8 citations · 8 across the 5 of their papers we have counts for
5 papers
The Galaxy Luminosity Functions in ASTRID: Predictions for LSST
Fatemeh Hafezianzadeh, Tianqing Zhang, Paul Rogozenski +6
We present validated and forward-modelled galaxy luminosity functions and photometric predictions for the Vera C. Rubin Observatory Legacy Survey of Space and Time using the ASTRID…
The ASTRID Simulation at z=0: From Massive Black Holes to Large-scale Structure
Yihao Zhou, Tiziana Di Matteo, Simeon Bird +7
We present the results for the cosmological simulation ASTRID. Hosting 0.33 trillion particles in a box of per side, ASTRID is one o…
An AI super-resolution field emulator for cosmological hydrodynamics: the Lyman-α forest
Fatemeh Hafezianzadeh, Xiaowen Zhang, Yueying Ni +4
We extend our super-resolution and emulation framework for cosmological dark matter simulations to include hydrodynamics. We present a two-stage deep learning model to emulate high…
Prediction of Star Formation Rates Using an Artificial Neural Network
Ashraf Ayubinia, Jong-hak Woo, Fatemeh Hafezianzadeh +2
In this study, we develop an artificial neural network to estimate the infrared (IR) luminosity and star formation rates (SFR) of galaxies. Our network is trained using 'true' IR l…
Exploring galactic properties with machine learning Predicting star formation, stellar mass, and metallicity from photometric data
F. Z. Zeraatgari, F. Hafezianzadeh, Y. -X. Zhang +2
Aims. We explore machine learning techniques to forecast star formation rate, stellar mass, and metallicity across galaxies with redshifts ranging from 0.01 to 0.3. Methods. Levera…