arXiv subjects

Yun-Long Li

Publications and source records attributed to Yun-Long Li.

4 recordsLinked to original sources

arXiv2022

Forecast of Cosmological Constraints with Type Ia Supernovae from the Chinese Space Station Telescope

The 2-m aperture Chinese Space Station Telescope (CSST), which observes at wavelengths ranging from 255 to 1000 nm, is expected to start science operations in 2024. An ultra-deep field observation program covering approximately 10 square degrees is proposed with supernovae (SNe) and other transients as one of its primary science drivers. This paper presents the simulated detection results of type Ia supernovae (SNe Ia) and explores the impact of new datasets on the determinations of cosmological parameters. The simulated observations are conducted with an exposure time of 150 s and cadences of 10, 20, and 30 days. The survey mode covering a total of 80 observations but with a random cadence in the range of 4 to 14 days is also explored. Our simulation results indicate that the CSST can detect up to $\sim 1800$ SNe Ia at z $<$ 1.3. The simulated SNe Ia are then used to constrain the cosmological parameters. The constraint on $Ω_m$ can be improved by 37.5% using the 10-day cadence sample in comparison with the Pantheon sample. A deeper measurement simulation with a 300 s exposure time together with the Pantheon sample improves the current constraints on $Ω_m$ by 58.3% and $ω$ by 47.7%. Taking future ground-based SNe Ia surveys into consideration, the constraints on $ω$ can be improved by 59.1%. The CSST ultra-deep field observation program is expected to discover large amounts of SNe Ia over a broad redshift span and enhance our understanding of the nature of dark energy.

astro-ph.CO↗

arXiv2019

Model-independent determination of cosmic curvature based on Padé approximation

Given observations of the standard candles and the cosmic chronometers, we apply Padé parameterization to the comoving distance and the Hubble paramter to find how stringent the constraint is set to the curvature parameter by the data. A weak informative prior is introduced in the modeling process to keep the inference away from the singularities. Bayesian evidence for different order of Padé parameterizations is evaluated during the inference to select the most suitable parameterization in light of the data. The data we used prefer a parameterization form of comoving distance as $D_{01}(z)=\frac{a_0 z}{1+b_1 z}$ as well as a competitive form $D_{02}(z)=\frac{a_0 z}{1+b_1 z + b_2 z^2}$. Similar constraints on the spatial curvature parameter are established by those models and given the Hubble constant as a byproduct: $Ω_k = 0.25^{+0.14}_{-0.13}$ (68\% confidence level [C.L.]), $H_0 = 67.7 \pm 2.0$ km/s/Mpc (68\% C.L.) for $D_{01}$, and $Ω_k = -0.01 \pm 0.13$ (68\% C.L.), $H_0 = 68.8 \pm 2.0$ km/s/Mpc (68\% C.L.) for $D_{02}$. The evidence of different models demonstrates the qualitative analysis of the Padé parameterizations for the comoving distance.

astro-ph.CO↗

arXiv2019

Model Comparison of Dark Energy models Using Deep Network

This work uses a combination of a variational auto-encoder and generative adversarial network to compare different dark energy models in light of observations, e.g., the distance modulus from type Ia supernovae. The network finds an analytical variational approximation to the true posterior of the latent parameters in the models, yielding consistent model comparison results with those derived by the standard Bayesian method, which suffers from a computationally expensive integral over the parameters in the product of the likelihood and the prior. The parallel computational nature of the network together with the stochastic gradient descent optimization technique leads to an efficient way to compare the physical models given a set of observations. The converged network also provides interpolation for a dataset, which is useful for data reconstruction.

astro-ph.CO↗

arXiv2014

Model-independent determination of curvature parameter by using $H(z)$ and $D_A(z)$ data pair from BAO measurement

We present a model-independent determination of the curvature parameter $Ω_k$ by using the Hubble parameter $H(z)$ and angular diameter distance $D_A(z)$ from the recent baryon acoustic oscillation (BAO) measurements. Each $H(z)$ and $D_A(z)$ pair from a BAO measurement can constrain a curvature parameter. The accuracy of the curvature measurement improves with increased redshift of $H(z)$ and $D_A(z)$ data. By using the $H(z)$ and $D_A(z)$ pair derived from BAO Lyman $α$ forest measurement at $z=2.36$, the $Ω_k$ is confined to be -0.05$\pm$0.06, which is consistent with the curvature $-0.037^{+0.044}_{-0.042}$ constrained by the nine-year WMAP data only. Considering future BAO meausurements, at least one order of magnitude improvement of this curvature measurement could be expected.

astro-ph.CO↗