The growth of dark matter halos is to first-order the main driver of galaxy formation in our standard cosmological model. Yet, complex and highly non-linear baryonic processes take over at the centers of these halos, leading to the diversity of galaxies observed in the Universe today. The coupling between baryonic and dark matter physics is central

Astrochemical models are important tools to interpret observations of molecular and atomic species in different environments. However, these models are time-consuming, precluding a thorough exploration of the parameter space, leading to uncertainties and biased results. Using neural networks to simulate the behaviour of astrochemical models is a

We present a Bayesian inference method to characterize the dust emission properties using the well-known dust-${\rm H\,{\small I}}$ correlation in the diffuse interstellar medium at Planck frequencies $\nu \ge 217$ GHz. We use the Galactic ${\rm H\,{\small I}}$ map from the Galactic All-Sky Survey (GASS) as a template to trace the Galactic dust

The spectral signatures of optical absorbers, when combined with those of infrared molecules, play a critical role in constraining the cloud properties of exoplanet atmospheres. We aim to use optical transmission spectroscopy to confirm the tentative color signature previously observed by multiband photometry in the atmosphere of hot Jupiter HAT-P

Aims: Various galaxy merger detection methods have been applied to diverse datasets. However, it is difficult to understand how they compare. Our aim is to benchmark the relative performance of merger detection methods based on machine learning (ML). Methods: We explore six leading ML methods using three main datasets. The first dataset consists of