Improving Astronomical Time-series Classification via Data Augmentation with Generative Adversarial Networks

García-Jara, Germán; Protopapas, Pavlos; Estévez, P. A.

doi:10.3847/1538-4357/ac6f5a

Cited by 9 publications

(6 citation statements)

References 40 publications

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“…There have been certain areas where the application of ML techniques has produced a remarkable result, especially when it focused on providing faster results as an alternative to existing methods (Ball 2011;Sipőcz et al 2020). There is plenty of excellent literature available on a wide range of topics of ML application in astronomy and astrophysics (García-Jara et al 2022;Gheller & Vazza 2022;Li et al 2022;Sheng et al 2022). In this paper, we incorporate a method from the ML domain known as autoencoder (Rumelhart et al 1986), which is based on a feed-forward mechanism, to generate light curves.…”

Section: Introductionmentioning

confidence: 99%

Rapid Generation of Kilonova Light Curves Using Conditional Variational Autoencoder

Saha,

Williams,

Datrier

et al. 2024

ApJ

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The discovery of the optical counterpart, along with the gravitational waves (GWs) from GW170817, of the first binary neutron star merger has opened up a new era for multimessenger astrophysics. Combining the GW data with the optical counterpart, also known as AT 2017gfo and classified as a kilonova, has revealed the nature of compact binary merging systems by extracting enriched information about the total binary mass, the mass ratio, the system geometry, and the equation of state. Even though the detection of kilonovae has brought about a revolution in the domain of multimessenger astronomy, there has been only one kilonova from a GW-detected binary neutron star merger event confirmed so far, and this limits the exact understanding of the origin and propagation of the kilonova. Here, we use a conditional variational autoencoder (CVAE) trained on light-curve data from two kilonova models having different temporal lengths, and consequently, generate kilonova light curves rapidly based on physical parameters of our choice with good accuracy. Once the CVAE is trained, the timescale for light-curve generation is of the order of a few milliseconds, which is a speedup of the generation of light curves by 1000 times as compared to the simulation. The mean squared error between the generated and original light curves is typically 0.015 with a maximum of 0.08 for each set of considered physical parameters, while having a maximum of ≈0.6 error across the whole parameter space. Hence, implementing this technique provides fast and reliably accurate results.

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Section: Introductionmentioning

confidence: 99%

Rapid Generation of Kilonova Light Curves Using Conditional Variational Autoencoder

Saha,

Williams,

Datrier

et al. 2024

ApJ

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“…In astronomy, GANs also demonstrate great potential in spectral denoising and data augmentation for improved astronomical object classification. In particular, they are employed to generate synthetic light curves from variable stars, thus enhancing the classification accuracy when training with synthetic data and testing with real data (García-Jara et al 2022). A feed-forward neural network called Spectra Generative Adversarial Nets (Spectra-GANs) outperforms traditional methods such as PCA, wavelet analysis, and restricted Boltzmann machines in solving spectral denoising challenges (Wu et al 2020).…”

Section: Introductionmentioning

confidence: 99%

StellarGAN: Classifying Stellar Spectra with Generative Adversarial Networks in SDSS and APOGEE Sky Surveys

Liu,

Cao,

et al. 2024

ApJS

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Extracting precise stellar labels is crucial for large spectroscopic surveys like the Sloan Digital Sky Survey (SDSS) and APOGEE. In this paper, we report the newest implementation of StellarGAN, a data-driven method based on generative adversarial networks (GANs). Using 1D operators like convolution, the 2D GAN is modified into StellarGAN. This allows it to learn the relevant features of 1D stellar spectra without needing labels for specific stellar types. We test the performance of StellarGAN on different stellar spectra trained on SDSS and APOGEE data sets. Our result reveals that StellarGAN attains the highest overall F1-score on SDSS data sets (F1-score = 0.82, 0.77, 0.74, 0.53, 0.51, 0.61, and 0.55, for O-type, B-type, A-type, F-type, G-type, K-type, and M-type stars) when the signal-to-noise ratio (S/N) is low (90% of the spectra have an S/N < 50), with 1% of labeled spectra used for training. Using 50% of the labeled spectral data for training, StellarGAN consistently demonstrates performance that surpasses or is comparable to that of other data-driven models, as evidenced by the F1-scores of 0.92, 0.77, 0.77, 0.84, 0.84, 0.80, and 0.67. In the case of APOGEE (90% of the spectra have an S/N < 500), our method is also superior regarding its comprehensive performance (F1-score = 0.53, 0.60, 0.56, 0.56, and 0.78 for A-type, F-type, G-type, K-type, and M-type stars) with 1% of labeled spectra for training, manifesting its learning ability out of a limited number of labeled spectra. Our proposed method is also applicable to other types of data that need to be classified (such as gravitational-wave signals, light curves, etc.).

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“…ML can be used to direct follow-up observations based on the latest survey data (e.g., Sravan et al 2023). ML techniques have also been applied to large surveys to classify different kinds of sources, including microlensing events (e.g., Godines et al 2019), transients (e.g., Stachie et al 2020;Gomez et al 2023;Rehemtulla & Miller 2023), and variables (e.g., Richards et al 2011;García-Jara et al 2022;Mistry et al 2022).…”

Section: Introductionmentioning

confidence: 99%

The ZTF Source Classification Project. III. A Catalog of Variable Sources

Healy,

Coughlin,

Mahabal

et al. 2024

ApJS

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The classification of variable objects provides insight into a wide variety of astrophysics ranging from stellar interiors to galactic nuclei. The Zwicky Transient Facility (ZTF) provides time-series observations that record the variability of more than a billion sources. The scale of these data necessitates automated approaches to make a thorough analysis. Building on previous work, this paper reports the results of the ZTF Source Classification Project (SCoPe), which trains neural network and XGBoost (XGB) machine-learning (ML) algorithms to perform dichotomous classification of variable ZTF sources using a manually constructed training set containing 170,632 light curves. We find that several classifiers achieve high precision and recall scores, suggesting the reliability of their predictions for 209,991,147 light curves across 77 ZTF fields. We also identify the most important features for XGB classification and compare the performance of the two ML algorithms, finding a pattern of higher precision among XGB classifiers. The resulting classification catalog is available to the public, and the software developed for SCoPe is open source and adaptable to future time-domain surveys.

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Improving Astronomical Time-series Classification via Data Augmentation with Generative Adversarial Networks

Cited by 9 publications

References 40 publications

Rapid Generation of Kilonova Light Curves Using Conditional Variational Autoencoder

Rapid Generation of Kilonova Light Curves Using Conditional Variational Autoencoder

StellarGAN: Classifying Stellar Spectra with Generative Adversarial Networks in SDSS and APOGEE Sky Surveys

The ZTF Source Classification Project. III. A Catalog of Variable Sources

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