arXiv subjects

Jun-Qing Xia

Publications and source records attributed to Jun-Qing Xia.

At least 37 records · Page 2Linked to original sources

arXiv2023

Recovering Cosmic Microwave Background Polarization Signals with Machine Learning

Primordial B-mode detection is one of the main goals of current and future cosmic microwave background (CMB) experiments. However, the weak B-mode signal is overshadowed by several Galactic polarized emissions, such as thermal dust emission and synchrotron radiation. Subtracting foreground components from CMB observations is one of the key challenges in searching for the primordial B-mode signal. Here, we construct a deep convolutional neural network (CNN) model, called \texttt{CMBFSCNN} (Cosmic Microwave Background Foreground Subtraction with CNN), which can cleanly remove various foreground components from simulated CMB observational maps at the sensitivity of the CMB-S4 experiment. Noisy CMB Q (or U) maps are recovered with a mean absolute difference of $0.018 \pm 0.023\ μ$K (or $0.021 \pm 0.028\ μ$K). To remove the residual instrumental noise from the foreground-cleaned map, inspired by the needlet internal linear combination method, we divide the whole data set into two ``half-split maps,'' which share the same sky signal, but have uncorrelated noise, and perform a cross-correlation technique to reduce the instrumental noise effects at the power spectrum level. We find that the CMB EE and BB power spectra can be precisely recovered with significantly reduced noise effects. Finally, we apply this pipeline to current Planck observations. As expected, various foregrounds are cleanly removed from the Planck observational maps, with the recovered EE and BB power spectra being in good agreement with the official Planck results.

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arXiv2022

Searching for Signal of Primordial Black Hole from CMB Lensing and $γ$-ray Emissions

In this $\textit{Letter}$, we search for the signal of the primordial black holes (PBHs) by correlating the $γ$-ray emissions in the MeV energy band produced by the Hawking evaporation and the lensing effect of the cosmic microwave background (CMB). We use the conservative case of the astrophysical model as much as possible in the calculations, since the potential astrophysical origins dominate the observed emission in the MeV energy band. By carefully discussing the appropriate energy bands corresponding to different PBHs masses, it is worth expecting a tight constraint on the fraction of the Schwarzschild PBHs in the mass range of $10^{16} - 5\times10^{17}\,{\rm g}$, by simulations of the sensitivity of the future CMB-S4 project and the $γ$-ray telescope e-ASTROGAM. Furthermore, we also consider the PBHs model with spins, and find that the constraining ability of the PBHs fraction from the correlation between CMB lensing and $γ$-ray emissions can be improved by another order of magnitude, which could importantly fill the gaps with PBHs fraction limits in the mass range of $5\times 10^{17} - 2\times 10^{18}\,{\rm g}$.

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arXiv2022

Likelihood-free Inference with Mixture Density Network

In this work, we propose using the mixture density network (MDN) to estimate cosmological parameters. We test the MDN method by constraining parameters of the $Λ$CDM and $w$CDM models using Type Ia supernovae and the power spectra of the cosmic microwave background. We find that the MDN method can achieve the same level of accuracy as the Markov Chain Monte Carlo method, with a slight difference of $\mathcal{O}(10^{-2}σ)$. Furthermore, the MDN method can provide accurate parameter estimates with $\mathcal{O}(10^3)$ forward simulation samples, which are useful for complex and resource-consuming cosmological models. This method can process either one data set or multiple data sets to achieve joint constraints on parameters, extendable for any parameter estimation of complicated models in a wider scientific field. Thus, the MDN provides an alternative way for likelihood-free inference of parameters.

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arXiv2022

Reconstructing Functions and Estimating Parameters with Artificial Neural Networks: A Test with the Hubble Parameter and SNe Ia

In this work, we propose a new nonparametric approach for reconstructing a function from observational data using an Artificial Neural Network (ANN), which has no assumptions about the data and is a completely data-driven approach. We test the ANN method by reconstructing functions of the Hubble parameter measurements $H(z)$ and the distance-redshift relation $D_L(z)$ of Type Ia supernovae. We find that both $H(z)$ and $D_L(z)$ can be reconstructed with high accuracy. Furthermore, we estimate cosmological parameters using the reconstructed functions of $H(z)$ and $D_L(z)$ and find the results are consistent with those obtained using the observational data directly. Therefore, we propose that the function reconstructed by ANN can represent the actual distribution of observational data and can be used for parameter estimation in further cosmological research. In addition, we present a new strategy for training and evaluating the neural network, and a code for reconstructing functions using ANN has been developed and is available for download.

