Bright Foreground Research Articles

Eliminating polarization leakage effect for neutral hydrogen intensity mapping with deep learning

ABSTRACT The neutral hydrogen (H i) intensity mapping (IM) survey is regarded as a promising approach for cosmic large-scale structure studies. A major issue for the H i IM survey is to remove the bright foreground contamination. A key to successfully removing the bright foreground is to well control or eliminate the instrumental effects. In this work, we consider the instrumental effects of polarization leakage and use the U-Net approach, a deep learning-based foreground removal technique, to eliminate the polarization leakage effect. The thermal noise is assumed to be a subdominant factor compared with the polarization leakage for future H i IM surveys and ignored in this analysis. In this method, the principal component analysis (PCA) foreground subtraction is used as a pre-processing step for the U-Net foreground subtraction. Our results show that the additional U-Net processing could either remove the foreground residual after the conservative PCA subtraction or compensate for the signal loss caused by the aggressive PCA pre-processing. Finally, we test the robustness of the U-Net foreground subtraction technique and show that it is still reliable in the case of existing constraint error on H i fluctuation amplitude.

Deriving physical parameters of unresolved star clusters

Context. This work is the seventh study in a series dedicated to investigating degeneracies of simultaneous age, mass, extinction, and metallicity determinations of partially resolved or unresolved star clusters with Hubble Space Telescope broadband aperture photometry. In the sixth work (hereafter, Paper I), it was demonstrated that the adaptive aperture photometry, performed to avoid the majority of the projected foreground and background stars falling within the apertures, gives more consistent colour indices for star clusters. Aims. In this study, we aim to supplement the homogeneous multi-colour aperture photometry results published in Paper I and provide a complete M 31 Panchromatic Hubble Andromeda Treasury (PHAT) survey star cluster photometry catalogue for further analysis. Methods. Following Paper I, we used a two-aperture approach for photometry. The first aperture is the standard one used to measure total cluster fluxes. The second (smaller) aperture is introduced to avoid the bright foreground and background stars projecting onto the clusters. We selected the radii of smaller apertures to be larger than the half-light radii of the clusters. Results. We present the second part of the star cluster aperture photometry catalogues for a sample of 1477 star clusters from the M 31 PHAT survey not covered in Paper I. Compared to the M 31 PHAT star cluster aperture photometry catalogue published by Johnson et al., adjustments were made to the cluster centre coordinates, aperture sizes, and sky background levels.

Low latency detection of massive black hole binaries

The next decade is expected to see the launch of one or more space based gravitational wave detectors: the European lead Laser Interferometer Space Antenna (LISA); and one or more Chinese mission concepts, Taiji and TianQin. One of the primary scientific targets for these missions are the mergers of black holes with masses between $10^3 M_\odot$ and $10^8 M_\odot$. These systems may produce detectable electromagnetic signatures in additional to gravitational waves due to the presence of gas in mini-disks around each black hole, and a circumbinary disk surrounding the system. The electromagnetic emission may occur before, during and after the merger. In order to have the best chance of capturing all phases of the emission it is imperative that the gravitational wave signals can be detected in low latency, and used to produce reliable estimates for the sky location and distance to help guide the search for counterparts. The low latency detection also provides a starting point for the ``global fit'' of the myriad signals that are simultaneously present in the data. Here a low latency analysis pipeline is presented that is capable of analyzing months of data in just a few hours using a laptop from the last decade. The problem of performing a global fit is avoided by whitening out the bright foreground produced by nearby galactic binaries. The performance of the pipeline is illustrated using simulated data from the LISA Data Challenge.

An unbiased estimator of the full-sky CMB angular power spectrum at large scales using neural networks

ABSTRACT Accurate estimation of the cosmic microwave background (CMB) angular power spectrum is enticing due to the prospect for precision cosmology it presents. Galactic foreground emissions, however, contaminate the CMB signal and need to be subtracted reliably in order to lessen systematic errors on the CMB temperature estimates. Typically, bright foregrounds in a region lead to further uncertainty in temperature estimates in the area even after some foreground removal technique is performed and hence determining the underlying full-sky angular power spectrum poses a challenge. We explore the feasibility of utilizing artificial neural networks to predict the angular power spectrum of the full-sky CMB temperature maps from the observed angular power spectrum of the partial sky in which CMB temperatures in some bright foreground regions are masked. We present our analysis at large angular scales with two different masks. We produce unbiased predictions of the full-sky angular power spectrum and recover the underlying theoretical power spectrum using neural networks. Our predictions are also uncorrelated to a large extent. We further show that the multipole-space covariances of the predictions of full-sky spectra made by the artificial neural networks are much smaller than those of the estimates obtained using the pseudo-Cℓ method.

