The ionizing photon escape fraction [Lyman continuum (LyC) f esc] of star-forming galaxies is the single greatest unknown in the reionization budget. Stochastic sightline effects prohibit the direct separation of LyC leakers from non-leakers at significant redshifts. Here we circumvent this uncertainty by inferring f esc using resolved (R > 4000)

We present a one-dimensional, local thermodynamic equilibrium homogeneous analysis of 132 stars observed at high resolution with ESPaDOnS. This represents the largest sample observed at high resolution (R ~ 40 000) from the Pristine survey. This sample is based on the first version of the Pristine catalogue and covers the full range of

We do not have a final answer to the question of why galaxies choose a particular internal mass distribution. Here we examine whether the distribution is set by thermodynamic equilibrium (TE). Traditionally, TE is discarded for a number of reasons including the inefficiency of two-body collisions to thermalize the mass distribution in a Hubble time

We quantify evolution in the cluster-scale stellar mass-halo mass (SMHM) relation's parameters using 2323 clusters and brightest central galaxies (BCGs) over the redshift range 0.03 ≤ z ≤ 0.60. The precision on the inferred SMHM parameters is improved by including the magnitude gap (m gap) between the BCG and fourth-brightest cluster member (M14)

Aims: We investigate the origin of the extraplanar diffuse ionized gas (eDIG) and its predominant ionization mechanisms in five nearby (17-46 Mpc) low-mass (10 9-10 10 M ⊙) edge-on disk galaxies: ESO 157-49, ESO 469-15, ESO 544-27, IC 217, and IC 1553. Methods: We acquired Multi Unit Spectroscopic Explorer (MUSE) integral field spectroscopy and

The chemical abundances of planet-hosting stars offer a glimpse into the composition of planet-forming environments. To further understand this connection, we make the first ever measurement of the correlation between planet occurrence and chemical abundances for ten different elements (C, Mg, Al, Si, S, K, Ca, Mn, Fe, and Ni). Leveraging data from