag and 23 mag.
We apply an algorithm using artificial neural networks (ANNs) to establish a preliminary classification model for asteroid taxonomy.
Using the SMASS II spectra and the Bus-Binzel taxonomy system, our ANN classification tool composed of 5 individual ANNs is constructed.
arXiv Detail & Related papers (2023-11-18T03:27:26Z) - Rapid detection of soil carbonates by means of NIR spectroscopy, deep
learning methods and phase quantification by powder Xray diffraction [0.0]
We propose a rapid and efficient way to predict carbonates content in soil by means of FT NIR spectroscopy and by use of deep learning methods.
We exploited multiple machine learning methods, such as: 1) a Regressor and 2) a CNN and compare their performance with other traditional ML algorithms.
arXiv Detail & Related papers (2023-07-23T14:32:07Z) - Deep Learning Models of the Discrete Component of the Galactic
Interstellar Gamma-Ray Emission [61.26321023273399]
A significant point-like component from the small scale (or discrete) structure in the H2 interstellar gas might be present in the Fermi-LAT data.
We show that deep learning may be effectively employed to model the gamma-ray emission traced by these rare H2 proxies within statistical significance in data-rich regions.
arXiv Detail & Related papers (2022-06-06T18:00:07Z) - Tracking perovskite crystallization via deep learning-based feature
detection on 2D X-ray scattering data [137.47124933818066]
We propose an automated pipeline for the analysis of X-ray diffraction images based on the Faster R-CNN deep learning architecture.
We demonstrate our method on real-time tracking of organic-inorganic perovskite structure crystallization and test it on two applications.
arXiv Detail & Related papers (2022-02-22T15:39:00Z) - Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet
Transmission Spectra [68.8204255655161]
We focus on unsupervised techniques for analyzing spectral data from transiting exoplanets.
We show that there is a high degree of correlation in the spectral data, which calls for appropriate low-dimensional representations.
We uncover interesting structures in the principal component basis, namely, well-defined branches corresponding to different chemical regimes.
arXiv Detail & Related papers (2022-01-07T22:26:33Z) - Analytical Modelling of Exoplanet Transit Specroscopy with Dimensional
Analysis and Symbolic Regression [68.8204255655161]
The deep learning revolution has opened the door for deriving such analytical results directly with a computer algorithm fitting to the data.
We successfully demonstrate the use of symbolic regression on synthetic data for the transit radii of generic hot Jupiter exoplanets.
As a preprocessing step, we use dimensional analysis to identify the relevant dimensionless combinations of variables.
arXiv Detail & Related papers (2021-12-22T00:52:56Z) - Unsupervised Spectral Unmixing For Telluric Correction Using A Neural
Network Autoencoder [58.720142291102135]
We present a neural network autoencoder approach for extracting a telluric transmission spectrum from a large set of high-precision observed solar spectra from the HARPS-N radial velocity spectrograph.
arXiv Detail & Related papers (2021-11-17T12:54:48Z) - Measuring chemical likeness of stars with RSCA [0.0]
We present a novel data-driven model that is capable of identifying chemically similar stars from spectra alone.
We find that our representation identifies known stellar siblings more effectively than stellar abundance measurements.
arXiv Detail & Related papers (2021-10-05T18:03:59Z) - Disentangled Representation Learning for Astronomical Chemical Tagging [0.0]
We present a method for isolating the chemical factors of variation in stellar spectra from those of other parameters.
This enables us to build a spectral projection for each star with these parameters removed.
Our work demonstrates the feasibility of data-driven abundance-free chemical tagging.
arXiv Detail & Related papers (2021-03-10T22:55:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.