A Comprehensive Risk Assessment Model for Ovarian Cancer Patients with Phospho-STAT3 and IL-31 as Immune Infiltration Relevant Genes.

IntroductionOvarian carcinoma is a malignant tumor with a high mortality rate and a lack of effective treatment options for patients at advanced stages. For improving outcomes and helping patients with poor prognosis, choose a suitable therapy and an excellent risk assessment model and new treatment options are needed.Materials and MethodsOvarian cancer gene expression profile of GSE32062 was downloaded from the NCBI GEO database for screening differentially expressed genes (DEGs) between well and poor prognosis groups using limma package in R (version 3.4.1). Prognosis-related genes and clinical prognostic factors were obtained from univariate and multivariate Cox regression analyses, and a comprehensive risk assessment model was constructed using a Pathway Dysregulation Score (PDS) matrix, Cox-Proportional Hazards (Cox-PH) regression, as well as L1-least absolute shrinkage and selection operator (L1-LASSO) penalization. Then, significant DEGs were converted to pathways and optimal prognosis-related pathways were screened. Finally, risk prediction models based on pathways, genes involved in pathways, and comprehensive clinical risk factors with pathways were built. Their prognostic functions were assessed in verification sets. Besides, genes involved in immune-pathways were checked for immune infiltration using immunohistochemistry.ResultsA superior risk assessment model involving 9 optimal combinations of pathways and one clinical factor was constructed. The pathway-based model was found to be superior to the gene-based model. Phospho-STAT3 (from JAK-STAT signaling pathway) and IL-31 (from DEGs) were found to be related to immune infiltration.ConclusionWe have generated a comprehensive risk assessment model consisting of a clinical risk factor and pathways that showed a possible bright foreground. The set of significant pathways might play as a better prognosis model which is more accurate and applicable than the DEG set. Besides, p-STAT3 and IL-31 showing correlation to immune infiltration of ovarian cancer tissues may be potential therapeutic targets for treating ovarian cancers.

Extracting the 21cm Global Signal using Artificial Neural Networks

Abstract The study of the cosmic Dark Ages, Cosmic Dawn, and Epoch of Reionization (EoR) using the all-sky averaged redshifted HI 21cm signal, are some of the key science goals of most of the ongoing or upcoming experiments, for example, EDGES, SARAS, and the SKA. This signal can be detected by averaging over the entire sky, using a single radio telescope, in the form of a Global signal as a function of only redshifted HI 21cm frequencies. One of the major challenges faced while detecting this signal is the dominating, bright foreground. The success of such detection lies in the accuracy of the foreground removal. The presence of instrumental gain fluctuations, chromatic primary beam, radio frequency interference (RFI) and the Earth’s ionosphere corrupts any observation of radio signals from the Earth. Here, we propose the use of Artificial Neural Networks (ANN) to extract the faint redshifted 21cm Global signal buried in a sea of bright Galactic foregrounds and contaminated by different instrumental models. The most striking advantage of using ANN is the fact that, when the corrupted signal is fed into a trained network, we can simultaneously extract the signal as well as foreground parameters very accurately. Our results show that ANN can detect the Global signal with $\gtrsim 92 {{\ \rm per\ cent}}$ accuracy even in cases of mock observations where the instrument has some residual time-varying gain across the spectrum.

Endowing LiTi2(PO4)3/C with excellent electrochemical performances through rational crystal doping

Aqueous lithium ion batteries show bright foreground in energy storage and electronic products. However, the performance of anode needs to be further improved to meet their higher requirements. Herein, we endowed LiTi2(PO4)3/C with excellent electrochemical performance through rational Br doping on PO43− sites. A series of Br doped LiTi2(PO4)3-xBrx/C (x = 0.00, 0.12, 0.18, and 0.24) samples were obtained via sol-gel means associated with heat-treatment, which were employed as anodes for aqueous lithium ion batteries. There are no notable impacts on morphology and lattice structure of LiTi2(PO4)3 with Br doping. And electrochemical capabilities of LiTi2(PO4)3 are evidently ameliorated by Br doping contrasted to original LiTi2(PO4)3/C (LC). LiTi2(PO4)2.82Br0.18/C demonstrates superb electrochemical property with the discharge capacities of 132.3, 112.3, and 86.3 mAh g−1 at 0.5, 7, and 15 C, separately, 27.3, 32.7, and 34.3 mAh g−1 higher than these of original LC. This may be due to that Br doping widens pathway for Li ions and improves intrinsic electronic conductivity of sample, accelerating ion and electron migration. The work shows that Br doping is a valid approach to ameliorate LiTi2(PO4)3/C performance for aqueous lithium ion batteries.

