arXiv subjects

Weitian Li

Publications and source records attributed to Weitian Li.

12 recordsLinked to original sources

arXiv2020

Pre-selection of the Candidate Fields for Deep Imaging of the Epoch of Reionization with SKA1-Low

The Square Kilometre Array (SKA) will be the first low-frequency instrument with the capability to directly image the structures of the Epoch of Reionization (EoR). Indeed, deep imaging of the EoR over 5 targeted fields of 20 square degrees each has been selected as the highest priority science objective for SKA1. Aiming at preparing for this highly challenging observation, we perform an extensive pre-selection of the `quietest' and `cleanest' candidate fields in the southern sky to be suited for deep imaging of the EoR using existing catalogs and observations over a broad frequency range. The candidate fields should meet a number of strict criteria to avoid contaminations from foreground structures and sources. The candidate fields should also exhibit both the lowest average surface brightness and smallest variance to ensure uniformity and high quality deep imaging over the fields. Our selection eventually yields a sample of 7 `ideal' fields of 20 square degrees in the southern sky that could be targeted for deep imaging of the EoR. Finally, these selected fields are convolved with the synthesized beam of SKA1-low stations to ensure that the effect of sidelobes from the far field bright sources is also weak.

astro-ph.IM↗

arXiv2019

Modelling cosmic ray electron physics in cosmological smoothed particle hydrodynamics simulation

Cosmic ray electron (CRE) acceleration and cooling are important physical processes in astrophysics. We develop an approximative framework to treat CRE physics in the parallel smoothed particle hydrodynamics code Gadget-3. In our methodology, the CRE spectrum of each fluid element is approximated by a single power-law distribution with spatially varying amplitude, upper cut-off, lower cut-off, and spectral index. We consider diffusive shock acceleration to be the source of injection, and oppositely the sinking processes is attributed to synchrotron radiation, inverse Compton scatters, and Coulomb scatters. The adiabatic gains and losses are also included. We show that our formalism produces the energy and pressure with an accuracy of $ > 90\%$ for a free cooling CRE spectrum. Both slope and intensity of the radio emission computed from the CRE population given by our method in cosmological hydro-simulation coincide well with observations, and our results also show that relaxed clusters have lower fluxes. Finally, we investigate several impacts of the CRE processes on the cosmological hydro-simulation, we find that: (1) the pressure of the CRE spectrum is very small and can be ignored in hydro-simulation, (2) the impacts of the CRE processes on the gas phase-space state of hydro-simulation is up to $3\%$, (3) the CRE processes induce a $5\%$ influence on the mass function in the mass range $10^{12} -10^{13} h^{-1} M_{\odot}$, (4) The gas temperature of massive galaxy cluster is influenced by the CRE processes up to $\sim 10\%$.

astro-ph.HE↗

arXiv2019

Contribution of Radio Halos to the Foreground for SKA EoR Experiments

The overwhelming foreground contamination is one of the primary impediments to probing the Epoch of Reionization (EoR) through measuring the redshifted 21 cm signal. Among various foreground components, radio halos are less studied and their impacts on the EoR observations are still poorly understood. In this work, we employ the Press-Schechter formalism, merger-induced turbulent re-acceleration model, and the latest SKA1-Low layout configuration to simulate the SKA "observed" images of radio halos. We calculate the one-dimensional power spectra from simulated images and find that radio halos can be about $10^4$, $10^3$ and $10^{2.5}$ times more luminous than the EoR signal on scales of $0.1\,\text{Mpc}^{-1} < k < 2\,\text{Mpc}^{-1}$ in the 120-128, 154-162, and 192-200 MHz bands, respectively. By examining the two-dimensional power spectra inside properly defined EoR windows, we find that the power leaked by radio halos can still be significant, as the power ratios of radio halos to the EoR signal on scales of $0.5\,\text{Mpc}^{-1} \lesssim k \lesssim 1\,\text{Mpc}^{-1}$ can be up to about 230-800%, 18-95%, and 7-40% in the three bands, when the 68% uncertainties caused by the variation of the number density of bright radio halos are considered. Furthermore, we find that radio halos located inside the far side-lobes of the station beam can also impose strong contamination within the EoR window. In conclusion, we argue that radio halos are severe foreground sources and need serious treatments in future EoR experiments.

