分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： In the procedure of constraining the cosmological parameters with the observational Hubble data and the type Ia supernova data, the combination of Masked Autoregressive Flow and Denoising Autoencoder can perform a good result. The above combination extracts the features from OHD with DAE, and estimates the posterior distribution of cosmological parameters with MAF. We ask whether we can find a better tool to compress large data in order to gain better results while constraining the cosmological parameters. Information maximising neural networks, a kind of simulation-based machine learning technique, was proposed at an earlier time. In a series of numerical examples, the results show that IMNN can find optimal, non-linear summaries robustly. In this work, we mainly compare the dimensionality reduction capabilities of IMNN and DAE. We use IMNN and DAE to compress the data into different dimensions and set different learning rates for MAF to calculate the posterior. Meanwhile, the training data and mock OHD are generated with a simple Gaussian likelihood, the spatially flat {\Lambda}CDM model and the real OHD data. To avoid the complex calculation in comparing the posterior directly, we set different criteria to compare IMNN and DAE.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： In the procedure of constraining the cosmological parameters with the observational Hubble data and the type Ia supernova data, the combination of Masked Autoregressive Flow and Denoising Autoencoder can perform a good result. The above combination extracts the features from OHD with DAE, and estimates the posterior distribution of cosmological parameters with MAF. We ask whether we can find a better tool to compress large data in order to gain better results while constraining the cosmological parameters. Information maximising neural networks, a kind of simulation-based machine learning technique, was proposed at an earlier time. In a series of numerical examples, the results show that IMNN can find optimal, non-linear summaries robustly. In this work, we mainly compare the dimensionality reduction capabilities of IMNN and DAE. We use IMNN and DAE to compress the data into different dimensions and set different learning rates for MAF to calculate the posterior. Meanwhile, the training data and mock OHD are generated with a simple Gaussian likelihood, the spatially flat {\Lambda}CDM model and the real OHD data. To avoid the complex calculation in comparing the posterior directly, we set different criteria to compare IMNN and DAE.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： The $L-\sigma$ relation of HII galaxies (HIIGx) calibrated by a distance indicator is a reliable standard candle for measuring the Hubble constant $H_0$. The most straightforward calibration technique anchors them with the first tier of distance ladders from the same galaxies. Recently another promising method that uses the cosmological model-independent Cosmic Chronometers (CC) as a calibrator has been proposed. We promote this technique by removing the assumptions about the cosmic flatness and using a non-parametric Artificial Neural Network for the data reconstruction process. We observe a correlation between the cosmic curvature density parameter and the slope of the $L-\sigma$ relation, thereby improving the reliability of the calibration. Using the calibrated HIIGx Hubble diagram, we obtain a Type Ia Supernovae Hubble diagram free of the conventional assumption about $H_0$. Finally we get a value of $H_0=65.9_{-2.9}^{+3.0} \mathrm{km s^{-1} Mpc^{-1}}$, which is compatible with latest Planck18 measurement.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： The errors of cosmological data generated from complex processes, such as the observational Hubble parameter data (OHD) and the Type Ia supernova (SN Ia) data, cannot be accurately modeled by simple analytical probability distributions, e.g. Gaussian distribution. To constrain cosmological parameters from these data, likelihood-free inference is usually used to bypass the direct calculation of the likelihood. In this paper, we propose a new procedure to perform likelihood-free cosmological inference using two artificial neural networks (ANN), the Masked Autoregressive Flow (MAF) and the denoising autoencoder (DAE). Our procedure is the first to use DAE to extract features from data, in order to simplify the structure of MAF needed to estimate the posterior. Tested on simulated Hubble parameter data with a simple Gaussian likelihood, the procedure shows the capability of extracting features from data and estimating posterior distributions without the need of tractable likelihood. We demonstrate that it can accurately approximate the real posterior, achieve performance comparable to the traditional MCMC method, and the MAF gets better training results for small number of simulation when the DAE is added. We also discuss the application of the proposed procedure to OHD and Pantheon SN Ia data, and use them to constrain cosmological parameters from the non-flat $\Lambda$CDM model. For SNe Ia, we use fitted light curve parameters to find constraints on $H_0,\Omega_m,\Omega_\Lambda$ similar to relevant work, using less empirical distributions. In addition, this work is also the first to use Gaussian process in the procedure of OHD simulation.