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Determining if non-thermal emission is likely to be present in a sub-A class X-ray microflare observed by NuSTAR using nested sampling

Presentation #104.06 in the session Flares and Particle Acceleration.

Published onSep 18, 2023
Determining if non-thermal emission is likely to be present in a sub-A class X-ray microflare observed by NuSTAR using nested sampling

We use the Nuclear Spectroscopic Telescope Array (NuSTAR) to investigate very weak X-ray microflares that are highly frequent and occur in active regions (ARs). NuSTAR is an astrophysical direct imaging X-ray telescope capable of observing the Sun with a unique sensitivity to emission >2.2 keV. Determining the emission mechanisms present in flares at weak scales can be difficult as we commonly find a weak but significant emission excess at higher energies after fitting an isothermal model to the spectrum, and it is sometimes unclear whether this excess is due to accelerated particles or flare-heated plasma. We employ a nested sampling algorithm to compute the parameter posterior distributions and the evidence of one model representing the excess with thermal emission and another with non-thermal emission. Calculating Bayes factor from the evidences, we are able to assess which emission mechanism is more likely to be responsible for the count excess. We present spectral, spatial, and temporal analysis performed on an A0.003 GOES class equivalent microflare that was observed with NuSTAR on 2021 November 17 from AR SPoCA 26190. Along with EUV observations from the Solar Dynamics Observatory’s Atmospheric Imaging Assembly, we utilise a new Python spectral fitting tool and present the first application of nested sampling in solar X-ray spectroscopy to investigate the impulsive phase spectrum of the microflare. We show there is significantly stronger evidence for the high energy count excess to be represented by non-thermal emission from flare accelerated electrons than by an additional hot thermal source. Nested sampling analysis can be performed in future spectral fitting investigations where there is ambiguity between several model representations of the data, providing a quantitive degree of confidence for one model over another.

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