Comparative evaluation of Bayesian Neural Network surrogates versus Gaussian Processes in Bayesian Optimization applied to Carbon Capture and Storage operations, presented as the first such application in reservoir engineering.
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Late-time datasets yield 1-2.74σ preference for dynamical dark energy over ΛCDM, with consistent signs of Quintom-B behavior (ω0 > -1, ωa < 0) that strengthen when DES-Dovekie or Union3 supernovae are added.
DESI DR2 BAO combined with Pantheon+, DES-Dovekie and Union3 supernovae yields 1.1-2.3 sigma preference for Quintom-B type evolving dark energy (w0 > -1, wa < 0) with phantom crossing near z ~ 0.5, but no model reaches robust statistical significance.
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Bayesian Neural Network Surrogates for Bayesian Optimization of Carbon Capture and Storage Operations
Comparative evaluation of Bayesian Neural Network surrogates versus Gaussian Processes in Bayesian Optimization applied to Carbon Capture and Storage operations, presented as the first such application in reservoir engineering.
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Probing departures from $\Lambda$CDM by late-time datasets
Late-time datasets yield 1-2.74σ preference for dynamical dark energy over ΛCDM, with consistent signs of Quintom-B behavior (ω0 > -1, ωa < 0) that strengthen when DES-Dovekie or Union3 supernovae are added.
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Evidence for evolving dark energy from DESI DR2 BAO and Pantheon$^+$, DES-Dovekie, and Union3
DESI DR2 BAO combined with Pantheon+, DES-Dovekie and Union3 supernovae yields 1.1-2.3 sigma preference for Quintom-B type evolving dark energy (w0 > -1, wa < 0) with phantom crossing near z ~ 0.5, but no model reaches robust statistical significance.
- Evidence of dynamical dark energy found via the DESI DR2 Lyman$\alpha$ forest