arXiv subjects

Li-Yang Gao

Publications and source records attributed to Li-Yang Gao.

7 recordsLinked to original sources

arXiv2025

Alleviating the $H_0$ tension through the interacting dark energy model from quantum gravitational field theory in light of DESI DR2

Recent DESI DR2 data has shown a significant preference for dynamical dark energy, yet this has further exacerbated the $H_0$ tension. In this work, we explore the potential of interacting dark energy models ($\widetilde{\Lambda}$CDM and $e\widetilde{\Lambda}$CDM) within the asymptotic-safety framework of quantum gravitational field theory to alleviate the $H_0$ tension. We perform observational constraints using the latest baryon acoustic oscillation data from DESI DR2, cosmic microwave background (CMB) data from Planck and ACT, and type Ia supernova data from DESY5 and PantheonPlus, as well as the SH0ES data. From our analysis, we observe the dynamical scale parameter of the cosmological constant, $\delta_{\Lambda} = -0.270\pm 0.100$, in the $e\widetilde{\Lambda}$CDM model using the CMB+DESI+SH0ES data, which deviates from $\Lambda$CDM at the $2.7\sigma$ level. Simultaneously, we find $H_0 = 70.84\pm 0.74~\mathrm{km\,s^{-1}\,Mpc^{-1}}$, reducing the $H_0$ tension to $1.7\sigma$. This increase in the inferred $H_0$ is due to the anti-correlation between $\delta_{\Lambda}$ and $H_0$, whereby a negative $\delta_{\Lambda}$ leads to a higher $H_0$ value. Furthermore, for the CMB+DESI+SH0ES combination, we obtain $\Delta\chi^2_{\min}=-14.14$ and $\Delta\mathrm{DIC}=-9.18$, favoring the $e\widetilde{\Lambda}$CDM model over $\Lambda$CDM. Overall, the $e\widetilde{\Lambda}$CDM model can improve the fit and ease the $H_0$ tension, especially for the data combinations that provide the strongest statistical support.

astro-ph.CO↗

arXiv2024

Extracting the Epoch of Reionization Signal with 3D U-Net Neural Networks Using Data-driven Systematic Effect Model

Neutral hydrogen (HI) serves as a crucial probe for the Cosmic Dawn and the Epoch of Reionization (EoR). Actual observations of the 21-cm signal often encounter challenges such as thermal noise and various systematic effects. To overcome these challenges, we simulate SKA-Low-depth images in South Celestial Pole (SCP) field and process them with a deep learning method. We utilized foreground residuals acquired by LOFAR during actual North Celestial Pole (NCP) field observations, thermal and excess variances calculated via Gaussian process regression (GPR), and 21-cm signals generated with 21cmFAST for signal extraction tests. Our approach to overcome these foreground, thermal noise, and excess variance components employs a 3D U-Net neural network architecture for image analysis. When considering thermal noise corresponding to 1752 hours of integration time, U-Net provides reliable 2D power spectrum predictions, and robustness tests ensure that we get realistic EoR signals. Adding foreground residuals, however, causes inconsistencies below the horizon delay-line. Lastly, evaluating both thermal noise and excess variances with observations up to 4380 hours and 13140 hours ensures reliable power spectrum estimations within the EoR window and across nearly all scales, respectively. The incoherence of excess variances in the frequency direction can greatly affect deep learning to extract 21-cm signals.

astro-ph.IM↗

arXiv2022

Dark energy and matter interacting scenario to relieve $H_0$ and $S_8$ tensions

We consider a new cosmological model (called $\tilde\Lambda$CDM) in which the vacuum energy interacts with matter and radiation, and test this model using the current cosmological observations. Using the CMB+BAO+SN (CBS) dataset to constrain the model, we find that $H_0$ and $S_8$ tensions are relieved to $2.87\sigma$ and $2.77\sigma$, respectively. However, in this case, the $\tilde\Lambda$CDM model is not favored by the data, compared with $\Lambda$CDM. We find that when the $H_0$ and $S_8$ data are added to the data combination, the situation is significantly improved. In the CBS+$H_0$ case, the model relieves the $H_0$ tension to $0.47\sigma$, and the model is favored over $\Lambda$CDM. In the CBS+$H_0$+$S_8$ case, we obtain a synthetically best situation, in which the $H_0$ and $S_8$ tensions are relieved to $0.72\sigma$ and $2.11\sigma$, respectively. In this case, the model is most favored by the data. Therefore, this cosmological model can greatly relieve the $H_0$ tension and simultaneously effectively alleviate the $S_8$ tension.

astro-ph.CO↗

arXiv2022

Eliminating polarization leakage effect for neutral hydrogen intensity mapping with deep learning

