Deep Attention-Based Supernovae Classification of Multi-Band Light-Curves

Abstract

In astronomical surveys, such as the Zwicky Transient Facility, supernovae (SNe) are relatively uncommon objects compared to other classes of variable events. Along with this scarcity, the processing of multi-band light-curves is a challenging task due to the highly irregular cadence, long time gaps, missing-values, few observations, etc. These issues are particularly detrimental to the analysis of transient events: SN-like light-curves. We offer three main contributions: 1) Based on temporal modulation and attention mechanisms, we propose a Deep attention model (TimeModAttn) to classify multi-band light-curves of different SN types, avoiding photometric or hand-crafted feature computations, missing-value assumptions, and explicit imputation/interpolation methods. 2) We propose a model for the synthetic generation of SN multi-band light-curves based on the Supernova Parametric Model, allowing us to increase the number of samples and the diversity of cadence. Thus, the TimeModAttn model is first pre-trained using synthetic light-curves. Then, a fine-tuning process is performed. The TimeModAttn model outperformed other Deep Learning models, based on Recurrent Neural Networks, in two scenarios: late-classification and early-classification. Also, the TimeModAttn model outperformed a Balanced Random Forest (BRF) classifier (trained with real data), increasing the balanced-$F_1$score from $\approx .525$ to $\approx .596$. When training the BRF with synthetic data, this model achieved similar performance to the TimeModAttn model proposed while still maintaining extra advantages. 3) We conducted interpretability experiments. High attention scores were obtained for observations earlier than and close to the SN brightness peaks. This also correlated with an early highly variability of the learned temporal modulation.

Submitted to AJ on 14-Jan-2022

References in corpus (22)

• The Zwicky Transient Facility: System Overview, Performance, and First Results
• The Automatic Learning for the Rapid Classification of Events (ALeRCE) Alert Broker
• Alert Classification for the ALeRCE Broker System: The Light Curve Classifier
• A recurrent neural network for classification of unevenly sampled variable stars
• Early light curves for Type Ia supernova explosion models
• Alert Classification for the ALeRCE Broker System: The Real-time Stamp Classifier
• Anomaly detection in the Zwicky Transient Facility DR3
• Scalable End-to-end Recurrent Neural Network for Variable star classification
• The High Cadence Transient Survey (HiTS) - I. Survey design and supernova shock breakout constraints
• PELICAN: deeP architecturE for the LIght Curve ANalysis
• A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients
• Searching for changing-state AGNs in massive datasets -- I: applying deep learning and anomaly detection techniques to find AGNs with anomalous variability behaviours
• On Neural Architectures for Astronomical Time-series Classification with Application to Variable Stars
• Deep modeling of quasar variability
• Imbalance Learning for Variable Star Classification
• Unsupervised machine learning for transient discovery in Deeper, Wider, Faster light curves
• Deep Neural Network Classifier for Variable Stars with Novelty Detection Capability
• Real-Time Detection of Anomalies in Large-Scale Transient Surveys
• Classifying Image Sequences of Astronomical Transients with Deep Neural Networks
• Classification of Periodic Variable Stars with Novel Cyclic-Permutation Invariant Neural Networks
• The effect of phased recurrent units in the classification of multiple catalogs of astronomical lightcurves
• A "Crib Sheet" for Supernova Events

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