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Damped Lyman-$\alpha$ Absorber(DLA) of HI 21cm system is an ideal probe to directly measure cosmic acceleration in real-time cosmology via Sandage-Loeb(SL) test. During short observations toward two DLAs in the commissioning progress of FAST, we manage to exhibit an HI 21cm absorption feature from PKS1413+135 spectrum in one epoch with our highest resolution up to 100 Hz, preliminarily validating the frequency consistency under different resolutions and bandwidths. We make a Gaussian fitting to extract the spectral features, introduce two theoretical indicators to describe the fitted velocity uncertainty, and ultimately give a mean redshift and its constraint of $z_\mathrm{M}=0.24670045\pm0.00000036$ in accord with most literature. But our redshift error of the target is still three magnitudes higher than the level we can reach the drift signal. Though our first preparation has some flaws in time recording and diode settings, it still proves the correctness of our data process. Confined by limited observing time, we do not strech FAST's ability to obtain a better velocity constraint, so further researchs are needed and in schedule. With fine sensitivity and improving spectral resolution, such observations in FAST could have reasonable possibility to explore cosmic acceleration in late time universe practically.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Damped Lyman-$\alpha$ Absorber(DLA), or HI 21cm absorber, is an important probe to directly measure the acceleration of spectroscopic velocity $v_\mathrm{S}$ via the Sandage-Loeb(SL) effect. Confined by the shortage of actual DLAs samples and the coarse background radio sources assignment, the detectable amount of Damped Lyman-$\alpha$ Absorption System(DLAS) is ambiguous in most cases. After differing the unmeasurable, global and physical $\ddot{a}$ from the observed and local $\dot{v}_\mathrm{S}$, we make a statistical investigation of the components of DLASs. We use Kernel Density Estimation(KDE) to depict a general redshift distribution of background radio sources via three radio deep survey datasets, CENSORS, LBDS-Hercules and CoNFIG-4, and provide a multi-Gaussian expression. Testing the generation process of DLA redshift number density in literature, we try to make a modified power law fitting of low-redshift($z\lesssim1.65$) DLA preselected by MgII absorption and analysis its defects. Finally, we present a simple DLASs number estimation of FAST, ASKAP and SKA-Mid when considering a blind HI absorption survey with our derived radio number density and the previous DLA one in literature. For comparability, our FAST prediction gives a practical amount of 100, and an optimistic amount of 470, while our latter amount and previous predictions are within an order of magnitude.
分类： 光学 >> 量子光学 提交时间： 2023-02-19
Tingting Zhang
Yongjie Sun
Pengpeng Wang
Yufeng Qiu
Chenxi Wang
Xiaohui Du
Shaokai Li
Haixu Liu
Tongwei Chu
Cunguang Zhu
摘要： This paper proposes a novel calibration-free wavelength modulated spectroscopy (WMS) spectral fitting technique based on the firefly algorithm. The technique by simulating the information interaction behavior between fireflies to achieve the retrieval of gas concentration and laser parameters. Contrasted with the spectral fitting technique based on the classical Levenberg-Marquardt (LM) algorithm, the retrieval of gas concentrations by this technique is weakly dependent on the pre-characterization of the laser parameters. We select the P(13) absorption line of C2H2 at 1532.82 nm as the target spectra and compare the performance of two optimization method (LM and firefly) on gas concentration and laser parameters retrieval by simulation. The simulation results show that the spectral fitting technique based on the firefly algorithm performs better in terms of convergence speed and fitting accuracy, especially in the multi-parameter model without exact characterization.
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