3 papers
astro-ph.IM2026
Emergent Denoising of SDSS Galaxy Spectra Through Unsupervised Deep Learning
Oliver Camilleri, Zahra Sharbaf, Ignacio Ferreras
Spectroscopy represents the ideal observational method to maximally extract information from galaxies regarding their star formation and chemical enrichment histories. However, abs…
astro-ph.GA2026
A novel data-driven approach to extract stellar population properties from galaxy spectra using absorption indices
Zahra Sharbaf, Ignacio Ferreras, Anna R. Gallazzi +4
In an era of highly complex machine learning methods that often are informative but not straightforward to interpret, Principal Component Analysis (PCA) offers a simple, easily int…
astro-ph.CO2025
Evaluating quenching in cosmological simulations of galaxy formation with spectral covariance in the optical window
Z. Sharbaf, I. Ferreras, A. Negri +4
Cosmological hydrodynamical simulations provide valuable insights on galaxy evolution when coupled with observational data. Comparisons with real galaxies are typically performed v…