Cloud model inversions of strong chromospheric absorption lines using principal component analysis

TL;DR

This paper presents a fast, PCA-based inversion method for analyzing large volumes of high-resolution chromospheric Hα spectra, enabling efficient mapping of solar features like filaments and prominences.

Contribution

It introduces a PCA-accelerated cloud model inversion technique that handles big data and improves the analysis speed of chromospheric spectral observations.

Findings

PCA reduces spectral data dimensionality effectively.

The method enables rapid, noise-conditioned spectral inversions.

Physical parameter maps are generated efficiently from large datasets.

Abstract

High-resolution spectroscopy of strong chromospheric absorption lines delivers nowadays several millions of spectra per observing day, when using fast scanning devices to cover large regions on the solar surface. Therefore, fast and robust inversion schemes are needed to explore the large data volume. Cloud Model (CM) inversions of the chromospheric H$\alpha$ line are commonly employed to investigate various solar features including filaments, prominences, surges, jets, mottles, and (macro-)spicules. The choice of the CM was governed by its intuitive description of complex chromospheric structures as clouds suspended above the solar surface by magnetic fields. This study is based on observations of active region NOAA 11126 in H$\alpha$, which were obtained 2010 November 18-23 with the echelle spectrograph of the Vacuum Tower Telescope (VTT) at the Observatorio del Teide, Spain. Principal Component Analysis (PCA) reduces the dimensionality of spectra and conditions noise-stripped spectra for CM inversions. Modeled H$\alpha$ intensity and contrast profiles as well as CM parameters are collected in a database, which facilitates efficient processing of the observed spectra. Physical maps are computed representing the line-core and continuum intensity, absolute contrast, equivalent width, and Doppler velocities, among others. Noise-free spectra expedite the analysis of bisectors. The data processing is evaluated in the context of "big data", in particular with respect to automatic classification of spectra.