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arXiv2022

Probing the Cosmological Principle with the CSST Photometric Survey

The cosmological principle states that our Universe is statistically homogeneous and isotropic at large scales. However, due to the relative motion of the Solar System, an additional kinematic dipole can be detected in the distribution of galaxies, which should be consistent with the dipole observed in the cosmic microwave background temperature. In this paper, we forecast the mock number count maps from the China Space Station Telescope photometric survey to reconstruct the kinematic dipole. Using the whole photometric mock data, we obtain a positive evidence for the dipole signal detection at 3 sigma confidence level, and the significance would be increased to 4 sigma when we only use the high-redshift samples with z = 1.8 to 4. This result can provide a good consistency check between the kinematic dipoles measured in the CMB and that from the large scale structure, which can help us to verify the basic cosmological principle.

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arXiv2022

Constraints on the Helium Abundance from Fast Radio Bursts

Through the relationship between dispersion measures (DM) and redshifts, fast radio bursts (FRBs) are considered to be very promising cosmological probes. In this paper, we attempted to use the DM-z relationship of FRBs to study the helium abundance ($Y_{\rm He}$) in the universe. First, we used 17 current FRBs with known redshifts for our study. Due to their low redshifts and the strong degeneracy between $Y_{\rm He}$ and $Ω_bh^2$, however, this catalog could not provide a good constraint on the helium abundance. Then, we simulated 500 low redshift FRB mock data with $z\in[0,\,1.5]$ to forecast the constraining ability on $Y_{\rm He}$. In order to break the degeneracy between $Y_{\rm He}$ and $Ω_bh^2$ further, we introduced the shift parameters of the Planck measurement $(R,l_A,Ω_bh^2)$ as a prior, where $Ω_bh^2$ represents the baryon density parameter, and $R$ and $l_A$ correspond to the scaled distance to recombination and the angular scale of the sound horizon at recombination, respectively. We obtained the standard deviation for the helium abundance: $σ({Y_{\rm He}}) = 0.025$. Finally, we considered 2000 higher redshift FRB data with the redshift distribution of $[0,\,3]$ and found that the constraining power for $Y_{\rm He}$ would be improved by more than 2 times, $σ({Y_{\rm He}}) = 0.011$, which indicates that the FRB data with high redshift can provide a better constraint on the helium abundance. Hopefully, large FRB samples with high redshift from the Square Kilometre Array can provide high-precision measurements of the helium abundance in the near future.

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arXiv2022

Recovering the CMB Signal with Machine Learning

The cosmic microwave background (CMB), carrying the inhomogeneous information of the very early universe, is of great significance for understanding the origin and evolution of our universe. However, observational CMB maps contain serious foreground contaminations from several sources, such as galactic synchrotron and thermal dust emissions. Here, we build a deep convolutional neural network (CNN) to recover the tiny CMB signal from various huge foreground contaminations. Focusing on the CMB temperature fluctuations, we find that the CNN model can successfully recover the CMB temperature maps with high accuracy, and that the deviation of the recovered power spectrum $C_\ell$ is smaller than the cosmic variance at $\ell>10$. We then apply this method to the current Planck observation, and find that the recovered CMB is quite consistent with that disclosed by the Planck collaboration, which indicates that the CNN method can provide a promising approach to the component separation of CMB observations. Furthermore, we test the CNN method with simulated CMB polarization maps based on the CMB-S4 experiment. The result shows that both the EE and BB power spectra can be recovered with high accuracy. Therefore, this method will be helpful for the detection of primordial gravitational waves in current and future CMB experiments. The CNN is designed to analyze two-dimensional images, thus this method is not only able to process full-sky maps, but also partial-sky maps. Therefore, it can also be used for other similar experiments, such as radio surveys like the Square Kilometer Array.

