most citedA big step forward with SHARP: spatially resolved stellar population properties in passive galaxies at z > 1.5

1 citations · 2 across the 4 of their papers we have counts for

collaborators

20 papers

astro-ph.IM2026

WST -- Wide-field Spectroscopic Telescope: The Next Leap in Wide-field Spectroscopy

Roland Bacon, Olga Bellido, Philippe Dierickx +53

The Wide-field Spectroscopic Telescope (WST) is a concept for a dedicated 12-m spectroscopic survey facility designed to address some of the most important questions in astrophysic…

astro-ph.GA20261 cited

A big step forward with SHARP: spatially resolved stellar population properties in passive galaxies at z > 1.5

A. Gargiulo, C. Mancini, F. R. Ditrani +6

Understanding when and how massive quiescent galaxies (log(M*/Msun) > 10.5) assembled their stellar mass and quenched remains a central challenge in galaxy evolution. Spatially res…

astro-ph.GA2026

Probing IMF Variations in High-Redshift Early-Type Galaxies with SHARP

F. La Barbera, G. De Lucia, F. Ditrani +9

The stellar initial mass function (IMF), which describes the distribution of stellar masses at birth, is a fundamental ingredient in shaping galaxy evolution. Recent observations i…

astro-ph.GA20261 cited

A SHARP Look at Quenching and Bulge-Disk Growth in Massive Galaxies at Cosmic Noon

Chiara Mancini, Adriana Gargiulo, Alvio Renzini +11

The physical mechanisms that quench star formation in massive galaxies remain poorly understood. At cosmic noon (1<z<3), when star formation and AGN activity peak, galaxies rapidly…

astro-ph.IM2026

SHARP -- A spectrograph proposal to fully exploit ELT capabilities and look beyond JWST

P. Saracco, P. Conconi, C. Arcidiacono +47

The Extremely Large Telescopes (ELTs), with their large apertures and cutting-edge Multi-Conjugate Adaptive Optics (MCAO) systems, promise to deliver data that is both sharper and…

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…