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Abstract:

We report observations of a powerful ionized gas outflow in the z = 4.1 luminous radio galaxy TNJ1338-1942 hosting an obscured quasar using the Near Infrared Spectrograph (NIRSpec) on board JWST. We spatially resolve a large-scale (∼15 kpc) outflow and measure outflow rates. The outflowing gas shows velocities exceeding 900 km s−1 and broad line profiles with widths exceeding 1200 km s−1 located at an ∼10 kpc projected distance from the central nucleus. The outflowing nebula spatially overlaps with the brightest radio lobe, indicating that the powerful radio jets are responsible for the outflow kinematics. The gas is possibly ionized by the obscured quasar with a contribution from shocks induced by the jets. The mass outflow rate map shows that the region with the broadest line profiles exhibits the strongest outflow rates. The total mass outflow rate is ∼500 M ⊙ yr−1, and the mass loading factor is ∼1, indicating that a significant part of the gas is displaced outwards by the outflow. Our hypothesis is that the overpressured shocked jet fluid expands laterally to create an expanding ellipsoidal “cocoon” that causes the surrounding gas to accelerate outwards. The total kinetic energy injected by the radio jet is about 3 orders of magnitude larger than the energy in the outflowing ionized gas. This implies that kinetic energy must be transferred inefficiently from the jets to the gas. The bulk of the deposited energy possibly lies in the form of hot X-ray-emitting gas.

Abstract:

Constraining the escape fraction of Lyman Continuum (LyC) photons from high-redshift galaxies is crucial to understanding reionization. Recent observations have demonstrated that various characteristics of the Ly α emission line correlate with the inferred LyC escape fraction (f LyC esc ) of low-redshift galaxies. Using a data set of 9600 mock Ly α spectra of star-forming galaxies at 4.64 ≤ z ≤ 6 from the SPHINX20 cosmological radiation hydrodynamical simulation, we study the physics controlling the escape of Ly α and LyC photons. We find that our mock Ly α observations are representative of high-redshift observations and that typical observational methods tend to overpredict the Ly α escape fraction (f Ly α esc ) by as much as 2 dex. We investigate the correlations between f LyC esc and f Ly α esc , Ly α equivalent width (Wλ(Ly α)), peak separation (vsep), central escape fraction (fcen), and red peak asymmetry (Ared f ). We find that f Ly α esc and fcen are good diagnostics for LyC leakage, selecting for galaxies with lower neutral gas densities and less UV attenuation that have recently experienced supernova feedback. In contrast, Wλ(Ly α) and vsep are found to be necessary but insufficient diagnostics, while Ared f carries little information. Finally, we use stacks of Ly α, H α, and F150W mock surface brightness profiles to find that galaxies with high f LyC esc tend to have less extended Ly α and F150W haloes but larger H α haloes than their non-leaking counterparts. This confirms that Ly α spectral profiles and surface brightness morphology can be used to better understand the escape of LyC photons from galaxies during the epoch of reionization.

Abstract:

<jats:p>This paper presents the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inference in astrophysics and cosmology. The pipeline includes software for implementing various neural architectures, training schema, priors, and density estimators in a manner easily adaptable to any research workflow. It includes comprehensive validation metrics to assess posterior estimate coverage, enhancing the reliability of inferred results. Additionally, the pipeline is easily parallelizable, designed for efficient exploration of modeling hyperparameters. To demonstrate its capabilities, we present real applications across a range of astrophysics and cosmology problems, such as: estimating galaxy cluster masses from X-ray photometry; inferring cosmology from matter power spectra and halo point clouds; characterising progenitors in gravitational wave signals; capturing physical dust parameters from galaxy colors and luminosities; and establishing properties of semi-analytic models of galaxy formation. We also include exhaustive benchmarking and comparisons of all implemented methods as well as discussions about the challenges and pitfalls of ML inference in astronomical sciences. All code and examples are made publicly available at https://github.com/maho3/ltu-ili.</jats:p>