Dataset Open Access

Active Learning with RESSPECT: Data Set

da Silva de Souza, Rafael; Kennamer, Noble; de Oliveira Ishida, Emille Eugenia


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        <foaf:name>de Oliveira Ishida, Emille Eugenia</foaf:name>
        <foaf:givenName>Emille Eugenia</foaf:givenName>
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    <dct:title>Active Learning with RESSPECT: Data Set</dct:title>
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    <dcat:keyword>supernova</dcat:keyword>
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    <dct:description>&lt;p&gt;This folder contains pre-processed simulated data first made available by Rick Kessler for the&amp;nbsp;&lt;br&gt; &lt;a href="https://arxiv.org/abs/1008.1024"&gt;Supernova Photometric Classification Challenge (SNPCC)&lt;/a&gt;.&lt;/p&gt; &lt;p&gt;All data were feature extracted using the &lt;a href="https://arxiv.org/pdf/0904.1066.pdf"&gt;Bazin parametric&amp;nbsp;function&lt;/a&gt;.&lt;/p&gt; &lt;p&gt;This version of the data set&amp;nbsp;was used to obtain the results reported in&amp;nbsp;&lt;a href="https://arxiv.org/pdf/2010.05941.pdf"&gt;Kennamer et al., 2020&amp;nbsp;- &lt;em&gt;Active learning with RESSPECT: resource allocation for extragalactic astronomical transients&lt;/em&gt;.&lt;/a&gt;&amp;nbsp;Published during the&amp;nbsp;&lt;a href="http://www.ieeessci2020.org/symposiums/ciastro.html"&gt;2020 IEEE Symposium Series on Computational Intelligence&lt;/a&gt;. The code used to obtain the results shown in the paper is available in the &lt;a href="https://github.com/COINtoolbox/RESSPECT"&gt;COINtoolbox&lt;/a&gt; (github).&amp;nbsp;&lt;br&gt; &lt;br&gt; This work was developed under the &lt;a href="https://cosmostatistics-initiative.org/resspect/"&gt;RESSPECT project&lt;/a&gt;, an inter-collaboration agreement established between the &lt;a href="https://lsstdesc.org/"&gt;LSST Dark Energy Science Collaboration (LSST-DESC)&lt;/a&gt; and the &lt;a href="https://cosmostatistics-initiative.org/"&gt;Cosmostatistics Initiative (COIN)&lt;/a&gt; in order to develop an active learning pipeline to advise the allocation of telescope resources.&lt;/p&gt;</dct:description>
    <dct:description>The Cosmostatistics Initiative was supported by the French government via a CNRS-MOMENTUM grant from 2018-2020 under the project: Active Learning for Large Scale Sky Surveys.</dct:description>
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