The f(Q, L_m) gravity model fits observational data from BBN to late-time acceleration, acting as a viable quintessence-like alternative to the standard LambdaCDM model.
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Burnham and David R
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2026 6verdicts
UNVERDICTED 6representative citing papers
BayeSN analysis of ZTF Type Ia supernovae confirms a ~0.1 mag intrinsic environmental step in standardized brightness that is not explained by differences in dust extinction properties.
Rényi entropic corrections to cosmology are constrained by DESI DR2 BAO and GW data to a viable quintessence-like model that approaches ΛCDM without phantom behavior and satisfies BBN bounds.
Implements thermodynamic models for pure elements from 0 K in PyCalphad and ESPEI, remodeling 41 elements with MCMC uncertainty quantification to support improved CALPHAD descriptions.
Ensemble learning with Gaussian copula transformation predicts groundwater heavy metal pollution index with high accuracy (R²=0.96) while identifying key contaminants via clustering.
Analysis of an unidentified Fermi gamma-ray source shows inconclusive results with a mild spectral preference for dark matter annihilation over a pulsar origin.
citing papers explorer
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From Big Bang Nucleosynthesis to Late-Time Acceleration in $f(Q,L_m)$ Gravity
The f(Q, L_m) gravity model fits observational data from BBN to late-time acceleration, acting as a viable quintessence-like alternative to the standard LambdaCDM model.
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On the origin of the environmental step: A BayeSN view of the ZTF SN Ia DR2
BayeSN analysis of ZTF Type Ia supernovae confirms a ~0.1 mag intrinsic environmental step in standardized brightness that is not explained by differences in dust extinction properties.
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Exploring Cosmic Evolution in R\'enyi Entropic Cosmology with Constraints from DESI DR2 BAO and GW Data
Rényi entropic corrections to cosmology are constrained by DESI DR2 BAO and GW data to a viable quintessence-like model that approaches ΛCDM without phantom behavior and satisfies BBN bounds.
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Thermodynamic Modeling of Pure Elements from 0 K with Uncertainty Quantification using PyCalphad and ESPEI
Implements thermodynamic models for pure elements from 0 K in PyCalphad and ESPEI, remodeling 41 elements with MCMC uncertainty quantification to support improved CALPHAD descriptions.
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Smart Ensemble Learning Framework for Predicting Groundwater Heavy Metal Pollution
Ensemble learning with Gaussian copula transformation predicts groundwater heavy metal pollution index with high accuracy (R²=0.96) while identifying key contaminants via clustering.
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Deeper analysis of Fermi-LAT unassociated 4FGL J2112.5-3043 for possible identification
Analysis of an unidentified Fermi gamma-ray source shows inconclusive results with a mild spectral preference for dark matter annihilation over a pulsar origin.