Assessment of DKIST/VTF Capabilities for the Detection of Local Acoustic Source Wavefronts

TL;DR

This study develops a spectral-didelity framework to detect locally excited acoustic wavefronts in the solar photosphere using DKIST/VTF, by linking temperature perturbations from propagating wavefronts to line-intensity signatures via temperature response functions $RF_T$. It constructs a wavefront mask from simulation-based wavefronts, defines a wavefront-sensitivity metric with $\lambda^{*}(t)$ and $\lambda^{**}$ to identify optimal wavelengths (notably in the blue wing of Fe I lines) and to track upward propagation. Two observational strategies are proposed: (i) fast monochromatic imaging at $\lambda^{**}$ with sub-second cadence to maximize SNR, and (ii) interleaved multi-wavelength scanning across $\lambda^{*}(t)$ to trace height-dependent evolution, leveraging DKIST/VTF’s full-field capabilities. While practical limitations include instrument stability and the cadence-spectral coverage trade-off, the approach demonstrates that DKIST/VTF can meet the stringent requirements for detecting ultra-localized wavefronts, potentially enabling ultra-local helioseismic diagnostics and improved understanding of solar acoustic excitation mechanisms.

Abstract

Recent studies have demonstrated that temporal filtering can successfully identify local-acoustic-source wavefronts in radiative magnetohydrodynamic simulations of the solar photosphere. Extending this capability to observations promises new insight into the stochastic excitation of solar p-modes, the source depth distribution below the photosphere, and the dominant physical processes underlying acoustic wave excitation. Such measurements would also enable improved characterization of the complex wavefield in the lower chromosphere and open the possibility of ultra-local helioseismic diagnostics. In this work, we assess an observational strategy for the detection of local acoustic wavefronts on the Sun using the National Science Foundation's Daniel K. Inouye Solar Telescope's Visible Tunable Filter (DKIST/VTF). Because wavefront identification requires high spatial and temporal resolution and is limited by the small amplitudes of the wave perturbations, we focus on identifying specific wavelength combinations within spectral lines that maximize the sensitivity to the wave signal at the atmospheric heights where that signal is highest while minimizing contamination by atmospheric variability at other heights. Under the cadence and spectral resolution constraints of DKIST/VTF observations and for the particular simulated wavefront we examine, this approach suggests two possible strategies for the detection of acoustic wavefronts in solar observations: fast monochromatic imaging at 6302.425 A, or ordered interleaved observations in the blue wing of either the Fe I 6302.5 A or Fe I 5250.6 A line (between 6302.419 A and 6302.465 A, or between 5250.579 A and 5250.607 A respectively).

Figures (6)

• Figure 1: Propagation of a localized acoustic wavefront through the photosphere in a radiative MHD simulation. Panels show snapshots of the (A) line-of-sight velocity ( top), its (B) third-order temporal difference ( middle), and the corresponding (C) third-order temporal difference of temperature ( bottom), revealing a coherent wavefront expanding across a granulalation cell. Temporal differencing suppresses background convective motions and isolates the expanding wavefront as it traverses a granule, demonstrating the basis of the detection technique used throughout this work. Temporal increment between image frame is 12 s. The original MURaM data cubes $(N_x, N_y, N_z) = (384, 384, 256)$ are cropped to a $100 \times 100 \times 100$ pixel sub-region ($1.6 \times 1.6$ Mm horizontally). The sub-region shown in this image is centered at pixel $(x_c, y_c) = (48, 119)$, where $(x_c, y_c)$ denote global indices in the full MURaM cube.
• Figure 2: Height-dependent velocity and temperature structure of the simulated atmosphere at a single time step. The panel shows an instantaneous vertical slice of (A) line-of-sight velocity $v$ ( left), (B) temporal velocity difference $v^{(3)}$ ( middle), and (C) temperature fluctuations $T^\prime = T - T_0$, where $T_0$ is the horizontal mean temperature at each height ( right), over the full vertical extent ($3\,$Mm) of the MURaM solution. The dashed black fiducial lines indicate the most wave-sensitive height range, $z\sim0.0 - 0.42$ Mm (discussed in Section \ref{['sec:wavefronts']}), since the background atmosphere in this region is quietest. The shaded gray regions indicate the layers excluded during spectral synthesis (Section \ref{['sec:spectra']}).
• Figure 3: Construction of azimuthally averaged acoustic mask from the MURaM simulation. (A) Temporal velocity difference $v^{(3)}$ snapshot ( left) showing the acoustic wavefront in the MURaM photosphere with concentric annuli around fitted center (cyan). Annulus selected for analysis indicated with dark, bolded boundaries. (B) Standard score $Z(z,\,t)$ map for the region of interest ( right), revealing a clear, height-dependent acoustic propagation pattern. Dashed black lines indicate the most wave-sensitive height range while the solid red line outlines the region isolated by the mask described in Section \ref{['sec:perturbations']}.
• Figure 4: One-dimensional upward wave propagation constructed from azimuthally averaged perturbations in the annulus of interest. The temporally differenced velocity $v^{(3)}$ (solid black line) as well as differenced temperature $\delta T^{(3)}$ (dashed black line) are shown as functions of time and vertically offset by height, revealing the coherent upward progression of the acoustic wavefront through the atmosphere. The shaded red area indicates the wave mask $M_{\mathrm{wave}}$ of Section \ref{['sec:perturbations']} used to isolate the wave-dominated portion of the signal.
• Figure 5: Time-dependent drift of the wavelength of maximum temperature sensitivity, $\lambda^{\ast}$, averaged over the annulus of interest. The evolution of $\lambda^{\ast}$ toward the rest-frame line center ($\lambda_0$) traces the upward propagation of the acoustic wavefront and the corresponding shift in the atmospheric layer contributing most strongly to the intensity response. Error bars indicate the standard deviation of $\lambda^{\ast}$ over pixels.