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Poster Open Access

Linear and Neural Network Estimates of Magnetic Filling Factors on Sun-Like Stars

Milbourne, T.; Phillips, D.; Langellier, N.; Saar, S.; Walsworth, R.


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    <dct:title>Linear and Neural Network Estimates of Magnetic Filling Factors on Sun-Like Stars</dct:title>
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    <dcat:keyword>The Sun and the Heliosphere</dcat:keyword>
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        <foaf:name>Wolk, Scott</foaf:name>
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    <dct:description>&lt;p&gt;State of the art radial velocity (RV) searches for low-mass exoplanets are limited by the effects of stellar magnetic activity. Previously, we have shown that different types of active regions - spots, plage, and network - have different impacts on the apparent stellar RV. Differentiating the relative coverage of these active regions is thus necessary in order to successfully disentangle the RV signatures of stars from potential planetary signals. However, traditional activity indicators, such as the calcium S-index and photometry, only indicate the overall coverage by magnetized regions: more information is necessary to differentiate the different types of active regions. In this work, we outline techniques to estimate magnetic filling factors from spots, plage, and networks features on stellar targets using only spectroscopic and photometric observations. We demonstrate linear and neural network implementations of our techniques using real solar observations taken by the solar telescope at HARPS-N, the HK Project at the Mt. Wilson Observatory, and the Total Irradiance Monitor onboard SORCE, and compare the results of each technique to filling factors derived from full-disk images from the Solar Dynamics Observatory. We conclude by assessing the possibilities of applying these techniques to non-solar targets.&lt;/p&gt;</dct:description>
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