# Real-bogus classification for the Zwicky Transient Facility using deep learning

## TL;DR

This paper introduces 'braai', a deep learning convolutional neural network that effectively distinguishes real astrophysical events from false positives in ZTF data, enhancing detection accuracy and efficiency.

## Contribution

The paper presents a novel deep learning classifier 'braai' for real/bogus detection in ZTF data, with open-source tools and deployment on TPUs for improved efficiency.

## Key findings

- Achieves low false negative and false positive rates.
- Demonstrates comparable accuracy on TPU hardware.
- Enables cost-effective processing for large-scale surveys.

## Abstract

Efficient automated detection of flux-transient, reoccurring flux-variable, and moving objects is increasingly important for large-scale astronomical surveys. We present braai, a convolutional-neural-network, deep-learning real/bogus classifier designed to separate genuine astrophysical events and objects from false positive, or bogus, detections in the data of the Zwicky Transient Facility (ZTF), a new robotic time-domain survey currently in operation at the Palomar Observatory in California, USA. Braai demonstrates a state-of-the-art performance as quantified by its low false negative and false positive rates. We describe the open-source software tools used internally at Caltech to archive and access ZTF's alerts and light curves (Kowalski), and to label the data (Zwickyverse). We also report the initial results of the classifier deployment on the Edge Tensor Processing Units (TPUs) that show comparable performance in terms of accuracy, but in a much more (cost-) efficient manner, which has significant implications for current and future surveys.