Quantitative Analysis of Seeing with Height and Time at Muztagh-Ata Site Based on ERA5 Database

Seeing is an important index to evaluate the quality of an astronomical site.To estimate seeing at the Muztagh-Ata site with height and time quantitatively,the European Centre for Medium-Range Weather Forecasts reanalysis database(ERA5)is used.Seeing calculated from ERA5 is compared consistently with the Differential Image Motion Monitor seeing at the height of 12 m.Results show that seeing decays exponentially with height at the Muztagh-Ata site.Seeing decays the fastest in fall in 2021 and most slowly with height in summer.The seeing condition is better in fall than in summer.The median value of seeing at 12 m is 0.89 arcsec,the maximum value is1.21 arcsec in August and the minimum is 0.66 arcsec in October.The median value of seeing at 12 m is 0.72arcsec in the nighttime and 1.08 arcsec in the daytime.Seeing is a combination of annual and about biannual variations with the same phase as temperature and wind speed indicating that seeing variation with time is influenced by temperature and wind speed.The Richardson number Ri is used to analyze the atmospheric stability and the variations of seeing are consistent with Ri between layers.These quantitative results can provide an important reference for a telescopic observation strategy.

Observation and Analysis of VLF Nocturnal Multimode Interference Phenomenon based on Waveguide Mode Theory

Very low frequency(VLF)signals are propagated between the ground-ionosphere.Multimode interference will cause the phase to show oscillatory changes with distance while propagating at night,leading to abnormalities in the received VLF signal.This study uses the VLF signal received in Qingdao City,Shandong Province,from the Russian Alpha navigation system to explore the multimode interference problem of VLF signal propagation.The characteristics of the effect of multimode interference phenomena on the phase are analyzed according to the variation of the phase of the VLF signal.However,the phase of VLF signals will also be affected by the X-ray and energetic particles that are released during the eruption of solar flares,therefore the two phenomena are studied in this work.It is concluded that the X-ray will not affect the phase of VLF signals at night,but the energetic particles will affect the phase change,and the influence of energetic particles should be excluded in the study of multimode interference phenomena.Using VLF signals for navigation positioning in degraded or unavailable GPS conditions is of great practical significance for VLF navigation systems as it can avoid the influence of multimode interference and improve positioning accuracy.

Machine Learning-based Identification of Contaminated Images in Light Curve Data Preprocessing

Attitude is one of the crucial parameters for space objects and plays a vital role in collision prediction and debris removal.Analyzing light curves to determine attitude is the most commonly used method.In photometric observations,outliers may exist in the obtained light curves due to various reasons.Therefore,preprocessing is required to remove these outliers to obtain high quality light curves.Through statistical analysis,the reasons leading to outliers can be categorized into two main types:first,the brightness of the object significantly increases due to the passage of a star nearby,referred to as“stellar contamination,”and second,the brightness markedly decreases due to cloudy cover,referred to as“cloudy contamination.”The traditional approach of manually inspecting images for contamination is time-consuming and labor-intensive.However,we propose the utilization of machine learning methods as a substitute.Convolutional Neural Networks and SVMs are employed to identify cases of stellar contamination and cloudy contamination,achieving F1 scores of 1.00 and 0.98 on a test set,respectively.We also explore other machine learning methods such as ResNet-18 and Light Gradient Boosting Machine,then conduct comparative analyses of the results.

PSRDP:A Parallel Processing Method for Pulsar Baseband Data

To address the problem of real-time processing of ultra-wide bandwidth pulsar baseband data,we designed and implemented a pulsar baseband data processing algorithm(PSRDP)based on GPU parallel computing technology.PSRDP can perform operations such as baseband data unpacking,channel separation,coherent dedispersion,Stokes detection,phase and folding period prediction,and folding integration in GPU clusters.We tested the algorithm using the J0437-4715 pulsar baseband data generated by the CASPSR and Medusa backends of the Parkes,and the J0332+5434 pulsar baseband data generated by the self-developed backend of the Nan Shan Radio Telescope.We obtained the pulse profiles of each baseband data.Through experimental analysis,we have found that the pulse profiles generated by the PSRDP algorithm in this paper are essentially consistent with the processing results of Digital Signal Processing Software for Pulsar Astronomy(DSPSR),which verified the effectiveness of the PSRDP algorithm.Furthermore,using the same baseband data,we compared the processing speed of PSRDP with DSPSR,and the results showed that PSRDP was not slower than DSPSR in terms of speed.The theoretical and technical experience gained from the PSRDP algorithm research in this article lays a technical foundation for the real-time processing of QTT(Qi Tai radio Telescope)ultra-wide bandwidth pulsar baseband data.

