56 citations · 141 across the 7 of their papers we have counts for
7 papers
Parallel adaptive reweighting importance sampling for Bayesian astrophysics
Miaoxin Liu, Alvin J. K. Chua
Efficient sampling from high-dimensional, multi-modal posteriors is a central challenge in Bayesian inference across the physical sciences. Gravitational-wave (GW) astronomy, where…
Gravitational Wave Astronomy With TianQin
En-Kun Li, Shuai Liu, Alejandro Torres-Orjuela +50
The opening of the gravitational wave window has significantly enhanced our capacity to explore the universe's most extreme and dynamic sector. In the mHz frequency range, a divers…
Improving the scalability of Gaussian-process error marginalization in gravitational-wave inference
Miaoxin Liu, Xiao-Dong Li, Alvin J. K. Chua
The accuracy of Bayesian inference can be negatively affected by the use of inaccurate forward models. In the case of gravitational-wave inference, accurate but computationally exp…
Using the Mark Weighted Correlation Functions to Improve the Constraints on Cosmological Parameters
Yizhao Yang, Haitao Miao, Qinglin Ma +8
We used the mark weighted correlation functions (MCFs), , to study the large scale structure of the Universe. We studied five types of MCFs with the weighting scheme , w…
Can Non-standard Recombination Resolve the Hubble Tension?
Miaoxin Liu, Zhiqi Huang, Xiaolin Luo +3
The inconsistent Hubble constant values derived from cosmic microwave background (CMB) observations and from local distance-ladder measurements may suggest new physics beyond the s…
Band-limited Features in the Primordial Power Spectrum Do Not Resolve the Hubble Tension
MiaoXin Liu, Zhiqi Huang
For a standard CDM universe with a power-law primordial power spectrum, the discrepancy between early- and late-universe measurements of the Hubble constant continued to grow an…
Cosmological parameter estimation from large-scale structure deep learning
Shuyang Pan, Miaoxin Liu, Jaime Forero-Romero +4
We propose a light-weight deep convolutional neural network (CNN) to estimate the cosmological parameters from simulated 3-dimensional dark matter distributions with high accuracy.…