astro-ph.CO↗

arXiv2019

Separating the EoR Signal with a Convolutional Denoising Autoencoder: A Deep-learning-based Method

When applying the foreground removal methods to uncover the faint cosmological signal from the epoch of reionization (EoR), the foreground spectra are assumed to be smooth. However, this assumption can be seriously violated in practice since the unresolved or mis-subtracted foreground sources, which are further complicated by the frequency-dependent beam effects of interferometers, will generate significant fluctuations along the frequency dimension. To address this issue, we propose a novel deep-learning-based method that uses a 9-layer convolutional denoising autoencoder (CDAE) to separate the EoR signal. After being trained on the SKA images simulated with realistic beam effects, the CDAE achieves excellent performance as the mean correlation coefficient ($\barρ$) between the reconstructed and input EoR signals reaches $0.929 \pm 0.045$. In comparison, the two representative traditional methods, namely the polynomial fitting method and the continuous wavelet transform method, both have difficulties in modelling and removing the foreground emission complicated with the beam effects, yielding only $\barρ_{\text{poly}} = 0.296 \pm 0.121$ and $\barρ_{\text{cwt}} = 0.198 \pm 0.160$, respectively. We conclude that, by hierarchically learning sophisticated features through multiple convolutional layers, the CDAE is a powerful tool that can be used to overcome the complicated beam effects and accurately separate the EoR signal. Our results also exhibit the great potential of deep-learning-based methods in future EoR experiments.

astro-ph.IM↗

arXiv2019

A Machine Learning Based Morphological Classification of 14,245 Radio AGNs Selected From The Best-Heckman Sample

We present a morphological classification of 14,245 radio active galactic nuclei (AGNs) into six types, i.e., typical Fanaroff--Riley Class I / II (FRI/II), FRI/II-like bent-tailed, X-shaped radio galaxy, and ringlike radio galaxy, by designing a convolutional neural network (CNN) based autoencoder, namely MCRGNet, and applying it to a labeled radio galaxy (LRG) sample containing 1442 AGNs and an unlabeled radio galaxy (unLRG) sample containing 14,245 unlabeled AGNs selected from the Best--Heckman sample. We train MCRGNet and implement the classification task by a three-step strategy, i.e., pre-training, fine-tuning, and classification, which combines both unsupervised and supervised learnings. A four-layer dichotomous tree is designed to classify the radio AGNs, which leads to a significantly better performance than the direct six-type classification. On the LRG sample, our MCRGNet achieves a total precision of $\sim 93\%$ and an averaged sensitivity of $\sim 87\%$, which are better than those obtained in previous works. On the unLRG sample, whose labels have been human-inspected, the neural network achieves a total precision of $\sim 80\%$. Also, using the Sloan Digital Sky Survey (SDSS) Data Release 7 (DR7) to calculate the $r$-band absolute magnitude ($M_\mathrm{opt}$) and using the flux densities to calculate the radio luminosity ($L_\mathrm{radio}$), we find that the distributions of the unLRG sources on the $L_\mathrm{radio}$--$M_\mathrm{opt}$ plane do not show an apparent redshift evolution and could confirm with a sufficiently large sample that there could not exist an abrupt separation between FRIs and FRIIs as reported in some previous works.

astro-ph.GA↗

arXiv2018

A Study of the Merger History of the Galaxy Group HCG 62 Based on X-Ray Observations and SPH Simulations