Emerging Trends for Microbiome Analysis： From Single-Cell Functional Imaging to Microbiome Big Data

Method development has always been and will continue to be a core driving force of microbiome science, In this perspective, we argue that in the next decade, method development in microbiome analysis will be driven by three key changes in both ways of thinking and technological platforms： ① a shift from dissecting microbiota structure by sequencing to tracking microbiota state, function, and intercellular interaction via imaging; ② a shift from interrogating a consortium or population of cells to probing individual cells; and ③a shift from microbiome data analysis to microbiome data science. Some of the recent methoddevelopment efforts by Chinese microbiome scientists and their international collaborators that underlie these technological trends are highlighted here. It is our belief that the China Microbiome Initiative has the opportunity to deliver outstanding ＂Made-in-China＂ tools to the international research community, by building an ambitious, competitive, and collaborative program at the forefront of method development for microbiome science.

Preliminary analysis on the noise characteristics of MWISP data

Noise is a significant part within a millimeter-wave molecular line datacube.Analyzing the noise improves our understanding of noise characteristics,and further contributes to scientific discoveries.We measure the noise level of a single datacube from MWISP and perform statistical analyses.We identified major factors which increase the noise level of a single datacube,including bad channels,edge effects,baseline distortion and line contamination.Cleaning algorithms are applied to remove or reduce these noise components.As a result,we obtained the cleaned datacube in which noise follows a positively skewed normal distribution.We further analyzed the noise structure distribution of a 3 D mosaicked datacube in the range l=40°7 to 43°3 and b=-2°3 to 0°3 and found that noise in the final mosaicked datacube is mainly characterized by noise fluctuation among the cells.

Data processing and error analysis for the CE-1 Lunar microwave radiometer

The microwave radiometer （MRM） onboard the Chang＇ E-1 （CE-I） lu- nar orbiter is a 4-frequency microwave radiometer, and it is mainly used to obtain the brightness temperature （TB） of the lunar surface, from which the thickness, temperature, dielectric constant and other related properties of the lunar regolith can be derived. The working mode of the CE-1 MRM, the ground calibration （including the official calibration coefficients）, as well as the acquisition and processing of the raw data are introduced. Our data analysis shows that TB increases with increasing frequency, decreases towards the lunar poles and is significantly affected by solar illumination. Our analysis also reveals that the main uncertainty in TB comes from ground calibration.

Extraction and Analysis of Coronal High-temperature Components Based on Outlier Detection

The extraction of high-temperature regions in active regions(ARs)is an important means to help understand the mechanism of coronal heating.The important observational means of high-temperature radiation in ARs is the main emission line of Fe XVⅢin the 94?of the Atmospheric Imaging Assembly.However,the diagnostic algorithms for Fe XVⅢ,including the differential emission measure(DEM)and linear diagnostics proposed by Del based on the DEM,have been greatly limited for a long time,and the results obtained are different from the predictions.In this paper,we use the outlier detection method to establish the nonlinear correlation between 94?and 171,193,211?based on the former researches by others.A neural network based on 171,193,211?is constructed to replace the low-temperature emission lines in the ARs of 94?.The predicted results are regarded as the low-temperature components of 94?,and then the predicted results are subtracted from 94?to obtain the outlier component of 94?,or Fe XVⅢ.Then,the outlier components obtained by neural network are compared with the Fe XVⅢobtained by DEM and Del's method,and a high similarity is found,which proves the reliability of neural network to obtain the high-temperature components of ARs,but there are still many differences.In order to analyze the differences between the Fe XVⅢobtained by the three methods,we subtract the Fe XVⅢobtained by the DEM and Del's method from the Fe XVⅢobtained by the neural network to obtain the residual value,and compare it with the results of Fe XIV in the temperature range of 6.1-6.45 MK.It is found that there is a great similarity,which also shows that the Fe XVⅢobtained by DEM and Del's method still has a large low-temperature component dominated by Fe XIV,and the Fe XVⅢobtained by neural network is relatively pure.

