Bayesian inference of β-meteoroid parameters with Solar Orbiter

<p>Solar Orbiter&#8217;s Radio and Plasma Waves instrument (SolO/RPW) is capable of detecting hypervelocity dust impacts onto the spacecraft through the fast electrical phenomena that accompany the process. SolO operates within 1AU, in the environment with high density of <em>&#946;-meteoroids</em> &#8211; dust grains escaping from the proximity of the Sun due to radiation pressure force counteracting gravity. Recently, Convolutional Neural Network (CNN) classified data were made available^[1], analyzing all the recorded waveforms and providing us with the highest quality dataset of the impact events to date.</p> <p>We present a model for the in-situ impact rate on SolO/RPW assuming &#946;-meteoroids are the main component of the detections. We fit the model to the highest quality available CNN data assisted by <em>Integrated Nested Laplace Approximation</em> (INLA) for Bayesian inference with informative priors^[2].</p> <p>Taking into account spacecraft&#8217;s position and its velocity vector, we are able to infer mean <em>radial velocity</em> of the detected dust grains to be 63 &#177; 7 km/s. We are also able to constrain <em>&#946;-meteoroid predominance</em> and <em>dust&#8217;s mean acceleration</em> and by extension constrain its mean <em>&#946;-parameter</em>. The procedure is general enough to be used in a&#160;different setting for SolO, or by a&#160;different spacecraft in the future.</p> <p>References:</p> <p>[1] Kvammen, Andreas, et al. "Machine Learning Detection of Dust Impact Signals Observed by The Solar Orbiter." (2022). https://doi.org/10.5194/egusphere-2022-725</p> <p>[2] Ko&#269;i&#353;&#269;&#225;k, Samuel, et al. "Modelling Solar Orbiter Dust Detection Rates in Inner Heliosphere as a Poisson Process."&#160;<em>arXiv preprint arXiv:2210.03562</em> (2022). https://doi.org/10.48550/arXiv.2210.03562</p>