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Photometric Classification of Astronomical Transients and Variables With Biased Spectroscopic Samples

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Avocado

Photometric Classification of Astronomical Transients and Variables With Biased Spectroscopic Samples

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avocado is a general photometric classification code that is designed to produce classifications of arbitrary astronomical transients and variable objects. This code is designed from the ground up to address the problem of biased spectroscopic samples. It does so by generating many lightcurves from each object in the original spectroscopic sample at a variety of redshifts and with many different observing conditions. The "augmented" samples of lightcurves that are generated are much more representative of the full datasets than the original spectroscopic samples.

The original codebase of avocado was developed for and won the 2018 Kaggle PLAsTiCC challenge. A paper describing the algorithms implemented in this package can be found here, and there is also a discussion available on the kaggle forum. The PLAsTiCC results can be replicated using the latest version of avocado following the steps in the documentation, and all of the code used to generate the figures in the paper can be found in the avocado_paper_figures.ipynb notebook.

Carrick et al. 2021 (submitted to MNRAS) used avocado to study how to optimize spectroscopic training samples for photometric classification of supernovae. An example of the code used in that analysis can be found in the spcc_augment_methods.ipynb notebook.

Installation and Usage

Instructions on how to install and use avocado can be found on the avocado readthedocs page.

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Photometric Classification of Astronomical Transients and Variables With Biased Spectroscopic Samples

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