Cleaning Radio Frequency Interference in Pulsar-Folded Data Based on the Conditional Random Fields with an Adaptive Prior

Radio astronomy observations are frequently impacted by radio frequency interference(RFI).We propose a novel method,named 2σCRF,for cleaning RFI in the folded data of pulsar observations,utilizing a Bayesian-based model called conditional random fields(CRFs).This algorithm minimizes the“energy”of every pixel given an initial label.The standard deviations(i.e.,rms values)of the folded pulsar data are utilized as pixels for all subintegrations and channels.Non-RFI data without obvious interference is treated as“background noise,”while RFI-affected data have different classes due to their exceptional rms values.This initial labeling can be automated and is adaptive to the actual data.The CRF algorithm optimizes the label category for each pixel of the image with the prior initial labels.We demonstrate the efficacy of the proposed method on pulsar folded data obtained from Five-hundred-meter Aperture Spherical radio Telescope observations.It can effectively recognize and tag various categories of RFIs,including broadband or narrowband,constant or instantaneous,and even weak RFIs that are unrecognizable in some pixels but picked out based on their neighborhoods.The results are comparable to those obtained via manual labeling but without the need for human intervention,saving time and effort.

Two-dimensional Parameter Relationships for W UMa-type Systems Revisited

Reviewing the empirical and theoretical parameter relationships between various parameters is a good way to understand more about contact binary systems.In this investigation,two-dimensional(2D)relationships for P–MV(system),P–L1,2,M1,2–L1,2,and q–Lratiowere revisited.The sample used is related to 118 contact binary systems with an orbital period shorter than 0.6 days whose absolute parameters were estimated based on the Gaia Data Release 3 parallax.We reviewed previous studies on 2D relationships and updated six parameter relationships.Therefore,Markov chain Monte Carlo and Machine Learning methods were used,and the outcomes were compared.We selected 22 contact binary systems from eight previous studies for comparison,which had light curve solutions using spectroscopic data.The results show that the systems are in good agreement with the results of this study.

The GECAM Real-time Burst Alert System

Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor(GECAM),consisting of two microsatellites,is designed to detect gamma-ray bursts associated with gravitational-wave events.Here,we introduce the real-time burst alert system of GECAM,with the adoption of the BeiDou-3 short message communication service.We present the post-trigger operations,the detailed ground-based analysis,and the performance of the system.In the first year of the in-flight operation,GECAM was triggered by 42 gamma-ray bursts.The GECAM real-time burst alert system has the ability to distribute the alert within~1 minute after being triggered,which enables timely follow-up observations.

Spatiotemporal evolution and influencing factors of urban resilience in the Yellow River Basin,China

The Yellow River Basin of China is a key region that contains myriad interactions between human activities and natural environment.Industrialization and urbanization promote social-economic development,but they also have generated a series of environmental and ecological issues in this basin.Previous researches have evaluated urban resilience at the national,regional,urban agglomeration,city,and prefecture levels,but not at the watershed level.To address this research gap and elevate the Yellow River Basin’s urban resilience level,we constructed an urban resilience evaluation index system from five dimensions:industrial resilience,social resilience,environmental resilience,technological resilience,and organizational resilience.The entropy weight method was used to comprehensively evaluate urban resilience in the Yellow River Basin.The exploratory spatial data analysis method was employed to study the spatiotemporal differences in urban resilience in the Yellow River Basin in 2010,2015,and 2020.Furthermore,the grey correlation analysis method was utilized to explore the influencing factors of these differences.The results of this study are as follows:(1)the overall level of urban resilience in the Yellow River Basin was relatively low but showed an increasing trend during 2010–2015,and significant spatial distribution differences were observed,with a higher resilience level in the eastern region and a low-medium resilience level in the western region;(2)the differences in urban resilience were noticeable,with industrial resilience and social resilience being relatively highly developed,whereas organizational resilience and environmental resilience were relatively weak;and(3)the correlation ranking of resilience influencing factors was as follows:science and technology level>administrative power>openness>market forces.This research can provide a basis for improving the resilience level of cities in the Yellow River Basin and contribute to the high-quality development of the region.

Digital Beamforming Demonstration of a Microstrip Antenna Phased Array Feeds(PAF)at 1.25 GHz

We introduce the structure of a radio astronomy phased array feeds(PAF)beamforming demonstrator.In a laboratory environment,we have demonstrated beamforming on a received 1.25 GHz sinusoidal signal and used digital weighting techniques to plot the 2D pattern of the PAF.The radio frequency part of the demonstrator includes a 4×4 linearly polarized microstrip antenna array,all of which is connected in series with a low-noise amplifier.The signals from the central 4×2 array elements are injected into a radio frequency system-on-chip digital board,which can receive eight inputs with a bandwidth of 512 MHz.Combining the principle of undersampling,the beamforming is completed at a frequency of 1.25 GHz for the offline data,and a 2D image of the beam is plotted using beam scanning technology.

