A catalog-level Alcock-Paczynski wavelength-shift blinding scheme for the Lyman-alpha forest robustly hides the expansion history and correctly shifts the BAO peak on DESI DR1 data and mocks.
Cuceu et al.,DESI DR1 Lyαforest: 3D full-shape analysis and cosmological constraints, 2509.15308
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An EFT-based field-level forward model for the Lyman-alpha forest matches simulations at the percent level on quasi-linear scales and generates mocks for DESI and DESI-II analyses.
Composite dark matter from a keV–MeV confining phase transition sources an IR-enhanced curvature spectrum that competes with free-streaming suppression, yielding concrete CMB and Lyman-α bounds on transition strength and temperature.
A multi-eigenbasis denoising technique using mock reference and classifier eigenbases is introduced and shown on held-out mocks to outperform smoothing for covariance estimation in Lyα forest analyses.
A heuristic power-spectrum rescaling applied to DESI DR1 BAO data plus CMB acoustic scale anchor yields H0 values of 69.2 to 70.3 km/s/Mpc at sub-2% precision across three independent late-time datasets.
DESI DR1 bispectrum plus DR2 BAO data raise σ8 and S8 by ~1.1-1.2σ while tightening uncertainties, shift DESI-only w0waCDM toward ΛCDM, produce a 2.8σ deviation from ΛCDM when combined with CMB, and yield a neutrino mass sum posterior of 0.26±0.17 eV.
Fitting seven dark-energy parameterizations to DESI DR2 Lyman-alpha BAO plus CMB, galaxy BAO, and three supernova samples yields 2–2.5σ hints of evolving dark energy (w0>−1, wa<0) that weaken below 2σ when supernovae are included.
For DESI DR2 BAO versus DES SNe, the comoving-distance ratio is flat but the inverse-Hubble-distance ratio falls with redshift at 2.3–2.5σ, which the authors attribute to systematics.
Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.
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Alcock-Paczynski Blinding Scheme for the Ly-$\alpha$ Forest Analysis
A catalog-level Alcock-Paczynski wavelength-shift blinding scheme for the Lyman-alpha forest robustly hides the expansion history and correctly shifts the BAO peak on DESI DR1 data and mocks.
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Lyman-Alpha Forest and its Cross-Correlation with High-Redshift Galaxies in Effective Field Theory at the Field Level
An EFT-based field-level forward model for the Lyman-alpha forest matches simulations at the percent level on quasi-linear scales and generates mocks for DESI and DESI-II analyses.
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Probing Confining Dark Sectors with Cosmological Perturbations
Composite dark matter from a keV–MeV confining phase transition sources an IR-enhanced curvature spectrum that competes with free-streaming suppression, yielding concrete CMB and Lyman-α bounds on transition strength and temperature.
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A multi-eigenbasis approach to covariance matrix denoising for cosmological inference
A multi-eigenbasis denoising technique using mock reference and classifier eigenbases is introduced and shown on held-out mocks to outperform smoothing for covariance estimation in Lyα forest analyses.
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$H_0$ Without the Sound Horizon (or Supernovae): A 2% Measurement in DESI DR1
A heuristic power-spectrum rescaling applied to DESI DR1 BAO data plus CMB acoustic scale anchor yields H0 values of 69.2 to 70.3 km/s/Mpc at sub-2% precision across three independent late-time datasets.
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Cosmological constraints from the DESI DR1 Bispectrum Full-Shape and DR2 BAO
DESI DR1 bispectrum plus DR2 BAO data raise σ8 and S8 by ~1.1-1.2σ while tightening uncertainties, shift DESI-only w0waCDM toward ΛCDM, produce a 2.8σ deviation from ΛCDM when combined with CMB, and yield a neutrino mass sum posterior of 0.26±0.17 eV.
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Evidence of dynamical dark energy found via the DESI DR2 Lyman$\alpha$ forest
Fitting seven dark-energy parameterizations to DESI DR2 Lyman-alpha BAO plus CMB, galaxy BAO, and three supernova samples yields 2–2.5σ hints of evolving dark energy (w0>−1, wa<0) that weaken below 2σ when supernovae are included.
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Crosschecking Cosmic Distances from DESI BAO and DES SNe
For DESI DR2 BAO versus DES SNe, the comoving-distance ratio is flat but the inverse-Hubble-distance ratio falls with redshift at 2.3–2.5σ, which the authors attribute to systematics.
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Machine Learning Techniques for Astrophysics and Cosmology: Lyman-$\alpha$ forest
Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.