Active Learning with RESSPECT: Data Set

<p>This folder contains pre-processed simulated data first made available by Rick Kessler for the&nbsp;<br> <a href="https://arxiv.org/abs/1008.1024">Supernova Photometric Classification Challenge (SNPCC)</a>.</p> <p>All data were feature extracted using the <a href="https://arxiv.org/pdf/0904.1066.pdf">Bazin parametric&nbsp;function</a>.</p> <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> <br> This 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>