October 6, 2021 Poster Open Access

Zhao, Yinan; Dumusque, Xavier

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        <foaf:name>Zhao, Yinan</foaf:name>
        <foaf:givenName>Yinan</foaf:givenName>
        <foaf:familyName>Zhao</foaf:familyName>
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        <foaf:name>Dumusque, Xavier</foaf:name>
        <foaf:givenName>Xavier</foaf:givenName>
        <foaf:familyName>Dumusque</foaf:familyName>
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    <dct:title>SOAP-GPU: Efficient Spectral Modelling of Stellar Activity Using Graphical Processing Units</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2021</dct:issued>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2021-10-06</dct:issued>
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    <dct:description>&lt;p&gt;Stellar activity mitigation is one of the major challenges for the detection of earth-like exoplanets in radial velocity measurements. Several promising techniques are now investigating the use of spectral time-series, to differentiate between stellar and planetary perturbations. In this context, developing a software that can efficiently explore the parameter space of stellar activity at the spectral level is of great importance. We developed a new version of the Spot Oscillation And Planet (SOAP) 2.0 code that can model stellar activity at the spectral level using graphical processing units (GPUs). Benchmarking calculations show that this new code improves the computational speed by a factor of 60 while having the same accuracy. On top of that, we implemented a realistic simulation of activity on solar-type stars (number of active regions, evolution), therefore allowing to generate realistic spectral time-series affected by activity perturbations within seconds. The outcome of this code is essential to test any algorithm that tries improving planetary signal detection by mitigating stellar activity at the spectral level.&lt;/p&gt;</dct:description>
    <dct:description>{"references": ["Dumusque, X., Boisse, I., &amp; Santos, N. C. 2014, ApJ, 796, 132", "Borgniet, S., Meunier, N., &amp; Lagrange, A. M. 2015, A&amp;A, 581, A133"]}</dct:description>
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