Active Learning with RESSPECT: Data Set

This folder contains pre-processed simulated data first made available by Rick Kessler for the 
Supernova Photometric Classification Challenge (SNPCC).

All data were feature extracted using the Bazin parametric function.

This version of the data set was used to obtain the results reported in Kennamer et al., 2020 - Active learning with RESSPECT: resource allocation for extragalactic astronomical transients. Published during the 2020 IEEE Symposium Series on Computational Intelligence. The code used to obtain the results shown in the paper is available in the COINtoolbox (github). 

This work was developed under the RESSPECT project, an inter-collaboration agreement established between the LSST Dark Energy Science Collaboration (LSST-DESC) and the Cosmostatistics Initiative (COIN) in order to develop an active learning pipeline to advise the allocation of telescope resources.