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Temporal Stamp Classifier: Classifying Short Sequences of Astronomical Alerts

Daniel Neira O., Pablo A. Estévez, Francisco Förster·May 23, 2024

Summary

The paper introduces the Temporal Stamp Classifier, a deep learning model for classifying astronomical objects (AGN, SNe, VS) in ZTF alerts. The model combines CNNs and RNNs to process irregular time-series data, achieving an accuracy of around 98% with 2-5 detections. Enhancements include random rotations and varying image sizes, which improve test accuracy by 1.46%. The study compares different recurrent models and finds Conv1D to be a practical choice due to its speed and comparable accuracy. The model's performance is competitive with light curve classifiers and highlights the importance of metadata and image processing for multi-detection events. Future work involves refining categories, exploring time modulation, and deep attention models for enhanced outlier detection.
Introduction
Background
[ZTF ( Zwicky Transient Facility) and its importance]
[Challenges in classifying irregular time-series data]
Objective
[Development of Temporal Stamp Classifier]
[ Aim: High accuracy and multi-detection capability]
[Enhancements: Random rotations and varying image sizes]
Method
Data Collection
[ZTF alert data source]
[Inclusion of metadata and image data]
Data Preprocessing
[Image resizing and augmentation techniques]
[Temporal feature extraction from time-series data]
Model Architecture
Convolutional Neural Networks (CNNs)
[Role in extracting spatial features]
Recurrent Neural Networks (RNNs) - Conv1D
[Choice and comparison with other RNN models]
[Advantages: speed and accuracy]
Model Implementation
[Integration of CNNs and RNNs]
[Enhancements: random rotations and varying image sizes]
Performance Evaluation
[Accuracy of around 98% with 2-5 detections]
[Accuracy improvement with enhancements]
Comparison with Light Curve Classifiers
[Competitiveness and insights]
Future Research Directions
[Refining categories for better classification]
[Exploring time modulation in classification]
[Deep attention models for outlier detection]
Basic info
papers
instrumentation and methods for astrophysics
artificial intelligence
Advanced features
Insights
How do random rotations and varying image sizes impact the model's performance?
Why is Conv1D chosen as the recurrent model in the study?
What is the Temporal Stamp Classifier used for?
What is the accuracy of the Temporal Stamp Classifier in classifying astronomical objects?
Mind map
[ZTF ( Zwicky Transient Facility) and its importance]
[Challenges in classifying irregular time-series data]
Background
[Development of Temporal Stamp Classifier]
[ Aim: High accuracy and multi-detection capability]
[Enhancements: Random rotations and varying image sizes]
Objective
Introduction
[ZTF alert data source]
[Inclusion of metadata and image data]
Data Collection
[Image resizing and augmentation techniques]
[Temporal feature extraction from time-series data]
Data Preprocessing
[Role in extracting spatial features]
Convolutional Neural Networks (CNNs)
[Choice and comparison with other RNN models]
[Advantages: speed and accuracy]
Recurrent Neural Networks (RNNs) - Conv1D
Model Architecture
[Integration of CNNs and RNNs]
[Enhancements: random rotations and varying image sizes]
Model Implementation
[Accuracy of around 98% with 2-5 detections]
[Accuracy improvement with enhancements]
Performance Evaluation
[Competitiveness and insights]
Comparison with Light Curve Classifiers
[Refining categories for better classification]
[Exploring time modulation in classification]
[Deep attention models for outlier detection]
Future Research Directions
Method
Outline
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