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.

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.

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.

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.

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.

The RFI Fast Mitigation Algorithm Based on Block LMS Filter

The radio telescope possesses high sensitivity and strong signal collection capabilities.While receiving celestial radiation signals,it also captures Radio Frequency Interferences(RFIs)introduced by human activities.RFI,as signals originating from sources other than the astronomical targets,significantly impacts the quality of astronomical data.This paper presents an RFI fast mitigation algorithm based on block Least Mean Square(LMS)algorithm.It enhances the traditional adaptive LMS filter by grouping L adjacent time-sampled points into one block and applying the same filter coefficients for filtering within each block.This transformation reduces multiplication calculations and enhances algorithm efficiency by leveraging the time-domain convolution theorem.The algorithm is tested using baseband data from the Parkes 64 m radio telescope's pulsar observations and simulated data.The results confirm the algorithm's effectiveness,as the pulsar profile after RFI mitigation closely matches the original pulsar profile.

Radio frequency compatibility analysis for BDSBAS and GPS/WAAS

Radio frequency signal compatibility is the basis of interoperability of the Satellite eBased Augmentation System(SBAS).SBAS should abide by relative international radio regulations of International Telecommunication Union(ITU) and meet the compatibility requirements of radio frequency signal between the Global Navigation Satellite System(GNSS)/SBAS,in order to avoid negative mutual interference.According to ITU Proposal and related reference and assumptions,the paper made simulation of signal receiving maximum power in the Bei Dou Satellite-Based Augmentation System(BDSBAS)global signal coverage.And then,interference of BDSBAS to Global Positioning System(GPS)/WAAS(Wide Area Augmentation System) on L1/L5 bands were calculated and analyzed,with equivalent carrierto-noise ratio as the evaluation parameter.The result shows that the carrier-to-noise ratio decrease of GPS/WAAS caused by BDSBAS B1 C and B2 a signals are extremely lower than inter-system interference of GPS/WAAS,and thus can be ignored in practical applications.Therefore,BDSBAS will not cause the service performance degradation of GPS and WAAS.

A GPU based single-pulse search pipeline(GSP)with database and its application to the Commensal Radio Astronomy FAST Survey(CRAFTS)

We developed a GPU based single-pulse search pipeline(GSP)with a candidate-archiving database.Largely based upon the infrastructure of the open source PulsaR Exploration and Search Toolkit(PRESTO),GSP implements GPU acceleration of the de-dispersion and integrates a candidate-archiving database.We applied GSP to the data streams from the Commensal Radio Astronomy FAST Survey(CRAFTS),which resulted in quasi-real-time processing.The integrated candidate database facilitates synergistic usage of multiple machine-learning tools and thus improves efficient identification of radio pulsars such as rotating radio transients(RRATs)and fast radio bursts(FRBs).We first tested GSP on pilot CRAFTS observations with the FAST Ultra-Wide Band(UWB)receiver.GSP detected all pulsars known from the the Parkes multibeam pulsar survey in the corresponding sky area covered by the FAST-UWB.GSP also discovered 13 new pulsars.We measured the computational efficiency of GSP to be~120 times faster than the original PRESTO and~60 times faster than an MPI-parallelized version of PRESTO.

The Data Processing of the LAMOST Medium-resolution Spectral Survey of Galactic Nebulae(LAMOST MRS-N Pipeline)

The Large sky Area Multi-Object Fiber Spectroscopic Telescope(LAMOST) medium-resolution spectral survey of Galactic Nebulae(MRS-N) has conducted for more than three years since 2018 September and observed more than190 thousand nebular spectra and 20 thousand stellar spectra.However,there is not yet a data processing pipeline for nebular spectra.To significantly improve the accuracy of nebulae classification and their physical parameters,we developed the MRS-N Pipeline.This article presented in detail each data processing step of the MRS-N Pipeline,such as removing cosmic rays,merging single exposure,fitting sky light emission lines,wavelength recalibration,subtracting skylight,measuring nebular parameters,creating catalogs and packing spectra.Finally,a description of the data products,including nebular spectra files and parameter catalogs,is provided.

The first data release of LAMOST low-resolution single-epoch spectra

LAMOST Data Release 5,covering 17000 deg^(2) from-10°to 80°in declination,contains 9 million co-added low-resolution spectra of celestial objects,each spectrum combined from repeat exposure of two to tens of times during Oct 2011 to Jun 2017.In this paper,we present the spectra of individual exposures for all the objects in LAMOST Data Release 5.For each spectrum,the equivalent width of 60lines from 11 different elements are calculated with a new method combining the actual line core and fitted line wings.For stars earlier than F type,the Balmer lines are fitted with both emission and absorption profiles once two components are detected.Radial velocity of each individual exposure is measured by minimizing χ^(2) between the spectrum and its best template.The database for equivalent widths of spectral lines and radial velocities of individual spectra are available online.Radial velocity uncertainties with different stellar type and signal-to-noise ratio are quantified by comparing different exposure of the same objects.We notice that the radial velocity uncertainty depends on the time lag between observations.For stars observed in the same day and with signal-to-noise ratio higher than 20,the radial velocity uncertainty is below 5 km s^(-1),and increases to 10 km s^(-1) for stars observed in different nights.

