Rescaling merger trees with a halo-profile correction enables cheap generation of galaxy summary statistics across cosmologies using semi-analytic models, matching dedicated simulation accuracy with far fewer base runs.
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The Dark Energy Spectroscopic Instrument (DESI)
11 Pith papers cite this work. Polarity classification is still indexing.
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abstract
We present the status of the Dark Energy Spectroscopic Instrument (DESI) and its plans and opportunities for the coming decade. DESI construction and its initial five years of operations are an approved experiment of the US Department of Energy and is summarized here as context for the Astro2020 panel. Beyond 2025, DESI will require new funding to continue operations. We expect that DESI will remain one of the world's best facilities for wide-field spectroscopy throughout the decade. More about the DESI instrument and survey can be found at https://www.desi.lbl.gov.
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Hierarchical Bayesian inference allows accurate recovery of intrinsic astrophysical source populations even when follow-up selection is unknown and correlated with parameters of interest.
Bayesian tension analysis shows Planck CMB dipole in >5σ disagreement with CatWISE infrared sources and moderate-to-strong disagreement with radio surveys NVSS and RACS, with evidence for shared astrophysical signals in some catalogs.
CMB-only data give f_EDE < 0.07 at 95% CL with no strong AEDE signal, while CMB+DESI yields f_EDE = 0.055^{+0.024}_{-0.047} at 68% CL and lowers Hubble tension to 2.6 sigma.
Forecasts indicate 10-sigma detection for squeezed triangles and 100-sigma for combined shapes in the 21cm-galaxy cross-bispectrum with 100 hours of SKA-Mid interferometric observations on scales 0.2 to 0.9 per Mpc.
Interacting k-essence dark energy and non-pressureless dark matter models with two interaction forms are shown to reproduce major cosmological epochs and fit observations comparably to LambdaCDM while admitting late-time de Sitter attractors.
Models of interacting bosonic dark energy and fermionic dark matter in Einstein-scalar-Gauss-Bonnet gravity with exponential and power-law potentials are dynamically analyzed and constrained by observational data, showing consistency with LambdaCDM.
Interacting scalar fields coupled to Gauss-Bonnet gravity yield viable dark energy and dark matter models that match Pantheon+ and DES supernova data while preferring over LambdaCDM at high redshifts with Roman mocks.
LStein is presented as a novel visualization approach for sparse 2.5-dimensional data, implemented in Python and demonstrated on astronomical lightcurves.
CLOE has been extended to model magnification bias, massive neutrinos, and modified gravity for Euclid probes and validated via posterior sampling on synthetic data.