80 citations · 164 across the 12 of their papers we have counts for
12 papers
Exploring the Viability of Fisher Discriminants in Galaxy Morphology Classification
Sazatul Nadhilah Zakaria, Santtosh Muniyandy, John Y. H. Soo
One of the major challenges in astronomy involves accurately classifying galaxies, particularly distinguishing between different galaxy types. While many complex algorithms have sh…
Classification of Spiral Galaxies by Spiral Arm Number using Convolutional Neural Network
Ming Wei Lee, John Y. H. Soo, Syarawi M. H. Sharoni
The structural information of spiral galaxies such as the spiral arm number, offer valuable insights into their formation processes and physical roles in galaxy evolution. We devel…
ANNZ+: an enhanced photometric redshift estimation algorithm with applications on the PAU Survey
Imdad Mahmud Pathi, John Y. H. Soo, Mao Jie Wee +16
ANNZ is a fast and simple algorithm which utilises artificial neural networks (ANNs), it was known as one of the pioneers of machine learning approaches to photometric redshift est…
CNUCTRAN: A program for computing final nuclide concentrations using a direct simulation approach
K. A. Bala, M. R Omar, John Y. H. Soon +1
It is essential to precisely determine the evolving concentrations of radioactive nuclides within transmutation problems. It is also a crucial aspect of nuclear physics with widesp…
Characterising Solar Magnetic Reconnection in Confined and Eruptive Flares
Kanniah Balamuralikrishna, John Y. H. Soo, Norhaslinda Mohamed Tahrin +1
Magnetic reconnection is a fundamental mechanism through which energy stored in magnetic fields is released explosively on a massive scale, they could be presented as eruptive or c…
Machine learning applications in astrophysics: Photometric redshift estimation
John Y. H. Soo, Ishaq Y. K. Alshuaili, Imdad Mahmud Pathi
Machine learning has rose to become an important research tool in the past decade, its application has been expanded to almost if not all disciplines known to mankind. Particularly…
Improving Photometric Redshifts by Merging Probability Density Functions from Template-Based and Machine Learning Algorithms
Ishaq Y. K. Alshuaili, John Y. H. Soo, Mohd Zubir Mat Jafri +1
This study aims to improve the photometric redshifts (photo-s) of galaxies by integrating two contemporary methods: template-fitting and machine learning. Finding the synergy be…
The PAU Survey: a new constraint on galaxy formation models using the observed colour redshift relation
G. Manzoni, C. M. Baugh, P. Norberg +27
We use the GALFORM semi-analytical galaxy formation model implemented in the Planck Millennium N-body simulation to build a mock galaxy catalogue on an observer's past lightcone. T…
Characterising Improvements in Photometric Redshift Probability Density Functions with Galaxy Morphology
John Y. H. Soo, Benjamin Joachimi
In this work, we studied the impact of galaxy morphology on photometric redshift (photo-) probability density functions (PDFs). By including galaxy morphological parameters like…
The PAU Survey: narrowband photometric redshifts using Gaussian processes
John Y. H. Soo, Benjamin Joachimi, Martin Eriksen +16
We study the performance of the hybrid template-machine-learning photometric redshift (photo-) algorithm Delight, which uses Gaussian processes, on a subset of the early data re…
Evaluation of probabilistic photometric redshift estimation approaches for The Rubin Observatory Legacy Survey of Space and Time (LSST)
S. J. Schmidt, A. I. Malz, J. Y. H. Soo +24
Many scientific investigations of photometric galaxy surveys require redshift estimates, whose uncertainty properties are best encapsulated by photometric redshift (photo-z) poster…
Morpho-z: improving photometric redshifts with galaxy morphology
John Y. H. Soo, Bruno Moraes, Benjamin Joachimi +10
We conduct a comprehensive study of the effects of incorporating galaxy morphology information in photometric redshift estimation. Using machine learning methods, we assess the cha…