We conduct a search for strong gravitational lenses in the Dark Energy Survey (DES) Year 6 imaging data. We implement a pre-trained Vision Transformer (ViT) for our machine learning (ML) architecture and adopt interactive machine learning to construct a training sample with multiple classes to address common types of false positives. Our ML model

A typical Bayesian inference on the values of some parameters of interest q from some data D involves running a Markov Chain (MC) to sample from the posterior p ( q , n ∣ D ) ∝ L ( D ∣ q , n ) p ( q ) p ( n ) , where n are some nuisance parameters with a separable prior. In some cases, the nuisance parameters are high-dimensional, and their prior p

In 2021 May the Dark Energy Spectroscopic Instrument (DESI) collaboration began a 5 yr spectroscopic redshift survey to produce a detailed map of the evolving three-dimensional structure of the Universe between z = 0 and z ≍ 4. DESI's principal scientific objectives are to place precise constraints on the equation of state of dark energy, the

We analyse high signal-to-noise ESPaDOnS/CFHT spectra of 20 very metal-poor (VMP) stars ([Fe/H] $\,\lt\,-2.0$) in the solar neighbourhood (within $\sim 2$ kpc), selected to be on planar orbits with maximum heights $\lesssim 4$ kpc. The sample comprises 11 stars on prograde and 9 on retrograde orbits, all with relatively high eccentricities (0.5─0.9