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arXiv2022

ECoPANN: A Framework for Estimating Cosmological Parameters using Artificial Neural Networks

In this work, we present a new method to estimate cosmological parameters accurately based on the artificial neural network (ANN), and a code called ECoPANN (Estimating Cosmological Parameters with ANN) is developed to achieve parameter inference. We test the ANN method by estimating the basic parameters of the concordance cosmological model using the simulated temperature power spectrum of the cosmic microwave background (CMB). The results show that the ANN performs excellently on best-fit values and errors of parameters, as well as correlations between parameters when compared with that of the Markov Chain Monte Carlo (MCMC) method. Besides, for a well-trained ANN model, it is capable of estimating parameters for multiple experiments that have different precisions, which can greatly reduce the consumption of time and computing resources for parameter inference. Furthermore, we extend the ANN to a multibranch network to achieve a joint constraint on parameters. We test the multibranch network using the simulated temperature and polarization power spectra of the CMB, Type Ia supernovae, and baryon acoustic oscillations, and almost obtain the same results as the MCMC method. Therefore, we propose that the ANN can provide an alternative way to accurately and quickly estimate cosmological parameters, and ECoPANN can be applied to the research of cosmology and even other broader scientific fields.

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arXiv2021

Constraining the anisotropy of the Universe with the X-ray and UV fluxes of quasars

We test the anisotropy in the Finslerian cosmological model with the X-ray and ultraviolet (UV) fluxes of 808 quasars. The dipole amplitude is $A_D=0.302_{ -0.124}^{ +0.185}$ and the dipole direction points towards $(l, b) = ( 288.92_{~ -28.80^{\circ}}^{^{\circ}+23.74^{\circ}}, 6.10_{~ -16.40^{\circ}}^{^{\circ} +16.55^{\circ}} )$. We find that the dipole direction from the X-ray and UV fluxes of quasars is very close to the dipole direction given by the "Joint Light-curve Analysis" (JLA) compilation in the Finslerian cosmological model and the angular difference between the two dipole directions is only $10.44^{\circ}$. We also find the angular difference between the dipole direction from the 808 quasars in the Finslerian cosmological model and ones from the supernovae of type Ia (SNe Ia) samples in the dipole-modulated $Λ$CDM model is around $30^{\circ}$. Six gravitationally lensed quasars are considered to investigate the Hubble constant $H_0$ in the Finslerian cosmological model. We get a slightly smaller $H_0$ than the result given by the six gravitationally lensed quasars. Finally, we forecast the future constraints on the dipole parameters with the X-ray and UV fluxes of quasars. As the number of simulations increases, the precisions of the parameters related to anisotropy in the Finslerian cosmological model improve significantly. The X-ray and UV fluxes of quasars have a promising future as a probe of anisotropy in Finsler spacetime.

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arXiv2021

Reconstruction of Reionization History through Dispersion Measure of Fast Radio Bursts

In this paper, we study the evolution of the ionization fraction $x_e(z)$ during the epoch of reionization by using the dispersion measurements (DMs) of fast radio bursts (FRBs). Different from the previous studies, here we turn to consider the large-scale clustering information of observed DMs of FRB catalog, which only needs the rough redshift distribution, instead of the exact redshift information of each FRB. Firstly, we consider the instantaneous ``\texttt{tanh}'' model for $x_e(z)$ and find that including the auto-correlation information of the mock catalog, about $10^4$ FRBs with the intrinsic DM scatter of 100 $\rm pc/cm^3$ spanning 20\% of all sky, could significantly improve the constraint on the width $Δ_z$ of the model, when comparing with that from the CMB data alone. The evolution shape of the ionization fraction will be tightly narrowed, namely the duration of the epoch of reionization has been shrunk, $z_{\rm dur}<2.24$ (95\% C.L.). Furthermore, we also use another redshift-asymmetric reionization model and obtain that the FRB mock catalog could measure the ionization fraction at $z=6$ precisely with the $1σ$ error $Δx_e(z=6)=0.012$, which means that the large-scale clustering information of observed DMs of FRB catalog is very sensitive to the ionization fraction of the end of reionization epoch. We conclude that the observation of high-redshift FRBs could be a complementary probe to study the reionization history in the future.

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arXiv2021

Reconstruction of Baryon Fraction in Intergalactic Medium through Dispersion Measurements of Fast Radio Bursts

