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Active Learning with RESSPECT: Data Set

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


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    "description": "<p>This folder contains pre-processed simulated data first made available by Rick Kessler for the&nbsp;<br>\n<a href=\"https://arxiv.org/abs/1008.1024\">Supernova Photometric Classification Challenge (SNPCC)</a>.</p>\n\n<p>All data were feature extracted using the <a href=\"https://arxiv.org/pdf/0904.1066.pdf\">Bazin parametric&nbsp;function</a>.</p>\n\n<p>This version of the data set&nbsp;was used to obtain the results reported in&nbsp;<a href=\"https://arxiv.org/pdf/2010.05941.pdf\">Kennamer et al., 2020&nbsp;- <em>Active learning with RESSPECT: resource allocation for extragalactic astronomical transients</em>.</a>&nbsp;Published during the&nbsp;<a href=\"http://www.ieeessci2020.org/symposiums/ciastro.html\">2020 IEEE Symposium Series on Computational Intelligence</a>. The code used to obtain the results shown in the paper is available in the <a href=\"https://github.com/COINtoolbox/RESSPECT\">COINtoolbox</a> (github).&nbsp;<br>\n<br>\nThis work was developed under the <a href=\"https://cosmostatistics-initiative.org/resspect/\">RESSPECT project</a>, an inter-collaboration agreement established between the <a href=\"https://lsstdesc.org/\">LSST Dark Energy Science Collaboration (LSST-DESC)</a> and the <a href=\"https://cosmostatistics-initiative.org/\">Cosmostatistics Initiative (COIN)</a> in order to develop an active learning pipeline to advise the allocation of telescope resources.</p>", 
    "contributors": [], 
    "title": "Active Learning with RESSPECT: Data Set", 
    "license": {
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    "notes": "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.", 
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    "publication_date": "2020-10-26", 
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