Attention-Based Preprocessing Framework for Improving Rare Transient Classification

TL;DR

This paper introduces a novel data augmentation framework using image and light curve processing, synthetic sample generation, and focus techniques to enhance rare transient classification accuracy in astronomical surveys.

Contribution

It presents a new preprocessing and augmentation pipeline that improves classification of rare transients like SLSNe-I and TDEs by focusing classifier attention and generating realistic synthetic data.

Findings

Achieved 75% purity for SLSNe-I at 0.8 confidence

Achieved 75% purity for TDEs at 0.8 confidence

Significantly increased classification purity and completeness

Abstract

With large numbers of transients discovered by current and future imaging surveys, machine learning is increasingly applied to light curve and host galaxy properties to select events for follow-up. However, finding rare types of transients remains difficult due to extreme class imbalances in training sets, and extracting features from host images is complicated by the presence of bright foreground sources, particularly if the true host is faint or distant. Here we present a data augmentation pipeline for images and light curves that mitigates these issues, and apply this to improve classification of Superluminous Supernovae Type I (SLSNe-I) and Tidal Disruption Events (TDEs) with our existing NEEDLE code. The method uses a Similarity Index to remove image artefacts, and a masking procedure that removes unrelated sources while preserving the transient and its host. This focuses classifier attention on the relevant pixels, and enables arbitrary rotations for class upsampling. We also fit observed multi-band light curves with a two-dimensional Gaussian Process and generate data-driven synthetic samples by resampling and redshifting these models, cross-matching with galaxy images in the same class to produce unique but realistic new examples for training. Models trained with the augmented dataset achieve substantially higher purity: for classifications with a confidence of 0.8 or higher, we achieve 75% (43%) purity and 75% (66%) completeness for SLSNe-I (TDEs).