Browsing by Author "Wang, Jingying"

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    Direct parameter inference from global EoR signal with Bayesian statistics
    (Oxford University Press, 2020) Gu, Junhua; Wang, Jingying
    In the observation of sky-averaged HI signal from Epoch of Reionization (EoR), model parameter inference can be a computation-intensive work, which makes it hard to perform a direct one-stage model parameter inference by using Markov Chain Monte Carlo (MCMC) sampling method in Bayesian framework. Instead, a two-stage inference is usually used, i.e. the parameters of some characteristic points on the EoR spectrum model are first estimated, which are then used as the input to estimate physical model parameters further. However, some previous works had noticed that this kind of method could bias results, and it could be meaningful to answer the question of whether it is feasible to perform direct one-stage MCMC sampling and obtain unbiased physical model parameter estimations. In this work, we studied this problem and confirmed the feasibility. We find that unbiased estimations to physical model parameters can be obtained with a one-stage direct MCMC sampling method. We also study the influence of some factors that should be considered in practical observations to model parameter inference. We find that a very tiny amplifier gain calibration error (10−5 relative error) with complex spectral structures can significantly bias the parameter estimation; the frequency-dependent antenna beam and geographical position can also influence the results, so that should be carefully handled.
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    Intrinsic scatter of caustic masses and hydrostatic bias: An observational study
    (EDP Sciences, 2017) Andreon, Stefano; Trinchier, G.; Wang, Jingying
    All estimates of cluster mass have some intrinsic scatter and perhaps some bias with true mass even in the absence of measurement errors for example caused by cluster triaxiality and large scale structure. Knowledge of the bias and scatter values is fundamental for both cluster cosmology and astrophysics. In this paper we show that the intrinsic scatter of a mass proxy can be constrained by measurements of the gas fraction because masses with higher values of intrinsic scatter with true mass produce more scattered gas fractions. Moreover, the relative bias of two mass estimates can be constrained by comparing the mean gas fraction at the same (nominal) cluster mass. Our observational study addresses the scatter between caustic (i.e., dynamically estimated) and true masses, and the relative bias of caustic and hydrostatic masses. For these purposes, we used the X-ray Unbiased Cluster Sample, a cluster sample selected independently from the intracluster medium content with reliable masses: 34 galaxy clusters in the nearby (0.050 < z < 0.135) Universe, mostly with 14 < log M500/M . 14.5, and with caustic masses. We found a 35% scatter between caustic and true masses. Furthermore, we found that the relative bias between caustic and hydrostatic masses is small, 0.06 ± 0.05 dex, improving upon past measurements. The small scatter found confirms our previous measurements of a highly variable amount of feedback from cluster to cluster, which is the cause of the observed large variety of core-excised X-ray luminosities and gas masses.
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    MeerKLASS L-band deep-field intensity maps: entering the H I dominated regime
    (Oxford University Press, 2025) Bull, Philip; Camera, Stefano; Engelbrecht, Brandon; Fonseca, José; Irfan, Melis; Li, Yichao; Pourtsidou, Alkistis; Santos, Mario; Spinelli, Marta; Wang, Jingying; Witzemann, Amadeus
    We present results from MeerKAT single-dish HI intensity maps, the final observations to be performed in L-band in the MeerKAT Large Area Synoptic Survey (MeerKLASS) campaign. The observations represent the deepest single-dish HI intensity maps to date, produced from 41 repeated scans over 236 deg2, providing 62 h of observational data for each of the 64 dishes before flagging. By introducing an iterative self-calibration process, the estimated thermal noise of the reconstructed maps is limited to ∼ 1.21 mK (1.2 × the theoretical noise level). This thermal noise will be subdominant relative to the HI fluctuations on large scales (k ≲ 0.15 h Mpc−1), which demands upgrades to power spectrum analysis techniques, particularly for covariance estimation. In this work, we present the improved MeerKLASS analysis pipeline, validating it on both a suite of mock simulations and a small sample of overlapping spectroscopic galaxies from the Galaxy And Mass Assembly (GAMA) survey. Despite only overlapping with ∼ 25 per cent of the MeerKLASS deep field, and a conservative approach to covariance estimation, we still obtain a > 4 σ detection of the cross-power spectrum between the intensity maps and the 2269 galaxies at the narrow redshift range 0.39 < z < 0.46. We briefly discuss the HI autopower spectrum from these data, the detection of which will be the focus of follow-up work. For the first time with MeerKAT single-dish intensity maps, we also present evidence of HI emission from stacking the maps onto the positions of the GAMA galaxies.