The Present State of Deep-sea Underwater Glider Technologies and Applications

Underwater gliders are a new type of underwater robots which are buoyancy-driven, moving up and down in the ocean without propeller system, so they tend to be applied to conduct marine environmental observations within long-time and large-range dimensions. In this paper, we give an overview of the system components of deep-sea underwater gliders on power, control, communication and detection, and also introduce the development and present state of deep-sea underwater glider technologies and applications in china and abroad.Taking Sea-Wing underwater gliders developed by Shenyang Institute of Automation as an example, we elaborate on the achievements on the platform technologies and scientific applications of deep-sea underwater gliders in recent years. Finally, combining with the present state of deep-sea underwater gliders, we analyze the core technologies and development tendencies, and we believe that deep-sea underwater gliders will have wide application and bright foreground in observation and networking in the waters surrounding our country in the near future.

A Takagi-Sugeno-Kang (TSK) Power Model Using Compressed-sensing Sampling

More and more information are needed in social life and commercial production, causing significant pressure on the sampling and too much time spent on signal sampling. Compressed sensing is one emerging hotspot in signal processing which employs a special sampling method to capture and represent compressible signals at a rate significantly below the Nyquist rate. In this paper, a Takagi-Sugeno-Kang (TSK) Model based on compressed-sensing sampling theorem is proposed for grinding power. It is further tested by using the actual production data, and the algorithm performance in grinding power model is also analyzed. The experiments show the validity and effectiveness of the proposed modeling method and its bright application foreground in other fields with similar features, such as power, metallurgy and so on.

HOW TO IDENTIFY AND SEPARATE BRIGHT GALAXY CLUSTERS FROM THE LOW-FREQUENCY RADIO SKY

In this work we simulate the $50-200$ MHz radio sky that is constrained in the field of view ($5^{\circ}$ radius) of the 21 Centimeter Array (21CMA), by carrying out Monte-Carlo simulations to model redshifted cosmological reionization signals and strong contaminating foregrounds, including emissions from our Galaxy, galaxy clusters, and extragalactic point sources. As an improvement of previous works, we consider in detail not only random variations of morphological and spectroscopic parameters within the ranges allowed by multi-band observations, but also evolution of radio halos in galaxy clusters, assuming that relativistic electrons are re-accelerated in the ICM in merger events and lose energy via both synchrotron emission and inverse Compton scattering with CMB photons. By introducing a new approach designed on the basis of independent component analysis (ICA) and wavelet detection algorithm, we prove that, with a cumulative observation of one month with the 21CMA array, about $80\%$ of galaxy clusters with central brightness temperatures of $> 10~{\rm K}$ at 65 MHz can be safely identified and separated from the overwhelmingly bright foreground. We find that the morphological and spectroscopic distortions are extremely small as compared to the input simulated clusters, and the reduced $\chi^2$ of brightness temperature profiles and spectra are controlled to be $\lesssim 0.5$ and $\lesssim 1.3$, respectively. These results robustly indicate that in the near future a sample of dozens of bright galaxy clusters will be disentangled from the foreground in 21CMA observations, the study of which will greatly improve our knowledge about cluster merger rates, electron acceleration mechanisms in cluster radio halos, and magnetic field in the ICM.

FERMI LARGE AREA TELESCOPE FIRST SOURCE CATALOG

ABSTRACT We present a catalog of high-energy gamma-ray sources detected by the Large Area Telescope (LAT), the primary science instrument on the Fermi Gamma-ray Space Telescope (Fermi), during the first 11 months of the science phase of the mission, which began on 2008 August 4. The First Fermi-LAT catalog (1FGL) contains 1451 sources detected and characterized in the 100 MeV to 100 GeV range. Source detection was based on the average flux over the 11 month period, and the threshold likelihood Test Statistic is 25, corresponding to a significance of just over 4σ. The 1FGL catalog includes source location regions, defined in terms of elliptical fits to the 95% confidence regions and power-law spectral fits as well as flux measurements in five energy bands for each source. In addition, monthly light curves are provided. Using a protocol defined before launch we have tested for several populations of gamma-ray sources among the sources in the catalog. For individual LAT-detected sources we provide firm identifications or plausible associations with sources in other astronomical catalogs. Identifications are based on correlated variability with counterparts at other wavelengths, or on spin or orbital periodicity. For the catalogs and association criteria that we have selected, 630 of the sources are unassociated. Care was taken to characterize the sensitivity of the results to the model of interstellar diffuse gamma-ray emission used to model the bright foreground, with the result that 161 sources at low Galactic latitudes and toward bright local interstellar clouds are flagged as having properties that are strongly dependent on the model or as potentially being due to incorrectly modeled structure in the Galactic diffuse emission.