Using transfer learning to detect galaxy mergers

Ackermann, Sandro, Schawinski, Kevin, Zhang, Ce, Weigel, Anna K, Turp, M Dennis·2018·Monthly Notices of the Royal Astronomical Society
DOI 10.1093/mnras/sty1398

Cited by (136)

Items 101–136 of 136

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  4. A Machine-learning Approach to Enhancing eROSITA Observations
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  5. Domain adaptation techniques for improved cross-domain study of galaxy mergers
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  6. Classifying merger stages with adaptive deep learning and cosmological hydrodynamical simulations
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  7. The major merger–active galactic nucleus connection up to cosmic noon
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  8. Galaxy merger challenge: A comparison study between machine learning-based detection methods
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  9. First search for high-energy neutrino emission from galaxy mergers
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  10. A self-supervised, physics-aware, Bayesian neural network architecture for modelling galaxy emission-line kinematics
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  11. Parameter measurement based on photometric images
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  12. Automatic Machine Learning Framework to Study Morphological Parameters of AGN Host Galaxies within z < 1.4 in the Hyper Supreme-Cam Wide Survey
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  13. Spectral and Morphological Classification of Celestial Objects using Physics Informed Machine Learning
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  14. Identifying astrophysical anomalies in 99.6 million source cutouts from the Hubble legacy archive using AnomalyMatch
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  15. Identifying Mergers in the Legacy Surveys with Few-shot Learning
    Shoulin Wei, Xiang Song, Zhijian Zhang, Bo Liang, Wei Dai, Wei Lu, Junxi Tao · 2024 · The Astrophysical Journal Supplement Series
  16. Deep learning for galaxy mergers in the galaxy main sequence
    William Pearson, Lingyu Wang, James W. Trayford, Carlo E. Petrillo, Floris F. S. van der Tak · 2019 · Proceedings of the International Astronomical Union
  17. Sim-to-Real Domain Adaptation For High Energy Physics
    M. Baalouch, M. Defurne, Jean-Philippe Poli, Noëlie Cherrier · 2019 · arXiv (Cornell University)
  18. AutoSource: Radio-astronomical source-finding with convolutional autoencoders
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  19. CInC Flow: Characterizable Invertible 3x3 Convolution
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  20. Introduction
    Kana Moriwaki · 2022 · Springer theses
  21. Estimating dynamical parameters of two interacting galaxies using deep learning
    Adarsh Mahor, Janvita Reddy, Amitesh Singh, Shashwat Singh · 2023 · Monthly Notices of the Royal Astronomical Society
  22. Introduction
    Jindong Wang, Yiqiang Chen · 2022 · Machine learning
  23. Determining the time before or after a galaxy merger event
    William Pearson, Vicente Rodríguez-Gómez, Sandor Kruk, Berta Margalef-Bentabol · 2024 · Springer Link (Chiba Institute of Technology)
  24. MixerFlow: MLP-Mixer Meets Normalising Flows
    Eshant English, Matthias Kirchler, Christoph Lippert · 2024 · Lecture notes in computer science
  25. Improvement of Classification Results of Convolutional Neural Networks Using Various Gan-Based Augmentation Techniques
    Lenka Kališková, Peter Butka · 2024 · Acta Electrotechnica et Informatica
  26. The Effect of Environment in Galaxy Merger Incidence
    Kiyoaki Christopher Omori · 2024 · Springer theses
  27. Search for spatial coincidences between galaxy mergers and Fermi-LAT 4FGL-DR4 sources
    Siddhant Manna, S. Desai · 2025 · Journal of High Energy Astrophysics
  28. Exploring celestial classification: Astrophysical features-guided machine learning for spectral and morphological analysis
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  29. Convolutional Neural Networks for Classifying Galaxy Mergers: Can Faint Tidal Features Aid in Classifying Mergers?
    Yeonkyung Lee, Hyunmi Song, Jihye Shin, Sungryong Hong, Jaehyun Lee, Kyungwon Chun · 2026 · The Astrophysical Journal
  30. Galaxy Mergers Classification Using CNNs Trained on Sérsic Models, Residuals, and Raw Images
    D. M. Chudy, William Pearson, L. E. Suelves, Berta Margalef-Bentabol, A. Pollo, Lingyu Wang, Vicente Rodríguez-Gómez, Antonio La Marca · 2026 · The Astrophysical Journal
  31. Transfer learning of convolutional neural networks for texture synthesis and visual recognition in artistic images
    Nicolas Gonthier · 2021
  32. Probing galactic growth with low surface brightness (LSB) data from deep imaging surveys
    Elisabeth Sola · 2023
  33. Morpho-kinematic study of intermediate-mass galaxies : revealing the impact of galaxy interaction 5 Gyrs ago.
    Marcella González Paillalef · 2024
  34. A Hybrid Ensemble and Deep Learning Framework for Detecting High-redshift Ly α Blobs in Broadband Surveys
    Daniel Godines, Moire K. M. Prescott · 2026 · Publications of the Astronomical Society of the Pacific
  35. Transfer Learning for Constellation Recognition: A Comparative Study of Lightweight CNNs on Small-Scale Astronomical Image Data
    Kelzang Yuden, Ziaullah Momand, Pornchai Mongkolnam, Debajyoti Pal · 2026
  36. Performance of morphological classifiers for galaxy mergers compared to current machine learning methods
    Aidan P. Cotter, William Pearson, Subhrata Dey, Berta Margalef-Bentabol, Alejandro Guzmán-Ortega, Vicente Rodríguez-Gómez · 2026 · Astronomy and Astrophysics