Capabilities of bisector analysis of the Si I 10 827 Å line for estimating line-of-sight velocities in the quiet Sun

Abstract

We examine the capabilities of a fast and simple method to infer line-of-sight (LOS) velocities from observations of the photospheric Si i 10827 Å line. This spectral line is routinely observed together with the chromospheric He i 10830 Å triplet as it helps to constrain the atmospheric parameters. We study the accuracy of bisector analysis and a line core fit of Si i 10827 Å. We employ synthetic profiles starting from the Bifrost enhanced network simulation. The profiles are computed solving the radiative transfer equation, including non-local thermodynamic equilibrium effects on the determination of the atomic level populations of Si i. We found a good correlation between the inferred velocities from bisectors taken at different line profile intensities and the original simulation velocity at given optical depths. This good correlation means that we can associate bisectors taken at different line-profile percentages with atmospheric layers that linearly increase as we scan lower spectral line intensities. We also determined that a fit to the linecore intensity is robust and reliable, providing information about atmospheric layers that are above those accessible through bisectors. Therefore, by combining both methods on the Si i 10827 Å line, we can seamlessly trace the quiet-Sun LOS velocity stratification from the deep photosphere to higher layers until around log τ = −3.5 in a fast and straightforward way. This method is ideal for generating quick-look reference images for future missions like the Daniel K. Inoue Solar Telescope and the European Solar Telescope, for example.

Magnetoacoustic wave energy dissipation in the atmosphere of solar pores

“…This allows the line-of-sight (LOS) velocity to be derived at different optical depths (and hence atmospheric layers) using bisector methods [31]. Through robust spectral inversions and modelling, recent work has shown that Doppler motions derived at different percentages of the Si ɪ 10827 Å line depth may be coupled to specific optical depths [32].…”

Accurately constraining velocity information from spectral imaging observations using machine learning techniques

“…Similarly, W (12) represents the matrix of weights between the hidden layer and output layer, with x (1) representing the output of the hidden layer. The biases, b ∈ R m , for each neuron of the hidden layer are given by b (1) , while the output layer can be represented by b (2) . The output of the neural network is therefore y ∈ R 5 .…”

“…x (1) = f W (01) x (0) + b (1) (3.3) and y = softmax W (12) x (1) + b (2) , (3.4) where f : R → R and softmax : R → R are the ReLU and softmax activation functions, respectively, applied to the matrix elements. Subsequently, the spectrum is then assigned a classification associated with the largest output neuron value.…”

“…Indeed, due to radiative transfer effects, these lines are formed across different heights of the solar atmosphere, with spectral information extracted at a particular percentage line depth corresponding to differing optical depths, and hence particular atmospheric heights [1]. Hence, it is possible to extract solar plasma properties as a function of optical depth (and atmospheric height if radiative transfer processes are well constrained) by taking measurements at specific percentage line depths [2].…”

“…Doppler (line-of-sight) velocities are typically acquired from spectroscopic observations using a number of techniques. They can be measured across a range of optical depths by calculating, at specific percentage line depths, the spectral line bisector shift from the central line core wavelength [2,18]. If full spectropolarimetric measurements are available, other atmospheric parameters (such as temperature, line-of-sight magnetic flux, and magnetic field inclination angles) can be estimated by inversion codes [19].…”