Integrating Temporal and Spectral Features of Astronomical Data Using Wavelet Analysis for Source Classification
T. N. Ukwatta, P. R. Wozniak

TL;DR
This paper introduces a novel method that combines temporal and spectral features of astronomical data into an image, then applies wavelet analysis for feature extraction and classification, demonstrated on gamma-ray burst data.
Contribution
The paper presents a new approach that integrates light curves and spectra into an image and uses wavelet analysis for feature extraction, improving classification of astronomical sources.
Findings
Effective classification of gamma-ray bursts into high- and low-redshift groups.
Wavelet-based features outperform traditional separate analysis methods.
Method enhances sensitivity to unknown properties of astronomical signals.
Abstract
Temporal and spectral information extracted from a stream of photons received from astronomical sources is the foundation on which we build understanding of various objects and processes in the Universe. Typically astronomers fit a number of models separately to light curves and spectra to extract relevant features. These features are then used to classify, identify, and understand the nature of the sources. However, these feature extraction methods may not be optimally sensitive to unknown properties of light curves and spectra. One can use the raw light curves and spectra as features to train classifiers, but this typically increases the dimensionality of the problem, often by several orders of magnitude. We overcome this problem by integrating light curves and spectra to create an abstract image and using wavelet analysis to extract important features from the image. Such features…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
