132 citations · 157 across the 7 of their papers we have counts for
4 papers · 1 filter
Machine learning classification of baseband data of CHIME FRBs
Mohanraj Madheshwaran, Tetsuya Hashimoto, Tomotsugu Goto +5
Fast Radio Bursts (FRBs) are bright millisecond radio pulses. Their origin is still unknown in the field of astronomy. A notable distinction among FRBs is that some sources repeat,…
Observationally derived change in the star formation rate as mergers progress
W. J. Pearson, L. Wang, V. Rodriguez-Gomez +2
Galaxy mergers can change the rate at which stars are formed. We can trace when these changes occur in simulations of galaxy mergers. However, for observed galaxies we do not know…
Galaxy mergers classification using CNNs trained on Sérsic models, residuals and raw images
D. M. Chudy, W. J. Pearson, A. Pollo +5
Galaxy mergers are crucial for understanding galaxy evolution, and with large upcoming datasets, automated methods such as Convolutional Neural Networks (CNNs) are essential for ef…
Morphological Classification of Galaxies Through Structural and Star Formation Parameters Using Machine Learning
G. Aguilar-Argüello, G. Fuentes-Pineda, H. M. Hernández-Toledo +9
We employ the XGBoost machine learning (ML) method for the morphological classification of galaxies into two (early-type, late-type) and five (E, S0--S0a, Sa--Sb, Sbc--Scd, Sd--Irr…