On the Performances of Estimating Stellar Atmospheric Parameters from CSST Broad-band Photometry

Deriving atmospheric parameters of a large sample of stars is of vital importance to understand the formation and evolution of the Milky Way.Photometric surveys,especially those with near-ultraviolet filters,can offer accurate measurements of stellar parameters,with the precision comparable to that from low/medium resolution spectroscopy.In this study,we explore the capability of measuring stellar atmospheric parameters from Chinese Space Station Telescope(CSST)broad-band photometry(particularly in the near-ultraviolet bands),based on synthetic colors derived from model spectra.We find that colors from the optical and near-ultraviolet filter systems adopted by CSST show significant sensitivities to the stellar atmospheric parameters,especially the metallicity.According to our mock data tests,the precision of the photometric metallicity is quite high,with typical values of0.17 and 0.20 dex for dwarf and giant stars,respectively.The precision of the effective temperature estimated from broad-band colors are within 50 K.

Inverse Calculation and Regularization Process for the Solar Aspect System(SAS) of HXI Payload on ASO-S Spacecraft

For the ASO-S/HXI payload, the accuracy of the flare reconstruction is reliant on important factors such as the alignment of the dual grating and the precise measurement of observation orientation. To guarantee optimal functionality of the instrument throughout its life cycle, the Solar Aspect System (SAS) is imperative to ensure that measurements are accurate and reliable. This is achieved by capturing the target motion and utilizing a physical model-based inversion algorithm. However, the SAS optical system’s inversion model is a typical ill-posed inverse problem due to its optical parameters, which results in small target sampling errors triggering unacceptable shifts in the solution. To enhance inversion accuracy and make it more robust against observation errors, we suggest dividing the inversion operation into two stages based on the SAS spot motion model. First, the as-rigid-aspossible (ARAP) transformation algorithm calculates the relative rotations and an intermediate variable between the substrates. Second, we solve an inversion linear equation for the relative translation of the substrates, the offset of the optical axes, and the observation orientation. To address the ill-posed challenge, the Tikhonov method grounded on the discrepancy criterion and the maximum a posteriori (MAP) method founded on the Bayesian framework are utilized. The simulation results exhibit that the ARAP method achieves a solution with a rotational error of roughly±3 5 (1/2-quantile);both regularization techniques are successful in enhancing the stability of the solution, the variance of error in the MAP method is even smaller—it achieves a translational error of approximately±18μm (1/2-quantile) in comparison to the Tikhonov method’s error of around±24μm (1/2-quantile). Furthermore, the SAS practical application data indicates the method’s usability in this study. Lastly, this paper discusses the intrinsic interconnections between the regularization methods.

New γ-Ray Sources Identified in All-sky Surveys Based on Fermipy's Advanced Algorithm

We employ an efficient method for identifying γ-ray sources across the entire sky,leveraging advanced algorithms from Fermipy,and cleverly utilizing the Galactic diffuse background emission model to partition the entire sky into72 regions,thereby greatly enhancing the efficiency of discovering new sources throughout the sky through multithreaded parallel computing.After confirming the reliability of the new method,we applied it for the first time to analyze data from the Fermi Large Area Telescope(Fermi-LAT)encompassing approximately 15.41 yr of all-sky surveys.Through this analysis,we successfully identified 1379 new sources with significance levels exceeding 4σ,of which 497 sources exhibited higher significance levels exceeding 5σ.Subsequently,we performed a systematic analysis of the spatial extension,spectra,and light variation characteristics of these newly identified sources.We identified 21 extended sources and 23 sources exhibiting spectral curvature above 10 GeV.Additionally,we identified 44 variable sources above 1 GeV.

Low Surface Brightness Galaxies Selected by Different Model Fitting

We present a study of low surface brightness galaxies(LSBGs) selected by fitting the images for all the galaxies inα.40 SDSS DR7 sample with two kinds of single-component models and two kinds of two-component models(disk+bulge):single exponential,single sersic,exponential+deVaucular(exp+deV),and exponential+sérsic(exp+ser).Under the criteria of the B band disk central surface brightness μ_(0,disk)(B)≥22.5 mag arcsec^(-2) and the axis ratio b/a> 0.3,we selected four none-edge-on LSBG samples from each of the models which contain 1105,1038,207,and 75 galaxies,respectively.There are 756 galaxies in common between LSBGs selected by exponential and sersic models,corresponding to 68.42% of LSBGs selected by the exponential model and 72.83% of LSBGs selected by the sersic model,the rest of the discrepancy is due to the difference in obtaining μ_(0) between the exponential and sersic models.Based on the fitting,in the range of 0.5≤n≤1.5,the relation of μ_(0) from two models can be written as μ_(0,sérsic)-μ_(0,exp)=-1.34(n-1).The LSBGs selected by disk+bulge models(LSBG_(2)comps) are more massive than LSBGs selected by single-component models(LSBG_1comp),and also show a larger disk component.Though the bulges in the majority of our LSBG_(2)comps are not prominent,more than 60% of our LSBG_(2)comps will not be selected if we adopt a single-component model only.We also identified 31 giant low surface brightness galaxies(gLSBGs) from LSBG_(2)comps.They are located at the same region in the color-magnitude diagram as other gLSBGs.After we compared different criteria of gLSBGs selection,we find that for gas-rich LSBGs,M_(*)> 10^(10)M_⊙ is the best to distinguish between gLSBGs and normal LSBGs with bulge.