The neutral hydrogen (HI) intensity mapping (IM) survey is regarded as a promising approach for cosmic large-scale structure (LSS) studies. A major issue for the HI IM survey is to remove the bright foreground contamination. A key to successfully remove the bright foreground is to well control or eliminate the instrumental effects. In this work, we consider the instrumental effect of polarization leakage and use the U-Net approach, a deep learning-based foreground removal technique, to eliminate the polarization leakage effect. The thermal noise is assumed to be a subdominant factor compared with the polarization leakage for future HI IM surveys and ignored in this analysis. In this method, the principal component analysis (PCA) foreground subtraction is used as a preprocessing step for the U-Net foreground subtraction. Our results show that the additional U-Net processing could either remove the foreground residual after the conservative PCA subtraction or compensate for the signal loss caused by the aggressive PCA preprocessing. Finally, we test the robustness of the U-Net foreground subtraction technique and show that it is still reliable in the case of existing constraint error on HI fluctuation amplitude.

astro-ph.IM↗

arXiv2022

Eliminating Primary Beam Effect in Foreground Subtraction of Neutral Hydrogen Intensity Mapping Survey with Deep Learning

In the neutral hydrogen (HI) intensity mapping (IM) survey, the foreground contamination on the cosmological signals is extremely severe, and the systematic effects caused by radio telescopes themselves further aggravate the difficulties in subtracting foreground. In this work, we investigate whether the deep learning method, concretely the 3D U-Net algorithm here, can play a crucial role in foreground subtraction when considering the systematic effect caused by the telescope's primary beam. We consider two beam models, i.e., the Gaussian beam model as a simple case and the Cosine beam model as a sophisticated case. The traditional principal component analysis (PCA) method is employed as a comparison and, more importantly, as the preprocessing step for the U-Net method to reduce the sky map dynamic range. We find that in the case of the Gaussian beam, the PCA method can effectively clean the foreground. However, the PCA method cannot handle the systematic effect induced by the Cosine beam, and the additional U-Net method can improve the result significantly. In order to show how well the PCA and U-Net methods can recover the HI signals, we also derive the HI angular power spectra, as well as the HI 2D power spectra, after performing the foreground subtractions. It is found that, in the case of Gaussian beam, the concordance with the original HI map using U-Net is better than that using PCA by $27.4\%$, and in the case of Cosine beam, the concordance using U-Net is better than that using PCA by $144.8\%$. Therefore, the U-Net based foreground subtraction can efficiently eliminate the telescope primary beam effect and shed new light on recovering the HI power spectrum for future HI IM experiments.

astro-ph.IM↗

arXiv2021

Relieving the $H_0$ tension with a new interacting dark energy model

We investigate an extended cosmological model motivated by the asymptotic safety of gravitational field theory, in which the matter and radiation densities and the cosmological constant receive a correction parametrized by the parameters $\delta_G$ and $\delta_\Lambda$, leading to that both the evolutions of the matter and radiation densities and the cosmological constant slightly deviate from the standard forms. Here we explain this model as a scenario of vacuum energy interacting with matter and radiation. We consider two cases of the model: {(i) ${\tilde\Lambda}$CDM with one additional free parameter $\delta_G$, with $\delta_{\rm G}$ and $\delta_\Lambda$ related by a low-redshift limit relation and (ii) e${\tilde\Lambda}$CDM with two additional free parameters $\delta_G$ and $\delta_\Lambda$ that are independent of each other.} We use two data combinations, CMB+BAO+SN (CBS) and CMB+BAO+SN+$H_0$ (CBSH), to constrain the models. We find that, in the case of using the CBS data, neither ${\tilde\Lambda}$CDM nor e${\tilde\Lambda}$CDM can effectively alleviate the $H_0$ tension. However, it is found that using the CBSH data the $H_0$ tension can be greatly relieved by the models. In particular, in the case of e${\tilde\Lambda}$CDM, the $H_0$ tension can be resolved to 0.71$\sigma$. We conclude that as an interacting dark energy model, ${\tilde\Lambda}$CDM is much better than $\Lambda(t)$CDM in the sense of both relieving the $H_0$ tension and fitting to the current observational data.

astro-ph.CO↗

arXiv2019

Improving cosmological parameter estimation with the future 21 cm observation from SKA

Future observations of 21 cm emission from neutral hydrogen survey will become a promising approach to probe the large scale structure of Universe. In this paper, we investigate the impacts of Square Kilometer Array (SKA) 21 cm observation on the estimation of cosmological parameters. We use the simulated data of the baryonic acoustic oscillation (BAO) measurements based on the future SKA experiment with the intensity mapping (IM) technique to do the analysis. For the current observations, we use the latest cosmic microwave background (CMB) observation from {\it Planck} 2018, the optical BAO measurements, and the Type Ia supernovae (SN) observation (Pantheon compilation). We find that the SKA mock data could break the degeneracy between the matter density and the Hubble constant, further improving the cosmological constraints to a great extent. We also find that the constraint on the equation of state parameters of dark energy could be significantly improved by including the SKA mock data into the cosmological global fit.

astro-ph.CO↗