Standard evolutionary synthesis models rely on the assumption of a fully sampled stellar initial mass function (IMF). Under this assumption, the age, chemical composition, and IMF uniquely define the predicted absorption spectra. However, with current instrumentation pushing observations towards higher spatial resolutions and lower surface

The development of evolutionary stellar population models is central to interpreting observations of galaxies in terms of astrophysical quantities. Stellar population models must therefore be both accurate and compatible with inversion algorithms in order to extract meaningful information from the observed data. Here we present FASTAR, a fully

Context. The nuclear bulge of the Milky Way harbors stellar populations that provide crucial insights into galaxy formation processes and serve as a nearby analog for understanding bulge formation in external galaxies. However, detailed studies of this region are severely hampered by extreme and highly variable interstellar extinction, which

A primary target of the Euclid space mission is to constrain early-universe physics by searching for deviations from a primordial Gaussian random field. A significant detection of primordial non-Gaussianity would rule out the simplest models of cosmic inflation and transform our understanding of the origin of the Universe. This paper forecasts how

We present a 'multiple-pass' data-reduction tool designed for Euclid, based on software developed for the Hubble Space Telescope (HST), which improves the astrometric and photometric precision for faint sources and in crowded fields. In this work, we apply it to Euclid Early Release Observations of the Galactic globular cluster NGC 6397. By

We report the discovery of 31 new high-z quasars in the redshift range 6.6 < z < 7.8. These quasars were selected from approximately 3000 deg 2 of sky covered during the first 1.5 years of the Euclid Wide Survey, representing the initial results of the Euclid high-z quasar search. Our candidate selection employed multiple machine-learning and