Identifying acoustic wave sources on the Sun I. Two-dimensional waves in a simulated photosphere
Shah Mohammad Bahauddin, Mark Peter Rast

TL;DR
This study develops a neural network-based filtering method to identify and analyze acoustic wave sources in the solar photosphere within simulations, revealing new insights into their properties and locations.
Contribution
The paper introduces a novel filtering technique using neural network-derived properties to detect local acoustic wave sources in simulated solar data, bypassing complex direct measurements.
Findings
Acoustic sources cluster at mesogranular scales.
Peak emission occurs about 500 km below the photosphere.
Strong emission sites result from merging supersonic downflows.
Abstract
The solar acoustic oscillations are likely stochastically excited by convective dynamics in the solar photosphere, though few direct observations of individual source events have been made and their detailed characteristics are still unknown. Wave source identification requires measurements that can reliably discriminate the local wave signal from the background convective motions and resonant modal power. This is quite challenging as these 'noise' contributions have amplitudes several orders of magnitude greater than the sources and the propagating wave fields they induce. In this paper, we employ a high-temporal-frequency filter to identify sites of acoustic emission in a radiative magnetohydrodynamic simulation. The properties of the filter were determined from a convolutional neural network trained to identify the two-dimensional acoustic Green's function response of the atmosphere,…
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