We choose the bright compact group HCG 62, which was found to exhibit both excess X-ray emission and high Fe abundance to the southwest of its core, as an example to study the impact of mergers on chemical enrichment in the intragroup medium. We first reanalyze the high-quality Chandra and XMM-Newton archive data to search for the evidence for additional SN II yields, which is expected as a direct result of the possible merger-induced starburst. We reveal that, similar to the Fe abundance, the Mg abundance also shows a high value in both the innermost region and the southwest substructure, forming a high-abundance plateau, meanwhile all the SN Ia and SN II yields show rather flat distributions in $>0.1r_{200}$ in favor of an early enrichment. Then we carry out a series of idealized numerical simulations to model the collision of two initially isolated galaxy groups by using the TreePM-SPH GADGET-3 code. We find that the observed X-ray emission and metal distributions, as well as the relative positions of the two bright central galaxies with reference to the X-ray peak, can be well reproduced in a major merger with a mass ratio of 3 when the merger-induced starburst is assumed. The `best-match' snapshot is pinpointed after the third pericentric passage when the southwest substructure is formed due to gas sloshing. By following the evolution of the simulated merging system, we conclude that the effects of such a major merger on chemical enrichment are mostly restricted within the core region when the final relaxed state is reached.

astro-ph.HE↗

arXiv2018

Detection of a Double Relic in the Torpedo Cluster: SPT-CL J0245-5302

The Torpedo cluster, SPT-CL J0245-5302 (S0295) is a massive, merging cluster at a redshift of z = 0.300, which exhibits a strikingly similar morphology to the Bullet cluster 1E 0657-55.8 (z = 0.296), including a classic bow shock in the cluster's intra-cluster medium revealed by Chandra X-ray observations. We present Australia Telescope Compact Array data centred at 2.1 GHz and Murchison Widefield Array data at frequencies between 72 MHz and 231 MHz which we use to study the properties of the cluster. We characterise a number of discrete and diffuse radio sources in the cluster, including the detection of two previously unknown radio relics on the cluster periphery. The average spectral index of the diffuse emission between 70 MHz and 3.1 GHz is $α=-1.63_{-0.10}^{+0.10}$ and a radio-derived Mach number for the shock in the west of the cluster is calculated as M = 2.04. The Torpedo cluster is thus as a double relic system at moderate redshift.

astro-ph.GA↗

arXiv2018

Radio Galaxy Morphology Generation Using DNN Autoencoder and Gaussian Mixture Models

The morphology of a radio galaxy is highly affected by its central active galactic nuclei (AGN), which is studied to reveal the evolution of the super massive black hole (SMBH). In this work, we propose a morphology generation framework for two typical radio galaxies namely Fanaroff-Riley type-I (FRI) and type-II (FRII) with deep neural network based autoencoder (DNNAE) and Gaussian mixture models (GMMs). The encoder and decoder subnets in the DNNAE are symmetric aside a fully-connected layer namely code layer hosting the extracted feature vectors. By randomly generating the feature vectors later with a three-component Gaussian Mixture models, new FRI or FRII radio galaxy morphologies are simulated. Experiments were demonstrated on real radio galaxy images, where we discussed the length of feature vectors, selection of lost functions, and made comparisons on batch normalization and dropout techniques for training the network. The results suggest a high efficiency and performance of our morphology generation framework. Code is available at: https://github.com/myinxd/dnnae-gmm.

cs.CV↗

arXiv2017

X-ray Astronomical Point Sources Recognition Using Granular Binary-tree SVM

The study on point sources in astronomical images is of special importance, since most energetic celestial objects in the Universe exhibit a point-like appearance. An approach to recognize the point sources (PS) in the X-ray astronomical images using our newly designed granular binary-tree support vector machine (GBT-SVM) classifier is proposed. First, all potential point sources are located by peak detection on the image. The image and spectral features of these potential point sources are then extracted. Finally, a classifier to recognize the true point sources is build through the extracted features. Experiments and applications of our approach on real X-ray astronomical images are demonstrated. comparisons between our approach and other SVM-based classifiers are also carried out by evaluating the precision and recall rates, which prove that our approach is better and achieves a higher accuracy of around 89%.

cs.CV↗

arXiv2016

A Chandra Study of the Image Power Spectra of 41 Cool Core and Non-Cool Core Galaxy Clusters

