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

Dineva, Ekaterina; Verma, M.; Manrique, S. J. González; Schwartz, P.

doi:10.1002/asna.202013652

Cited by 9 publications

(15 citation statements)

References 26 publications

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“…The size of a scan step is 0.16 and a pixel along the slit corresponds to 0.18 . The data processing steps were described in Dineva et al (2020), which included PCA for noise stripping and computation of Cloud Model (CM) inversions (Beckers 1964). The final spectra are resampled to 601 wavelength points, which cover a wavelength range of ±3 Å around the Hα line core.…”

Section: Observations and Datamentioning

confidence: 99%

“…where I 0 (λ ) is the quiet-Sun spectral profile obtained either from observations or model atmospheres. We used the observed quiet-Sun background profiles of David (1961) and interpolated them for the heliocentric angle of µ = 0.86 as explained in Verma et al (2020) and Dineva et al (2020).…”

Section: Observations and Datamentioning

confidence: 99%

“…CM inversions (Beckers 1964) are widely used in solar physics to infer physical parameters for dark cloud-like absorption features observed in the Hα line (e.g., Tziotziou 2007). With some fundamental assumptions (see e.g., Kuckein et al 2016;Dineva et al 2020) four parameters are determined which define the radiative transfer and line formation. These four parameters are the optical thickness τ 0 , Doppler velocity of the cloud v D , Doppler width ∆λ D of the absorption profile, and source function S. Profiles which are suitable for CM inversions are characterized by strong absorption profiles.…”

Section: Observations and Datamentioning

confidence: 99%

“…Note that CM inversions cannot reproduce the strong central component with positive contrasts. More details on the implementation and computation of CM inversions are given in Dineva et al (2020).…”

Section: Observations and Datamentioning

confidence: 99%

“…The benchmark spectral data comprise intensity profiles, contrast profiles, and their counterparts based on the superposition of specific eigenfunctions derived from PCA and CM inversions. According to Dineva et al (2020), ten eigenfunctions are sufficient to construct the observed Hα profiles. In the present study, application of PCA and CM inversions yields noise-stripped contrast profiles, which are used as input for the t-SNE algorithm unless stated otherwise.…”

Section: Observations and Datamentioning

confidence: 99%

See 4 more Smart Citations

Classification of High-resolution Solar Hα Spectra using t-distributed Stochastic Neighbor Embedding

Verma,

Matijevič,

Denker

et al. 2020

Preprint

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The Hα spectral line is a well-studied absorption line revealing properties of the highly structured and dynamic solar chromosphere. Typical features with distinct spectral signatures in Hα include filaments and prominences, bright active-region plages, superpenumbrae around sunspots, surges, flares, Ellerman bombs, filigree, and mottles and rosettes, among others. This study is based on high-spectral resolution Hα spectra obtained with the echelle spectrograph of the Vacuum Tower Telescope (VTT) located at Observatorio del Teide (ODT), Tenerife, Spain. The t-distributed Stochastic Neighbor Embedding (t-SNE) is a machine learning algorithm, which is used for nonlinear dimensionality reduction. In this application, it projects Hα spectra onto a two-dimensional map, where it becomes possible to classify the spectra according to results of Cloud Model (CM) inversions. The CM parameters optical depth, Doppler width, line-of-sight velocity, and source function describe properties of the cloud material. Initial results of t-SNE indicate its strong discriminatory power to separate quiet-Sun and plage profiles from those that are suitable for CM inversions. In addition, a detailed study of various t-SNE parameters is conducted, the impact of seeing conditions on the classification is assessed, results for various types of input data are compared, and the identified clusters are linked to chromospheric features. Although t-SNE proves to be efficient in clustering high-dimensional data, human inference is required at each step to interpret the results. This exploratory study provides a framework and ideas on how to tailor a classification scheme towards specific spectral data and science questions.

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Section: Observations and Datamentioning

confidence: 99%

Section: Observations and Datamentioning

confidence: 99%

Section: Observations and Datamentioning

confidence: 99%

Section: Observations and Datamentioning

confidence: 99%

Section: Observations and Datamentioning

confidence: 99%

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Classification of High-resolution Solar Hα Spectra using t-distributed Stochastic Neighbor Embedding

Verma,

Matijevič,

Denker

et al. 2020

Preprint

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High-resolution spectroscopy of a surge in an emerging flux region

