The visibility of low-frequency solar acoustic modes

TL;DR

This paper predicts the detectability of low-frequency solar acoustic modes using extrapolated data, Monte Carlo simulations, and statistical tests, highlighting the challenges and potential improvements in solar data quality.

Contribution

It introduces a method to estimate low-frequency mode detectability and demonstrates the importance of enhanced data quality for future observations.

Findings

Detection probability decreases at lower frequencies

Doubling S/N ratio improves detection significantly

Current data quality limits low-frequency mode detection

Abstract

We make predictions of the detectability of low-frequency p modes. Estimates of the powers and damping times of these low-frequency modes are found by extrapolating the observed powers and widths of higher-frequency modes with large observed signal-to-noise ratios. The extrapolations predict that the low-frequency modes will have small signal-to-noise ratios and narrow widths in a frequency-power spectrum. Monte Carlo simulations were then performed where timeseries containing mode signals and normally distributed Gaussian noise were produced. The mode signals were simulated to have the powers and damping times predicted by the extrapolations. Various statistical tests were then performed on the frequency-amplitude spectra formed from these timeseries to investigate the fraction of spectra in which the modes could be detected. The results of these simulations were then compared to the number of p-modes candidates observed in real Sun-as-a-star data at low frequencies. The fraction of simulated spectra in which modes were detected decreases rapidly as the frequency of modes decreases and so the fraction of simulations in which the low-frequency modes were detected was very small. However, increasing the signal-to-noise (S/N) ratio of the low-frequency modes by a factor of 2 above the extrapolated values led to significantly more detections. Therefore efforts should continue to further improve the quality of solar data that is currently available.