Context. Detection and characterisation of Jovian analogues in precision radial velocity (RV) measurements is gaining momentum due to the constantly increasing observational baseline of Doppler surveys. The occurrence rate of Jovian-mass exoplanets is crucial to understanding the architecture of planetary systems. However, long-period RV signals in

Context. The James Webb Space Telescope (JWST) enables the identification of quiescent galaxies out to early epochs, offering a transformative view of their evolution. However, the photometric selection of quiescent galaxy candidates (QGCs) and the derivation of their key physical quantities, such as stellar masses (M ★) and dust attenuation

The tens of millions of spectra being captured by the Dark Energy Spectroscopic Instrument (DESI) provide tremendous discovery potential. In this work we show how Machine Learning, in particular Variational Autoencoders (VAE), can detect anomalies in a sample of approximately 200 000 DESI spectra comprising galaxies, quasars and stars. We

LTT 9779 b is an ultra-hot Neptune (R p ≍ 4.7 R ⊕, M p ≍ 29 M ⊕) orbiting its Sun-like host star in just 19 hr, placing it deep within the "hot-Neptune desert," where Neptunian planets are seldom found. We present new James Webb Space Telescope NIRSpec G395H phase curve observations that probe its atmospheric composition in unprecedented detail. At

I present ExoIris, a user-friendly Python package for exoplanet transmission and emission spectroscopy. Unlike existing tools, ExoIris models two-dimensional spectrophotometric transit time series directly and supports the joint analysis of multiple datasets obtained with different instruments and at different epochs, as well as modeling stellar