Verma

Denker

Diercke

et al. 2020

A&A

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Aims. The regular pattern of quiet-Sun magnetic fields was disturbed by newly emerging magnetic flux, which led a day later to two homologous surges after renewed flux emergence, affecting all atmospheric layers. Hence, simultaneous observations in different atmospheric heights are needed to understand the interaction of rising flux tubes with the surrounding plasma, in particular by exploiting the important diagnostic capabilities provided by the strong chromospheric Hα line regarding morphology and energetic processes in active regions. Methods. A newly emerged active region NOAA 12722 was observed with the Vacuum Tower Telescope (VTT) at Observatorio del Teide, Tenerife, Spain, on 11 September 2018. High spectral resolution observations using the echelle spectrograph in the chromospheric Hαλ6562.8 Å line were obtained in the early growth phase. Noise-stripped Hα line profiles yield maps of line-core and bisector velocities, which were contrasted with velocities inferred from Cloud Model inversions. A high-resolution imaging system recorded simultaneously broad- and narrowband Hα context images. The Solar Dynamics Observatory provided additional continuum images, line-of-sight (LOS) magnetograms, and UV and extreme UV (EUV) images, which link the different solar atmospheric layers. Results. The active region started as a bipolar region with continuous flux emergence when a new flux system emerged in the leading part during the VTT observations, resulting in two homologous surges. While flux cancellation at the base of the surges provided the energy for ejecting the cool plasma, strong proper motions of the leading pores changed the magnetic field topology making the region susceptible to surging. Despite the surge activity in the leading part, an arch filament system in the trailing part of the old flux remained stable. Thus, stable and violently expelled mass-loaded ascending magnetic structures can coexist in close proximity. Investigating the height dependence of LOS velocities revealed the existence of neighboring strong up- and downflows. However, downflows occur with a time lag. The opacity of the ejected cool plasma decreases with distance from the base of the surge, while the speed of the ejecta increases. The location at which the surge becomes invisible in Hα corresponds to the interface where the surge brightens in He IIλ304 Å. Broad-shouldered and dual-lobed Hα profiles suggests accelerated or decelerated and highly structured LOS plasma flows. Significantly broadened Hα profiles imply significant heating at the base of the surges, which is also supported by bright kernels in UV and EUV images uncovered by swaying motions of dark fibrils at the base of the surges. Conclusions. The interaction of newly emerging flux with pre-existing flux concentrations of a young, diffuse active region provided suitable conditions for two homologous surges. High-resolution spectroscopy revealed broadened and dual-lobed Hα profiles tracing accelerated or decelerated flows of cool plasma along the multi-threaded structure of the surge.

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Multiple Stokes I inversions for inferring magnetic fields in the spectral range around Cr I 5782 Å

Kuckein

Balthasar

Noda

et al. 2021

A&A

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Aims. In this work, we explore the spectral window containing Fraunhofer lines formed in the solar photosphere, around the magnetically sensitive Cr I lines at 5780.9, 5781.1, 5781.7, 5783.0, and 5783.8 Å, with Landé g-factors between 1.6 and 2.5. The goal is to simultaneously analyze 15 spectral lines, comprising Cr I, Cu I, Fe I, Mn I, and Si I lines, without the use of polarimetry, to infer the thermodynamic and magnetic properties in strongly magnetized plasmas using an inversion code. Methods. Our study is based on a new setup at the Vacuum Tower Telescope (VTT, Tenerife), which includes fast spectroscopic scans in the wavelength range around the Cr I 5781.75 Å line. The oscillator strengths log(gf) of all spectral lines, as well as their response functions to temperature, magnetic field, and Doppler velocity, were determined using the Stokes Inversion based on Response functions (SIR) code. Snapshot 385 of the enhanced network simulation from the Bifrost code serves to synthesize all the lines, which are, in turn, inverted simultaneously with SIR to establish the best inversion strategy. We applied this strategy to VTT observations of a sunspot belonging to NOAA 12723 on 2018 September 30 and compared the results to full-disk vector field data obtained with the Helioseismic and Magnetic Imager (HMI). Results. The 15 simultaneously inverted intensity profiles (Stokes I) delivered accurate temperatures and Doppler velocities when compared with the simulations. The derived magnetic fields and inclinations achieve the best level of accuracy when the fields are oriented along the line-of-sight (LOS) and less accurate when the fields are transverse to the LOS. In general, the results appear similar to what is reported in the HMI vector-field data, although some discrepancies exist. Conclusions. The analyzed spectral range has the potential to deliver thermal, dynamic, and magnetic information for strongly magnetized features on the Sun, such as pores and sunspots, even without the use of polarimetry. The highest sensitivity of the lines is found in the lower photosphere, on average, around log τ = −1. The multiple-line inversions provide smooth results across the whole field of view (FOV). The presented spectral range and inversion strategy will be used for future VTT observing campaigns.

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

Cited by 9 publications

References 26 publications

Classification of High-resolution Solar Hα Spectra using t-distributed Stochastic Neighbor Embedding

Classification of High-resolution Solar Hα Spectra using t-distributed Stochastic Neighbor Embedding

High-resolution spectroscopy of a surge in an emerging flux region

Multiple Stokes I inversions for inferring magnetic fields in the spectral range around Cr I 5782 Å

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

Cited by 9 publications

References 26 publications

Classification of High-resolution Solar Hα Spectra using t-distributed Stochastic Neighbor Embedding

Classification of High-resolution Solar Hα Spectra using t-distributed Stochastic Neighbor Embedding

High-resolution spectroscopy of a surge in an emerging flux region

Multiple Stokes I inversions for inferring magnetic fields in the spectral range around Cr I 5782 Å

Contact Info

Product

Resources

About

Multiple Stokes I inversions for inferring magnetic fields in the spectral range around Cr I 5782 Å