Temporal Stamp Classifier: Classifying Short Sequences of Astronomical Alerts

TL;DR

This paper introduces a deep learning model called the temporal stamp classifier that effectively classifies astronomical objects using sequences of alert images and metadata, achieving high accuracy and demonstrating improved performance with additional detections.

Contribution

The paper presents a novel deep learning model that classifies astronomical objects from alert sequences, incorporating metadata and demonstrating improved accuracy with more detections.

Findings

Achieves approximately 98% accuracy with 2-5 detections.

Performance improves as more detections are added.

Simple recurrence models perform competitively with complex models like LSTM.

Abstract

In this work, we propose a deep learning-based classification model of astronomical objects using alerts reported by the Zwicky Transient Facility (ZTF) survey. The model takes as inputs sequences of stamp images and metadata contained in each alert, as well as features from the All-WISE catalog. The proposed model, called temporal stamp classifier, is able to discriminate between three classes of astronomical objects: Active Galactic Nuclei (AGN), Super-Novae (SNe) and Variable Stars (VS), with an accuracy of approximately 98% in the test set, when using 2 to 5 detections. The results show that the model performance improves with the addition of more detections. Simple recurrence models obtain competitive results with those of more complex models such as LSTM.We also propose changes to the original stamp classifier model, which only uses the first detection. The performance of the latter model improves with changes in the architecture and the addition of random rotations, achieving a 1.46% increase in test accuracy.