From the 1 of 7 linked papers with an AI index.
7 papers
CatBoost versus Spectral Energy Distribution-Fitting: Estimating Galaxy Properties under Controlled Photometric Incompleteness
Vahid Asadi, Hosein Haghi, Akram Hasani Zonoozi
Estimating galaxy physical parameters from photometric data is fundamentally challenged by missing measurements that are endemic to astronomical surveys. Using a mock catalog from…
COSMOS2025: A Machine Learning Census of Massive Quiescent Galaxies at
Vahid Asadi, Hosein Haghi, Akram Hasani Zonoozi
The paper introduces a machine‑learning classifier (CatBoost) trained on mock photometry from semi‑analytic models to identify massive quiescent galaxies at redshifts 2.5–5 in the…
COSMOS2025: Machine Learning Classification of Early- and Late-type Galaxies at 0 < z < 3
Vahid Asadi, Najmeh Sheikhi
We present a fast, interpretable machine learning framework to classify early- and late-type galaxies in the COSMOS2025 catalog at , without relying on image-based train…
Machine Learning vs. Spectral Energy Distribution Fitting: A Comparative Analysis of Accuracy in Stellar Mass Estimation
Vahid Asadi, Akram Hasani Zonoozi, Hosein Haghi
Traditional spectral energy distribution (SED)-fitting methods for stellar mass estimation face persistent challenges including systematic biases and computational constraints. We…
Machine Learning Classification of COSMOS2020 Galaxies: Quiescent vs. Star-Forming
Vahid Asadi, Nima Chartab, Akram Hasani Zonoozi +4
Accurately distinguishing between quiescent and star-forming galaxies is essential for understanding galaxy evolution. Traditional methods, such as spectral energy distribution (SE…
Semi-supervised classification of stars, galaxies and quasars using K-means and random-forest approaches
Vahid Asadi, Hosein Haghi, Akram Hasani Zonoozi
Classifying stars, galaxies, and quasars is essential for understanding cosmic structure and evolution; however, the vast data from modern surveys make manual classification imprac…