Fast radio bursts (FRBs) probe the total column density of free electrons in the intergalactic medium (IGM) along the path of propagation though the dispersion measures (DMs) which depend on the baryon mass fraction in the IGM, i.e., $f_{\rm IGM}$. In this letter, we investigate the large-scale clustering information of DMs to study the evolution of $f_{\rm IGM}$. When combining with the Planck 2018 measurements, we could give tight constraints on the evolution of $f_{\rm IGM}(z)$ from about $10^4$ FRBs with the intrinsic DM scatter of $30(1+z)~ \rm pc/cm^3$ spanning 80% of the sky and redshift range $z=0-3$. Firstly, we consider the Taylor expansion of $f_{\rm IGM}(z)$ up to second order, and find that the mean relative standard deviation $σ(f_{\rm IGM})\equiv\left\langle σ[f_{\rm IGM}(z)] /f_{\rm IGM}(z) \right\rangle$ is about 7.2%. In order to alleviate the dependence on fiducial model, we also adopt a non-parametric methods in this work, the local principle component analysis. We obtain the consistent, but weaker constraints on the evolution of $f_{\rm IGM}(z)$, namely the mean relative standard deviation $σ(f_{\rm IGM})$ is 24.2%. With the forthcoming surveys, this could be a complimentary method to investigate the baryon mass fraction in the IGM.

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arXiv2021

Machine Learning the Cosmic Curvature in a Model-independent Way

In this work, we achieve the determination of the cosmic curvature $Ω_K$ in a cosmological model-independent way, by using the Hubble parameter measurements $H(z)$ and type Ia supernovae (SNe Ia). In our analysis, two nonlinear interpolating tools are used to reconstruct the Hubble parameter, one is the Artificial Neural Network (ANN) method, and the other is the Gaussian process (GP) method. We find that $Ω_K$ based on the GP method can be greatly influenced by the prior of $H_0$, while the ANN method can overcome this. Therefore, the ANN method may have more advantages than GP in the measurement of the cosmic curvature. Based on the ANN method, we find a spatially open universe is preferred by the current $H(z)$ and SNe Ia data, and the difference between our result and the value inferred from Planck CMB is $1.6σ$. In order to test the reliability of the ANN method, and the potentiality of the future gravitational waves (GW) standard sirens in the measurement of the cosmic curvature, we constrain $Ω_K$ using the simulated Hubble parameter and GW standard sirens in a model-independent way. We find that the ANN method is reliable and unbiased, and the error of $Ω_K$ is $\sim0.186$ when 100 GW events with electromagnetic counterparts are detected, which is $\sim56\%$ smaller than that constrained from the Pantheon SNe Ia. Therefore, the data-driven method based on ANN has potential in the measurement of the cosmic curvature.

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arXiv2020

Constraints on primordial non-Gaussianity using multitracer technique for skew spectrum

Extracting the bispectrum information from the large scale structure observations is challenging due to the complex models and the computational costs to measure the signal and its covariance. Recently, the skew spectrum was proposed to access parts of the bispectrum information with a more effective way and has been confirmed it can provide complementary information to that enclosed in the power spectrum measurements. In this work, we generalize the theory to apply the multitracer technique and explore its ability to constrain the local type primordial non-Gaussianity. Using the spectra and their covariance estimated from $N$-body simulations, we find the multitracer approach is effective to reduce the cosmic variance noise. The $1σ$ marginalized errors for $b_1^2A_s, n_s$ and $f_{\rm NL}^{\rm loc}$ are reduced by 50\%, 52\% and 73\% comparing with the results using only power spectrum obtained from a single tracer. It indicate that both the skew spectrum and the multitracer technique are useful to constrain the primordial non-Gaussianity with the forthcoming wide-field galaxy surveys.

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arXiv2020

What Can We Learn by Combining the Skew Spectrum and the Power Spectrum?

Clustering of the large scale structure provides complementary information to the measurements of the cosmic microwave background anisotropies through power spectrum and bispectrum of density perturbations. Extracting the bispectrum information, however, is more challenging than it is from the power spectrum due to the complex models and the computational cost to measure the signal and its covariance. To overcome these problems, we adopt a proxy statistic, skew spectrum which is a cross-spectrum of the density field and its quadratic field. By applying a large smoothing filter to the density field, we show the theory fits the simulations very well. With the spectra and their full covariance estimated from $N$-body simulations as our "mock" Universe, we perform a global fits for the cosmological parameters. The results show that adding skew spectrum to power spectrum the $1σ$ marginalized errors for parameters $ b_1^2A_s, n_s$ and $f_{\rm NL}^{\rm loc}$ are reduced by $31\%, 22\%, 44\%$, respectively. This is the answer to the question posed in the title and indicates that the skew spectrum will be a fast and effective method to access complementary information to that enclosed in the power spectrum measurements, especially for the forthcoming generation of wide-field galaxy surveys.

