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Generate noisified lightcurves based on the BTS sample and retrain Parsnip with these.

Project description

ztfparsnip

PyPI version CI Coverage Status

Retrain Parsnip for ZTF. This is achieved by using fpbot forced photometry lightcurves of the Bright Transient Survey. These are augmented (redshifted, noisifed and - when possible - K-corrected).

The package is maintained by A. Townsend (HU Berlin) and S. Reusch (DESY).

The following augmentation steps are taken:

  • draw uniformly from a redshift distribution with maximum redshift increase delta_z
  • only accept lightcurves with at least one datapoint making the signal-to-noise threshold SN_threshold
  • only accept lightcurves with at least n_det_threshold datapoints
  • for those lightcurves that have an existing SNCosmo template, apply a K-correction at that magnitude (if k_corr=True)
  • randomly drop datapoints until subsampling_rate is reached
  • add some scatter to the observed dates (jd_scatter_sigma in days)
  • if phase_lim=True, only keep datapoints drugin a typical duration (depends on the type of source)

Usage

Create augmented training sample

from pathlib import Path
from ztfparsnip.create import CreateLightcurves
weights = {"sn_ia": 9400, "tde": 9400, "sn_other": 9400, "agn": 9400, "star": 9400}
if __name__ == "__main__":
    sample = CreateLightcurves(
        output_format="parsnip",
        classkey="simpleclasses",
        weights=weights,
        train_dir=Path("train"),
        plot_dir=Path("plot"),
        seed=None,
        phase_lim=True,
        k_corr=True,
    )
    sample.select()
    sample.create(plot_debug=False)

Train Parsnip with the augmented sample

from ztfparsnip.train import Train
if __name__ == "__main__":
    train = Train(classkey="simpleclasses", seed=None)
    train.run()

Evaluate

Coming soon.

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