相关论文: Learning Theory Informed Priors for Bayesian Infer…

Primarily motivated by the Hubble tension, we analyze the varying electron mass model and axionlike early dark energy model (EDE) using baryon acoustic oscillation data from DESI DR2 data and including the recent results from ACT DR6. Our…

This paper addresses Bayesian inference related to partial differential equations (PDEs), particularly nonparametric regression constrained by PDEs. To effectively encode prior information, we propose a novel framework that learns a…

A popular proposal for resolving the Hubble tension involves an early phase of dark energy, driven by an axion field with a periodic potential. In this paper, we argue that these models are tightly constrained by the axion weak gravity…

Bayesian inversion is central to the quantification of uncertainty within problems arising from numerous applications in science and engineering. To formulate the approach, four ingredients are required: a forward model mapping the unknown…

Embedding physical knowledge into neural network (NN) training has been a hot topic. However, when facing the complex real-world, most of the existing methods still strongly rely on the quantity and quality of observation data. Furthermore,…

Cosmological models exhibiting crossing of the phantom divide improve the fit to current data, suggesting late-time dark energy (DE) dynamics at $\sim3\sigma$ CL. However, they favor low values of $H_0$, in tension with SH0ES. This may…

We establish concentration rates for estimation of treatment effects in experiments that incorporate prior sources of information -- such as past pilots, related studies, or expert assessments -- whose external validity is uncertain. Each…

Energy-based models (EBMs) are versatile density estimation models that directly parameterize an unnormalized log density. Although very flexible, EBMs lack a specified normalization constant of the model, making the likelihood of the model…

We investigate the observational consequences of a novel class of stable interacting dark energy (IDE) models, featuring interactions between dark matter (DM) and dark energy (DE). In the first part of our work, we start by considering two…

The Hubble ($H_0$) tension between direct measurements of the expansion rate and the prediction of the $\Lambda$CDM cosmological model calibrated on the Cosmic Microwave Background (CMB), is a strong motivation to explore alternative…

We present a physics-informed Bayesian neural-network framework to infer neutron-star equations of state from theoretical priors and to propagate the associated uncertainties to stellar observables. Trained on a large and representative…

We revisit the impact of early dark energy (EDE) on galaxy clustering using BOSS galaxy power spectra, analyzed using the effective field theory (EFT) of large-scale structure (LSS), and anisotropies of the cosmic microwave background (CMB)…

Adding domain knowledge to a learning system is known to improve results. In multi-parameter Bayesian frameworks, such knowledge is incorporated as a prior. On the other hand, various model parameters can have different learning rates in…

Analyses of the full shape of BOSS DR12 power spectrum using the one-loop prediction from the Effective Field Theory of Large-Scale Structure (EFTBOSS) have led to new constraints on extensions to the $\Lambda$CDM model, such as Early Dark…

We investigate the constraints on early dark energy (EDE) by combining the most recent CMB observations available, ACT DR4, SPT-3G, and Planck2018 ($\ell_\text{TT,max}=1000$) data. This combined CMB dataset favors non-zero EDE fractions and…

JWST has revealed a large population of ultra-violet (UV)-bright galaxies at $z\gtrsim 10$ and possibly overly massive galaxies at $z\gtrsim 7$, challenging standard galaxy formation models in the $\Lambda$CDM cosmology. We use an empirical…

We present a Dark Energy (DE) model with a sound derivation as a natural extension of the Standard Model of particle physics with no free parameters and an excellent fit with current cosmological data improving by 21% the $\Lambda$CDM fit…

We present the most up-to-date constraints on axion early dark energy (AEDE) from cosmic microwave background (CMB) and baryon acoustic oscillation (BAO) measurements. In particular, we assess the impact of data from ground-based CMB…

We review the current status of Early Dark Energy (EDE) models proposed to resolve the "Hubble tension", the discrepancy between "direct" measurements of the current expansion rate of the Universe and "indirect measurements" for which the…

We present a novel class of Physics-Informed Neural Networks that is formulated based on the principles of Evidential Deep Learning, where the model incorporates uncertainty quantification by learning parameters of a higher-order…