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arXiv2020

Impacts of the simulated standard siren on estimating cosmological parameters

We propose a method based on the process of extracting gravitational wave (GW) parameters from GW signals to simulate the binary neutron-star (BNS) merging events. We simulate 1000 GW standard sirens based on the observation of the Einstein Telescope (ET). Almost all the simulated GW data are in the redshift range of $[0,3]$. The role of the GW standard siren in the inference of the cosmological parameters is investigated. We find that the GW data can help improve the accuracy of cosmological parameters. Moreover, the degeneracy of cosmological parameters is broken by the GW data. The GW standard siren is helpful for the constraint of the cosmological parameters.

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arXiv2020

Searching for Integrated Sachs-Wolfe Effect from Fermi-LAT Diffuse $γ$-ray Map

In this paper, we estimate the cross-correlation power spectra between the Planck 2018 cosmic microwave background (CMB) temperature anisotropy map and the unresolved $γ$-ray background (UGRB) from the 9-years Fermi-Large Area Telescope (LAT) data. In this analysis, we use up to nine energy bins over a wide energy range of [0.631, 1000] GeV from the Fermi-LAT UGRB data. Firstly, we find that the Fermi data with the energy ranges [1.202, 2.290] GeV and [17.38, 36.31] GeV show the positive evidence for the Integrated Sachs-Wolfe (ISW) effect at $1.8σ$ confidence level, and the significance would be increased to $2.7σ$ when using these two energy bins together. Secondly, we apply the single power-law model to normalize the amplitude and use all the nine Fermi energy bins to measure the significance of the ISW effect, we obtained ${\rm A_{amp}}=0.95 \pm 0.53$ ($68\%$ C.L.). For the robustness test, we implement a null hypothesis by randomizing the Fermi mock maps of nine energy bins and obtain the non-detection of ISW effect, which confirms that the ISW signal comes from the Fermi-LAT diffuse $γ$-ray data and is consistent with the standard $Λ$CDM model prediction essentially. We use a cross-correlation coefficient to show the relation between different energy bins. Furthermore, we vary the cut ranges $|b|$ of galactic plane on the mask of Fermi map and carefully check the consequent influence on the ISW signal detection.

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arXiv2020

$H_0$ Reconstruction with Type Ia Supernovae, Baryon Acoustic Oscillation and Gravitational Lensing Time-Delay

There is a persistent $H_0$-tension, now at more than $\gtrsim 4σ$ level, between the local distance ladder value and the \emph{Planck} cosmic microwave background measurement, in the context of flat $Λ$CDM model. We reconstruct $H(z)$ in a cosmological-model-independent way using three low-redshift distance probes including the latest data from baryon acoustic oscillation, Type Ia supernova and four gravitational lensing Time-Delay observations. We adopt general parametric models of $H(z)$ and assume a Gaussian prior on the sound horizon at drag epoch, $r_{\mathrm s}$, from \emph{Planck} measurement. The reconstructed $H_0$ using Pantheon SN Ia and BAO data are consistent with the \emph{Planck} flat $Λ$CDM value. When including the GLTD data, $H_0$ increases mildly, yet remaining discrepant with the local measurement at $\sim 2.5σ$ level. Our reconstructions being blind to the dark sectors at low redshift, we reaffirm the earlier claims that the Hubble tension is not likely to be solved by modifying the energy budget of the low-redshift universe. We further forecast the constraining ability of future realistic mock BAO data from DESI and GLTD data from LSST, combining which, we anticipate that the uncertainty of the inferred $H_0$ would be improved by $\sim 38\%$, reaching $σ_{H_0} \approx 0.56$ uncertainty level.

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arXiv2020

Searching for gamma-ray emission from galaxy clusters at low redshift

We report the identification of a positive cross-correlation signal between the unresolved $γ$-ray emission, measured by the \emph{Fermi} Large Area Telescope, and four different galaxy cluster catalogues. The selected catalogues peak at low-redshift and span different frequency bands, including infrared, optical and X-rays. The signal-to-noise ratio of the detected cross-correlation amounts to 3.5 in the most significant case. We investigate and comment about its possible origin, in terms of compact $γ$-ray emission from AGNs inside clusters or diffuse emission from the intracluster medium. The analysis has been performed by introducing an accurate estimation of the cross-correlation power-spectrum covariance matrix, built with mock realisations of the gamma and galaxy cluster maps. Different methods to produce the mock realizations starting from the data maps have been investigated and compared, identifying suitable techniques which can be generalized to other cross-correlation studies.

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