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All Journal Spektra: Jurnal Fisika dan Aplikasinya
Jaelani, Anton Timur
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Aerospace Engineering Electrical & Electronics Engineering Energy Materials Science & Nanotechnology Physics

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Comparative Study of Activation Functions and Image Resolution on ResNet-34 for Spiral Galaxy Spin Classification Arwinata, Hafiz Indra; Kusuma, Sultan Hadi; Jaelani, Anton Timur
Spektra: Jurnal Fisika dan Aplikasinya Vol. 10 No. 3 (2025): SPEKTRA: Jurnal Fisika dan Aplikasinya, Volume 10 Issue 3, December 2025
Publisher : Program Studi Fisika Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/SPEKTRA.103.03

Abstract

This study investigates the application of the Residual Network (ResNet-34) architecture for classifying spiral galaxy spin directions, specifically focusing on the comparative performance of activation functions and cross-dataset generalizability using data derived from the Dark Energy Spectroscopic Instrument Legacy Survey (DESI LS) and the Hyper Suprime Cam Subaru Strategic Program (HSC-SSP) surveys. The methodology ensures robustness by training each model configuration across 10 independent runs. The results demonstrate the clear superiority of the Rectified Linear Unit (ReLU) over the Hyperbolic Tangent (Tanh); ReLU-based models achieved a mean peak accuracy of 94.7% and required only less than 60 epochs to converge, significantly faster than Tanh's 120 epochs. Crucially, we found that models trained on lower-resolution DESI LS images exhibited superior robustness and generalizability compared to high-resolution-trained models, suggesting that low-resolution training acts as effective implicit regularization. This research provides critical design recommendations for efficient machine learning pipelines, particularly for upcoming facilities like the 3.8-meter telescope at Timau National Observatory (TNO), ensuring model stability and transferability across diverse survey conditions.
Gravitational Lens Parameters Estimation at Intermediate Redshifts Using Convolutional Neural Networks Setiawan, Muhammad Doni; Jaelani, Anton Timur
Spektra: Jurnal Fisika dan Aplikasinya Vol. 10 No. 3 (2025): SPEKTRA: Jurnal Fisika dan Aplikasinya, Volume 10 Issue 3, December 2025
Publisher : Program Studi Fisika Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/SPEKTRA.103.05

Abstract

Strong gravitational lensing serves as a powerful astrophysical probe, enabling studies of dark matter, galaxy structure, and cosmological parameters. The number of strong gravitational lensing candidates at the galaxy scale is expected to reach O ~ 5 with ongoing and future wide-field galaxy surveys. Current modeling techniques largely rely on conventional fitting methods, such as least squares or maximum likelihood using Markov Chain Monte Carlo, which despite their effectiveness, are computationally expensive and require manual inspection. This motivates the development of faster yet accurate parameter estimation techniques. In this work, we construct a representative training dataset and develop an efficient Convolutional Neural Network to estimate lens parameters: the Einstein radius, axis ratio, and position angle. We utilize data from Public Data Release 3 of the Hyper Suprime-Cam Subaru Strategic Program, selecting lens galaxies in the range 0.3 ≤ z ≤ 0.9 based on the strong-lens probability distribution. We find that the choice of loss function and regularization strategy is critical. To enhance model generalization, we leverage SpatialDropout, which outperforms standard methods by addressing the spatial correlation inherent in convolutional features. Furthermore, prediction accuracy and convergence speed are strongly affected by the distribution of the training data, highlighting the importance of an appropriate loss function. Our optimized model demonstrates robust performance, achieving a Mean Absolute Error of 0.092 arcsec for the Einstein radius, providing a scalable framework for automated analysis in future wide-field surveys.
Co-Authors Arwinata, Hafiz Indra Kusuma, Sultan Hadi Setiawan, Muhammad Doni