  Photometric classifications of supernova (SN) light curves have become
necessary to utilize the full potential of large samples of observations
obtained from wide-field photometric surveys, such as the Zwicky Transient
Facility (ZTF) and the Vera C. Rubin Observatory. Here, we present a
photometric classifier for SN light curves that does not rely on redshift
information and still maintains comparable accuracy to redshift-dependent
classifiers. Our new package, Superphot+, uses a parametric model to extract
meaningful features from multiband SN light curves. We train a gradient-boosted
machine with fit parameters from 6,061 ZTF SNe that pass data quality cuts and
are spectroscopically classified as one of five classes: SN Ia, SN II, SN Ib/c,
SN IIn, and SLSN-I. Without redshift information, our classifier yields a
class-averaged F1-score of 0.61 +/- 0.02 and a total accuracy of 0.83 +/- 0.01.
Including redshift information improves these metrics to 0.71 +/- 0.02 and 0.88
+/- 0.01, respectively. We assign new class probabilities to 3,558 ZTF
transients that show SN-like characteristics (based on the ALeRCE Broker light
curve and stamp classifiers), but lack spectroscopic classifications. Finally,
we compare our predicted SN labels with those generated by the ALeRCE light
curve classifier, finding that the two classifiers agree on photometric labels
for 82 +/- 2% of light curves with spectroscopic labels and 72% of light curves
without spectroscopic labels. Superphot+ is currently classifying ZTF SNe in
real time via the ANTARES Broker, and is designed for simple adaptation to
six-band Rubin light curves in the future.
