Photometric classification of supernovae detected by the Zwicky Transient Facility using noise augmentation

TL;DR

This paper introduces a noise-augmented, feature-based photometric classifier for ZTF supernovae, achieving high accuracy in identifying Type Ia supernovae for cosmological research.

Contribution

It presents a novel noise augmentation technique combined with an autoencoder and decision tree classifier to improve SN Ia classification in large photometric surveys.

Findings

Noise augmentation improves classification of faint sources.

SN Ia recall exceeds 98% for bright and moderately faint sources.

Overall classification accuracy is 95% for the dataset.

Abstract

Modern time-domain surveys, such as the Zwicky Transient Facility (ZTF), detect far more extragalactic transients than can be spectroscopically classified. Photometric classification offers a scalable alternative, enabling the identification of larger, fainter, and higher-redshift supernova samples suitable for applications such as Type Ia supernova (SN Ia) cosmology. We present a feature-based photometric classifier for SNe detected by ZTF, with the primary goal of constructing a photometric SN Ia sample for cosmological analyses. Our approach utilises the autoencoder architecture of ParSNIP (Boone 2021) to capture the intrinsic diversity of SN light curves. We trained the model on a spectroscopically classified ZTF SN sample, incorporating a realistic noise augmentation procedure that simulates the flux uncertainties of fainter sources. Light curve features were used to train a gradient-boosted decision tree classifier, implemented in both binary (SN Ia vs. non-Ia) and multi-class configurations. We validated our classifier on independent, fainter ZTF data with and without noise augmentation. To evaluate real-time performance, we also applied our classifier to live ZTF alerts and conducted a spectroscopic classification survey within the ePESSTO+ collaboration. We found that noise augmentation significantly improves classification performance, particularly for fainter sources. Our binary classifier achieves an SN Ia recall of (98.1 $\pm$ 0.4)%, averaged across five train-test splits. SN Ia recall exceeds 98% for events with a peak apparent magnitude up to 20 and more than 10 detections, and remains above 96% up to magnitude 20.5. Overall, 95% of sources were correctly classified in both binary and multi-class modes. Our classifier performs efficiently on real ZTF data and enables construction of a large photometric SN Ia sample for cosmology.