In this work we propose a new diagnostic to segregate cool core (CC) clusters from non-cool core (NCC) clusters by studying the two-dimensional power spectra of the X-ray images observed with the Chandra X-ray observatory. Our sample contains 41 members ($z=0.01\sim 0.54$), which are selected from the Chandra archive when a high photon count, an adequate angular resolution, a relatively complete detector coverage, and coincident CC-NCC classifications derived with three traditional diagnostics are simultaneously guaranteed. We find that in the log-log space the derived image power spectra can be well represented by a constant model component at large wavenumbers, while at small wavenumbers a power excess beyond the constant component appears in all clusters, with a clear tendency that the excess is stronger in CC clusters. By introducing a new CC diagnostic parameter, i.e., the power excess index (PEI), we classify the clusters in our sample and compare the results with those obtained with three traditional CC diagnostics. We find that the results agree with each other very well. By calculating the PEI values of the simulated clusters, we find that the new diagnostic works well at redshifts up to 0.5 for intermediately sized and massive clusters with a typical Chandra or XMM pointing observation. The new CC diagnostic has several advantages over its counterparts, e.g., it is free of the effects of the commonly seen centroid shift of the X-ray halo caused by merger event, and the corresponding calculation is straightforward, almost irrelevant to the complicated spectral analysis.

astro-ph.CO↗

arXiv2015

A Chandra Study of Radial Temperature Profiles of the Intra-Cluster Medium in 50 Galaxy Clusters

In order to investigate the spatial distribution of the ICM temperature in galaxy clusters in a quantitative way and probe the physics behind, we analyze the X-ray spectra of a sample of 50 galaxy clusters, which were observed with the Chandra ACIS instrument in the past 15 years, and measure the radial temperature profiles out to $0.45r_{500}$. We construct a physical model that takes into account the effects of gravitational heating, thermal history (such as radiative cooling, AGN feedback, and thermal conduction) and work done via gas compression, and use it to fit the observed temperature profiles by running Bayesian regressions. The results show that in all cases our model provides an acceptable fit at the 68% confidence level. To further validate this model we select nine clusters that have been observed with both Chandra (out to $\gtrsim 0.3r_{500}$) and Suzaku (out to $\gtrsim 1.5r_{500}$), fit their Chandra spectra with our model, and compare the extrapolation of the best-fits with the Suzaku measurements. We find that the model profiles agree with the Suzaku results very well in seven clusters. In the rest two clusters the difference between the model and observation is possibly caused by local thermal substructures. Our study also implies that for most of the clusters the assumption of hydrostatic equilibrium is safe out to at least $0.5r_{500}$, and the non-gravitational interactions between dark matter and its luminous counterpart is consistent with zero.

astro-ph.CO↗

arXiv2012

Exploring the Cosmic Reionization Epoch in Frequency Space: An Improved Approach to Remove the Foreground in 21 cm Tomography

Aiming to correctly restore the redshifted 21 cm signals emitted by the neutral hydrogen during the cosmic reionization processes, we re-examine the separation approaches based on the quadratic polynomial fitting technique in frequency space to investigate whether they works satisfactorily with complex foreground, by quantitatively evaluate the quality of restored 21 cm signals in terms of sample statistics. We construct the foreground model to characterize both spatial and spectral substructures of the real sky, and use it to simulate the observed radio spectra. By comparing between different separation approaches through statistical analysis of restored 21 cm spectra and corresponding power spectra, as well as their constraints on the mean halo bias $b$ and average ionization fraction $x_e$ of the reionization processes, at $z=8$ and the noise level of 60 mK we find that, although the complex foreground can be well approximated with quadratic polynomial expansion, a significant part of Mpc-scale components of the 21 cm signals (75% for $\gtrsim 6h^{-1}$ Mpc scales and 34% for $\gtrsim 1h^{-1}$ Mpc scales) is lost because it tends to be mis-identified as part of the foreground when single-narrow-segment separation approach is applied. The best restoration of the 21 cm signals and the tightest determination of $b$ and $x_e$ can be obtained with the three-narrow-segment fitting technique as proposed in this paper. Similar results can be obtained at other redshifts.

astro-ph.CO↗