Bayesian reconstruction and exhaustive symbolic regression on CMB, BAO, and supernova data yield the one-parameter dark energy parametrization w(a) = w0 / sqrt(a) that fits observations comparably to CPL and better than LambdaCDM.
Improving the Flexibility and Robustness of Model-Based Derivative-Free Optimization Solvers
10 Pith papers cite this work. Polarity classification is still indexing.
10
Pith papers citing it
abstract
We present DFO-LS, a software package for derivative-free optimization (DFO) for nonlinear Least-Squares (LS) problems, with optional bound constraints. Inspired by the Gauss-Newton method, DFO-LS constructs simplified linear regression models for the residuals. DFO-LS allows flexible initialization for expensive problems, whereby it can begin making progress from as few as two objective evaluations. Numerical results show DFO-LS can gain reasonable progress on some medium-scale problems with fewer objective evaluations than is needed for one gradient evaluation. DFO-LS has improved robustness to noise, allowing sample averaging, the construction of regression-based models, and multiple restart strategies together with an auto-detection mechanism. Our extensive numerical experimentation shows that restarting the solver when stagnation is detected is a cheap and effective mechanism for achieving robustness, with superior performance over both sampling and regression techniques. We also present our package Py-BOBYQA, a Python implementation of BOBYQA (Powell, 2009), which also implements robustness to noise strategies. Our numerical experiments show that Py-BOBYQA is comparable to or better than existing general DFO solvers for noisy problems. In our comparisons, we introduce a new adaptive measure of accuracy for the data profiles of noisy functions that strikes a balance between measuring the true and the noisy objective improvement.
citation-role summary
method 4
citation-polarity summary
roles
method 4polarities
use method 4representative citing papers
Cobaya is a modular Bayesian analysis code that exploits model interdependencies via automatic caching and a novel parameter-blocking algorithm to minimize sampling cost.
A QCD-vacuum-based model of dynamical dark energy fits Planck+ACT+SPT, DESI DR2, and supernova data while reproducing the late-time evolution favored by DESI.
A late-onset oscillating quintessence model improves the fit to DESI plus supernova and CMB data by Delta chi squared of about 9 over Lambda CDM, driven by background expansion.
A sign-switching dark energy model (Λ_s CDM) recovers positive effective neutrino masses (0.055 ± 0.050 eV) consistent with oscillation data, unlike ΛCDM which prefers negative values (-0.075 eV), for DESI DR2 + CMB + supernova fits with z_† > 2.4.
Updated constraints on neutrino-sustained primordial vector modes imply magnetic fields too weak to seed observations and cannot reproduce the EB power spectrum while satisfying parity-even limits.
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.
Spline-based shape optimization via isogeometric integral equation simulation yields a multi-tapered coaxial balun with reduced scattering parameter magnitude across frequencies.
Recent CMB datasets tighten 95% CL upper bounds on vector-mode amplitude r_v to 1.3e-4 (neutrino isocurvature), 6.8 (octupole), and 4.2 (sourced) at k=0.05 Mpc^-1, with no significant detection.
Analysis of galaxy cluster and supernova data reveals a ~2σ directional variation in the Hubble constant, robust across calibration methods and aligned with the CMB dipole.
citing papers explorer
-
Data-Driven Discovery of a Simple Phantom-Crossing Dark Energy Parametrization
Bayesian reconstruction and exhaustive symbolic regression on CMB, BAO, and supernova data yield the one-parameter dark energy parametrization w(a) = w0 / sqrt(a) that fits observations comparably to CPL and better than LambdaCDM.
-
Evolving Dark Energy Is Vacuum Energy After All
A QCD-vacuum-based model of dynamical dark energy fits Planck+ACT+SPT, DESI DR2, and supernova data while reproducing the late-time evolution favored by DESI.
-
Late-Time Oscillating Quintessence in Light of DESI
A late-onset oscillating quintessence model improves the fit to DESI plus supernova and CMB data by Delta chi squared of about 9 over Lambda CDM, driven by background expansion.
-
Negative neutrino mass or negative dark energy?
A sign-switching dark energy model (Λ_s CDM) recovers positive effective neutrino masses (0.055 ± 0.050 eV) consistent with oscillation data, unlike ΛCDM which prefers negative values (-0.075 eV), for DESI DR2 + CMB + supernova fits with z_† > 2.4.
-
Revisiting constraints on primordial vector modes and implications for sourced magnetic fields and observed $EB$ power spectrum
Updated constraints on neutrino-sustained primordial vector modes imply magnetic fields too weak to seed observations and cannot reproduce the EB power spectrum while satisfying parity-even limits.
-
SPT-3G D1: Axion Early Dark Energy with CMB experiments and DESI
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.
-
The Status of Gravitational Vector Perturbations with Recent CMB Data
Recent CMB datasets tighten 95% CL upper bounds on vector-mode amplitude r_v to 1.3e-4 (neutrino isocurvature), 6.8 (octupole), and 4.2 (sourced) at k=0.05 Mpc^-1, with no significant detection.
-
Probing cosmic anisotropy with galaxy clusters and supernovae
Analysis of galaxy cluster and supernova data reveals a ~2σ directional variation in the Hubble constant, robust across calibration methods and aligned with the CMB dipole.