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Initially, the group divided into two camps: the doers, fighting the idea that they were \"trapped and dead,\" and the waiters, who the doers thought were afraid and \"paralyzed by the memory of running for their lives with the mountain falling around them.\"

We present a simple, differentiable method for predicting emission line strengths from rest-frame optical continua using an empirically-determined mapping. Extensive work has been done to develop mock galaxy catalogues that include robust predictions for galaxy photometry, but reliably predicting the strengths of emission lines has remained challenging. Our new mapping is a simple neural network implemented using the JAX Python automatic differentiation library. It is trained on Dark Energy Spectroscopic Instrument Early Release data to predict the equivalent widths (EWs) of the eight brightest optical emission lines (including H\\(\\alpha\\), H\\(\\beta\\), [O II], and [O III]) from a galaxy's rest-frame optical continuum. The predicted EW distributions are consistent with the observed ones when noise is accounted for, and we find Spearman's rank correlation coefficient \\(\\rho_s > 0.87\\) between predictions and observations for most lines. Using a non-linear dimensionality reduction technique (UMAP), we show that this is true for galaxies across the full range of observed spectral energy distributions. In addition, we find that adding measurement uncertainties to the predicted line strengths is essential for reproducing the distribution of observed line-ratios in the BPT diagram. Our trained network can easily be incorporated into a differentiable stellar population synthesis pipeline without hindering differentiability or scalability with GPUs. A synthetic catalogue generated with such a pipeline can be used to characterise and account for biases in the spectroscopic training sets used for training and calibration of photo-\\(z\\)'s, improving the modelling of systematic incompleteness for the Rubin Observatory LSST and other surveys.

The full-shape correlations of the Lyman alpha (Ly\\(\\)) forest contain a wealth of cosmological information through the Alcock-Paczy\\'nski effect. However, these measurements are challenging to model without robustly testing and verifying the theoretical framework used for analyzing them. Here, we leverage the accuracy and volume of the \\(N\\)-body simulation suite AbacusSummit to generate high-resolution Ly\\(\\) skewers and quasi-stellar object (QSO) catalogs. One of the main goals of our mocks is to aid in the full-shape Ly\\(\\) analysis planned by the Dark Energy Spectroscopic Instrument (DESI) team. We provide optical depth skewers for six of the fiducial cosmology base-resolution simulations (\\(L_ box = 2\\,h^-1 Gpc\\), \\(N = 6912^3\\)) at \\(z = 2.5\\). We adopt a simple recipe based on the Fluctuating Gunn-Peterson Approximation (FGPA) for constructing these skewers from the matter density in an \\(N\\)-body simulation and calibrate it against the 1D and 3D Ly\\(\\) power spectra extracted from the hydrodynamical simulation IllustrisTNG (TNG; \\(L_ box = 205\\,h^-1 Mpc\\), \\(N = 2500^3\\)). As an important application, we study the non-linear broadening of the baryon acoustic oscillation (BAO) peak and show the cross-correlation between DESI-like QSOs and our Ly\\(\\) forest skewers. We find differences on small scales between the Kaiser approximation prediction and our mock measurements of the Ly $\\alpha$ $\\times\\(QSO cross-correlation, which would be important to account for in upcoming analyses. The \\textsc{AbacusSummit} Ly\\)\\alpha\\( forest mocks open up the possibility for improved modelling of cross correlations between Ly\\)\\alpha\\( and cosmic microwave background (CMB) lensing and Ly\\)\\alpha\\( and QSOs, and for forecasts of the 3-point Ly\\)\\alpha$correlation function. Our catalogues and skewers are publicly available on Globus.

We characterise the selection cuts and clustering properties of a magnitude-limited sample of bright galaxies that is part of the Bright Galaxy Survey (BGS) of the Dark Energy Spectroscopic Instrument (DESI) using the ninth data release of the Legacy Imaging Surveys (DR9). We describe changes in the DR9 selection compared to the DR8 one as explored in Ruiz-Macias et al. (2021). We also compare the DR9 selection in three distinct regions: BASS/MzLS in the north Galactic Cap (NGC), DECaLS in the NGC, and DECaLS in the south Galactic Cap (SGC). We investigate the systematics associated with the selection and assess its completeness by matching the BGS targets with the Galaxy and Mass Assembly (GAMA) survey. We measure the angular clustering for the overall bright sample (r \\(\\) 19.5) and as function of apparent magnitude and colour. This enables to determine the clustering strength and slope by fitting a power-law model that can be used to generate accurate mock catalogues for this tracer. We use a counts-in-cells technique to explore higher-order statistics and cross-correlations with external spectroscopic data sets in order to check the evolution of the clustering with redshift and the redshift distribution of the BGS targets using clustering-redshifts. While this work validates the properties of the BGS bright targets, the final target selection pipeline and clustering properties of the entire DESI BGS will be fully characterised and validated with the spectroscopic data of Survey Validation.

We present a novel approach to the construction of mock galaxy catalogues for large-scale structure analysis based on the distribution of dark matter halos obtained with effective bias models at the field level. We aim to produce mock galaxy catalogues capable of generating accurate covariance matrices for a number of cosmological probes that are expected to be measured in current and forthcoming galaxy redshift surveys (e.g. two- and three-point statistics). We use the bias assignment method (BAM) to model the statistics of halo distribution through a learning algorithm using a few detailed \\(N\\)-body simulations, and approximated gravity solvers based on Lagrangian perturbation theory. Using specific models of halo occupation distributions, we generate galaxy mocks with the expected number density and central-satellite fraction of emission-line galaxies, which are a key target of the DESI experiment. BAM generates mock catalogues with per cent accuracy in a number of summary statistics, such as the abundance, the two- and three-point statistics of halo distributions, both in real and redshift space. In particular, the mock galaxy catalogues display \\(\\sim 3\\%-10\\%\\) accuracy in the multipoles of the power spectrum up to scales of \\(k\\sim 0.4\\,h^{-1}{\\rm Mpc}\\). We show that covariance matrices of two- and three-point statistics obtained with BAM display a similar structure to the reference simulation. BAM offers an efficient way to produce mock halo catalogues with accurate two- and three-point statistics, and is able to generate a variety of multi-tracer catalogues with precise covariance matrices of several cosmological probes. We discuss future developments of the algorithm towards mock production in DESI and other galaxy-redshift surveys. (Abridged)

In triumph and defeat, Graham pursued that vision by building political, social, and cultural bridges-with rock stars, movie stars, presidents, kings, queens, and popes-always in an attempt to communicate the power of Jesus Christ's life and mission.