As a large-scale overdensity collapses, it affects the orientation and shape of galaxies that form, by exerting tidal shear along their axes. Therefore, the shapes of elliptical galaxies align with the tidal field of cosmic structures. This intrinsic alignment provides insights into galaxy formation and the primordial universe, complements late-time cosmological probes and constitutes a significant systematic effect for weak gravitational lensing observations. In the present study, we provide constraints on the linear alignment model using a fully Bayesian field-level approach, using galaxy shape measurements from the SDSS-III BOSS LOWZ sample and three-dimensional tidal fields constrained with the LOWZ and CMASS galaxy samples of the SDSS-III BOSS survey. We find 4$\sigma$ evidence of intrinsic alignment, with an amplitude of $A_I=2.9 \pm 0.7$ at 20$h^{-1}\;\mathrm{Mpc}$. less

Analyzing the clustering of galaxies at the field level in principle promises access to all the cosmological information available. Given this incentive, in this paper we investigate the performance of field-based forward modeling approach to galaxy clustering using the effective field theory (EFT) framework of large-scale structure (LSS). We do so by applying this formalism to a set of consistency and convergence tests on synthetic datasets. We explore the high-dimensional joint posterior of LSS initial conditions by combining Hamiltonian Monte Carlo sampling for the field of initial conditions, and slice sampling for cosmology and model parameters. We adopt the Lagrangian perturbation theory forward model from [1], up to second order, for the forward model of biased tracers. We specifically include model mis-specifications in our synthetic datasets within the EFT framework. We achieve this by generating synthetic data at a higher cutoff scale $\Lambda_0$, which controls which Fourier modes enter the EFT likelihood evaluation, than the cutoff $\Lambda$ used in the inference. In the presence of model mis-specifications, we find that the EFT framework still allows for robust, unbiased joint inference of a) cosmological parameters - specifically, the scaling amplitude of the initial conditions - b) the initial conditions themselves, and c) the bias and noise parameters. In addition, we show that in the purely linear case, where the posterior is analytically tractable, our samplers fully explore the posterior surface. We also demonstrate convergence in the cases of nonlinear forward models. Our findings serve as a confirmation of the EFT field-based forward model framework developed in [2-7], and as another step towards field-level cosmological analyses of real galaxy surveys. less

We present the first detection of the baryon acoustic oscillations (BAO) signal obtained using unblinded data collected during the initial two months of operations of the Stage-IV ground-based Dark Energy Spectroscopic Instrument (DESI). From a selected sample of 261,291 Luminous Red Galaxies spanning the redshift interval 0.4 < z < 1.1 and covering 1651 square degrees with a 57.9% completeness level, we report a ~5 sigma level BAO detection and the measurement of the BAO location at a precision of 1.7%. Using a Bright Galaxy Sample of 109,523 galaxies in the redshift range 0.1 < z < 0.5, over 3677 square degrees with a 50.0% completeness, we also detect the BAO feature at ~3 sigma significance with a 2.6% precision. These first BAO measurements represent an important milestone, acting as a quality control on the optimal performance of the complex robotically-actuated, fiber-fed DESI spectrograph, as well as an early validation of the DESI spectroscopic pipeline and data management system. Based on these first promising results, we forecast that DESI is on target to achieve a high-significance BAO detection at sub-percent precision with the completed 5-year survey data, meeting the top-level science requirements on BAO measurements. This exquisite level of precision will set new standards in cosmology and confirm DESI as the most competitive BAO experiment for the remainder of this decade. less

The amount of information energy in the universe is of the same order of magnitude as the total mc2 energy equivalence of all universe baryons. Information energy can contribute to both dark energy and dark matter attributed effects. It is a transitional dark energy: phantom with increasing energy density in the early universe, changing around a redshift, z~1.35, to a near constant energy density in the late universe. On the scale of the universe, information energy is repulsive as a dark energy causing the accelerating expansion, but, on local scales, it is clumped and attractive like dark matter. The combined characteristics of information energy enable us to resolve many of the problems of the standard ΛCDM model: the cosmological constant problem; the dark matter problem; the Hubble tension; S8 matter fluctuation parameter tension; cosmological principle problem and the cosmological coincidence problem. In addition, the model satisfies the two ideal requirements of a cosmological model: simplicity and naturalness. Most importantly, the model predicts a measurable difference in the Hubble parameter around z~2 that provides a clear signature for the information energy model to be falsified. Journal citation: https://www.mdpi.com/1099-4300/24/3/385 less

We present QUOTAS, a novel research platform for the data-driven investigation of super-massive black hole populations. While supermassive black hole data sets -- observations and simulations -- have grown rapidly in complexity and abundance, our computational environments and analysis tools have not matured commensurately to exhaust opportunities for discovery. Motivated to explore black hole host galaxy and the parent dark matter halo connection, in this pilot version of QUOTAS, we assemble and co-locate the high-redshift, luminous quasar population at $z \geq 3$ alongside simulated data of the same epochs. Leveraging machine learning algorithms we expand simulation volumes that successfully replicate halo populations beyond the training set. Training machine learning algorithms on the Illustris-TNG300 simulation that includes baryonic physics, we populate the larger LEGACY Expanse dark matter-only box with quasars. Our first science results comparing observational and machine learning simulated quasars at $z \sim 3$, reveal that while the recovered Black Hole Mass Functions and clustering are in good agreement, simulated supermassive black holes fail to accrete, shine and grow at high enough rates to match observed quasars. We conclude that sub-grid models of mass accretion and supermassive black hole feedback implemented in Illustris-TNG300 do not reproduce their observed mass growth. QUOTAS, demonstrates the power of machine learning, both for analyzing large complex datasets, and offering a unique opportunity to interrogate our theoretical model assumptions. We deploy machine learning again to derive and devise an optimal survey strategy for bringing the undetected lower luminosity quasar population into view. QUOTAS, and all related materials are publicly available at the Google Kaggle platform. less