Unsupervised Spectral Unmixing For Telluric Correction Using A Neural Network Autoencoder

• URL: http://arxiv.org/abs/2111.09081v1
• Date: Wed, 17 Nov 2021 12:54:48 GMT
• Title: Unsupervised Spectral Unmixing For Telluric Correction Using A Neural Network Autoencoder
• Authors: Rune D. Kj{\ae}rsgaard, Aaron Bello-Arufe, Alexander D. Rathcke, Lars A. Buchhave, Line K. H. Clemmensen
• Abstract summary: 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.
• Score: 58.720142291102135
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The absorption of light by molecules in the atmosphere of Earth is a complication for ground-based observations of astrophysical objects. Comprehensive information on various molecular species is required to correct for this so called telluric absorption. 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. We accomplish this by reducing the data into a compressed representation, which allows us to unveil the underlying solar spectrum and simultaneously uncover the different modes of variation in the observed spectra relating to the absorption of $\mathrm{H_2O}$ and $\mathrm{O_2}$ in the atmosphere of Earth. We demonstrate how the extracted components can be used to remove $\mathrm{H_2O}$ and $\mathrm{O_2}$ tellurics in a validation observation with similar accuracy and at less computational expense than a synthetic approach with molecfit.

Related papers

• Unraveling Molecular Structure: A Multimodal Spectroscopic Dataset for Chemistry [0.1747623282473278]
  This dataset comprises simulated $1$H-NMR, $13$C-NMR, HSQC-NMR, Infrared, and Mass spectra for 790k molecules extracted from chemical reactions in patent data. We provide benchmarks for evaluating single-modality tasks such as structure elucidation, predicting the spectra for a target molecule, and functional group predictions.
  arXiv  Detail & Related papers  (2024-07-04T12:52:48Z)
• Machine Learning for Exoplanet Detection in High-Contrast Spectroscopy: Revealing Exoplanets by Leveraging Hidden Molecular Signatures in Cross-Correlated Spectra with Convolutional Neural Networks [0.0]
  Cross-correlation for spectroscopy uses molecular templates to isolate a planet's spectrum from its host star. We introduce machine learning for cross-correlation spectroscopy (MLCCS) The method aims to leverage weak assumptions on exoplanet characterisation, such as the presence of specific molecules in atmospheres, to improve detection sensitivity for exoplanets.
  arXiv  Detail & Related papers  (2024-05-22T09:25:58Z)
• Datacube segmentation via Deep Spectral Clustering [76.48544221010424]
  Extended Vision techniques often pose a challenge in their interpretation. The huge dimensionality of data cube spectra poses a complex task in its statistical interpretation. In this paper, we explore the possibility of applying unsupervised clustering methods in encoded space. A statistical dimensional reduction is performed by an ad hoc trained (Variational) AutoEncoder, while the clustering process is performed by a (learnable) iterative K-Means clustering algorithm.
  arXiv  Detail & Related papers  (2024-01-31T09:31:28Z)
• Efficiently predicting high resolution mass spectra with graph neural networks [28.387227518307604]
  Identifying a small molecule from its mass spectrum is the primary open problem in computational metabolomics. This is typically cast as information retrieval: an unknown spectrum is matched against spectra predicted computationally from a large database of chemical structures. We resolve this tradeoff by casting spectrum prediction as a mapping from an input molecular graph to a probability distribution over molecular formulas.
  arXiv  Detail & Related papers  (2023-01-26T21:10:26Z)
• Investigation of stellar magnetic activity using variational autoencoder based on low-resolution spectroscopic survey [0.25782420501870296]
  We apply variational autoencoder (VAE) to the LAMOST-K2 low-resolution spectra to detect magnetic activity of the stars in the K2 field. We measure the emissions of the chromospheric activity indicators, H$alpha$ and Ca$rm small II$ infrared triplet (IRT) lines. For cool active stars, we reveal a good agreement between the equivalent widths (EWs) of H$alpha$ line derived from the spectra with two resolutions.
  arXiv  Detail & Related papers  (2022-06-15T02:41:00Z)
• 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)
• Simulation of absorption spectra of molecular aggregates: a Hierarchy of Stochastic Pure States approach [68.8204255655161]
  hierarchy of pure states (HOPS) provides a formally exact solution based on local, trajectories. Exploiting the localization of HOPS for the simulation of absorption spectra in large aggregares requires a formulation in terms of normalized trajectories.
  arXiv  Detail & Related papers  (2021-11-01T16:59:54Z)
• Machine Learning for Vibrational Spectroscopy via Divide-and-Conquer Semiclassical Initial Value Representation Molecular Dynamics with Application to N-Methylacetamide [56.515978031364064]
  A machine learning algorithm for partitioning the nuclear vibrational space into subspaces is introduced. The subdivision criterion is based on Liouville's theorem, i.e. best preservation of the unitary of the reduced dimensionality Jacobian determinant. The algorithm is applied to the divide-and-conquer semiclassical calculation of the power spectrum of 12-atom trans-N-Methylacetamide.
  arXiv  Detail & Related papers  (2021-01-11T14:47:33Z)
• Two-Dimensional Single- and Multiple-Quantum Correlation Spectroscopy in Zero-Field Nuclear Magnetic Resonance [55.41644538483948]
  We present single- and multiple-quantum correlation $J$-spectroscopy detected in zero magnetic field using a Rb vapor-cell magnetometer. At zero field the spectrum of ethanol appears as a mixture of carbon isotopomers, and correlation spectroscopy is useful in separating the two composite spectra.
  arXiv  Detail & Related papers  (2020-04-09T10:02:45Z)
• Semiclassical Approach to Photophysics Beyond Kasha's Rule and Vibronic Spectroscopy Beyond the Condon Approximation. The Case of Azulene [0.0]
  We study the photophysics and spectroscopy of azulene and other non-conventional molecules. We develop a systematic, general, and efficient computational approach combining semiclassical dynamics of nuclei with ab initio electronic structure. We find that accuracy of the evaluated spectra requires the treatment of anharmonicity, Herzberg--Teller, and mode-mixing effects.
  arXiv  Detail & Related papers  (2020-01-23T09:08:29Z)

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.