Morphological Classification of Infrared Galaxies Based on WISE

This study introduces a novel convolutional neural network,the WISE Galaxy Classification Network(WGC),for classifying spiral and elliptical galaxies using Wide-field Infrared Survey Explorer(WISE)images.WGC attains an accuracy of 89.03%,surpassing the combined use of K-means or SVM with the Color-Color method in more accurately identifying galaxy morphologies.The enhanced variant,WGC_mag,integrates magnitude parameters with image features,further boosting the accuracy to 89.89%.The research also delves into the criteria for galaxy classification,discovering that WGC primarily categorizes dust-rich images as elliptical galaxies,corresponding to their lower star formation rates,and classifies less dusty images as spiral galaxies.The paper explores the consistency and complementarity of WISE infrared images with SDSS optical images in galaxy morphology classification.The SDSS Galaxy Classification Network(SGC),trained on SDSS images,achieved an accuracy of 94.64%.The accuracy reached 99.30% when predictions from SGC and WGC were consistent.Leveraging the complementarity of features in WISE and SDSS images,a novel variant of a classifier,namely the Multi-band Galaxy Morphology Integrated Classifier,has been developed.This classifier elevates the overall prediction accuracy to 95.39%.Lastly,the versatility of WGC was validated in other data sets.On the HyperLEDA data set,the distinction between elliptical galaxies and Sc,Scd and Sd spiral galaxies was most pronounced,achieving an accuracy of 90%,surpassing the classification results of the Galaxy Zoo 2 labeled WISE data set.This research not only demonstrates the effectiveness of WISE images in galaxy morphology classification but also represents an attempt to integrate multi-band astronomical data to enhance understanding of galaxy structures and evolution.

The Evolution of Photospheric Current Density During an X9.3-Class Solar Flare

This paper deduced the temporal evolution of the magnetic field through a series of high-resolution vector magnetograms and calculated the fine distribution map of current density during an X9.3-class flare eruptions using Ampère's law.The results show that a pair of conjugate current ribbons exist on both sides of the magnetic neutral line in this active region,and these conjugate current ribbons persist before,during,and after the flare.It was observed that the X9.3-class flare brightened in the form of a bright core and evolved into a double-ribbon flare over time.Importantly,the position of the double-ribbon flare matches the position of the current ribbons with high accuracy,and their morphologies are very similar.By investigating the complexity of current density and flare morphology,we discovered a potential connection between the eruption of major flares and the characteristics of current density.

Preparation for CSST:Star-galaxy Classification using a Rotationally Invariant Supervised Machine Learning Method

Most existing star-galaxy classifiers depend on the reduced information from catalogs,necessitating careful data processing and feature extraction.In this study,we employ a supervised machine learning method(GoogLeNet)to automatically classify stars and galaxies in the COSMOS field.Unlike traditional machine learning methods,we introduce several preprocessing techniques,including noise reduction and the unwrapping of denoised images in polar coordinates,applied to our carefully selected samples of stars and galaxies.By dividing the selected samples into training and validation sets in an 8:2 ratio,we evaluate the performance of the GoogLeNet model in distinguishing between stars and galaxies.The results indicate that the GoogLeNet model is highly effective,achieving accuracies of 99.6% and 99.9% for stars and galaxies,respectively.Furthermore,by comparing the results with and without preprocessing,we find that preprocessing can significantly improve classification accuracy(by approximately 2.0% to 6.0%)when the images are rotated.In preparation for the future launch of the China Space Station Telescope(CSST),we also evaluate the performance of the GoogLeNet model on the CSST simulation data.These results demonstrate a high level of accuracy(approximately 99.8%),indicating that this model can be effectively utilized for future observations with the CSST.