Data-driven Seeing Prediction for Optics Telescope:from Statistical Modeling,Machine Learning to Deep Learning Techniques

Predicting seeing of astronomical observations can provide hints of the quality of optical imaging in the near future,and facilitate flexible scheduling of observation tasks to maximize the use of astronomical observatories.Traditional approaches to seeing prediction mostly rely on regional weather models to capture the in-dome optical turbulence patterns.Thanks to the developing of data gathering and aggregation facilities of astronomical observatories in recent years,data-driven approaches are becoming increasingly feasible and attractive to predict astronomical seeing.This paper systematically investigates data-driven approaches to seeing prediction by leveraging various big data techniques,from traditional statistical modeling,machine learning to new emerging deep learning methods,on the monitoring data of the Large sky Area Multi-Object fiber Spectroscopic Telescope(LAMOST).The raw monitoring data are preprocessed to allow for big data modeling.Then we formulate the seeing prediction task under each type of modeling framework and develop seeing prediction models through using representative big data techniques,including ARIMA and Prophet for statistical modeling,MLP and XGBoost for machine learning,and LSTM,GRU and Transformer for deep learning.We perform empirical studies on the developed models with a variety of feature configurations,yielding notable insights into the applicability of big data techniques to the seeing prediction task.

Example-based super-resolution for single-image analysis from the Chang'e-1 Mission

Due to the low spatial resolution of images taken from the Chang'e-1(CE-1) orbiter, the details of the lunar surface are blurred and lost. Considering the limited spatial resolution of image data obtained by a CCD camera on CE-1, an example-based super-resolution(SR) algorithm is employed to obtain highresolution(HR) images. SR reconstruction is important for the application of image data to increase the resolution of images. In this article, a novel example-based algorithm is proposed to implement SR reconstruction by single-image analysis, and the computational cost is reduced compared to other example-based SR methods. The results show that this method can enhance the resolution of images using SR and recover detailed information about the lunar surface. Thus it can be used for surveying HR terrain and geological features. Moreover, the algorithm is significant for the HR processing of remotely sensed images obtained by other imaging systems.

The influence of the Insight-HXMT/LE time response on timing analysis

The LE is the low energy telescope that is carried on Insight-HXMT.It uses swept charge devices(SCDs)to detect soft X-ray photons.LE’s time response is caused by the structure of the SCDs.With theoretical analysis and Monte Carlo simulations we discuss the influence of LE time response(LTR)on the timing analysis from three aspects:the power spectral density,the pulse profile and the time lag.After the LTR,the value of power spectral density monotonously decreases with the increasing frequency.The power spectral density of a sinusoidal signal reduces by a half at frequency 536 Hz.The corresponding frequency for quasi-periodic oscillation(QPO)signals is 458 Hz.The root mean square(RMS)of QPOs holds a similar behaviour.After the LTR,the centroid frequency and full width at half maxima(FWHM)of QPOs signals do not change.The LTR reduces the RMS of pulse profiles and shifts the pulse phase.In the time domain,the LTR only reduces the peak value of the cross-correlation function while it does not change the peak position;thus it will not affect the result of the time lag.When considering the time lag obtained from two instruments and one among them is LE,a 1.18 ms lag is expected caused by the LTR.The time lag calculated in the frequency domain is the same as that in the time domain.

Automated classification technique for edge-on galaxies based on mathematical treatment of brightness data

Classification of edge-on galaxies is important to astronomical studies due to our Milky Way galaxy being an edge-on galaxy.Edge-on galaxies pose a problem to classification due to their less overall brightness levels and smaller numbers of pixels.In the current work,a novel technique for the classification of edge-on galaxies has been developed.This technique is based on the mathematical treatment of galaxy brightness data from their images.A special treatment for galaxies’brightness data is developed to enhance faint galaxies and eliminate adverse effects of high brightness backgrounds as well as adverse effects of background bright stars.A novel slimness weighting factor is developed to classify edge-on galaxies based on their slimness.The technique has the capacity to be optimized for different catalogs with different brightness levels.In the current work,the developed technique is optimized for the EFIGI catalog and is trained using a set of 1800 galaxies from this catalog.Upon classification of the full set of 4458 galaxies from the EFIGI catalog,an accuracy of 97.5% has been achieved,with an average processing time of about 0.26 seconds per galaxy on an average laptop.

A catalogue of 74 new open clusters found in Gaia Data-Release 2

Based on astrometric data from Gaia Data-Release 2(DR2),we employ an unsupervised machine learning method to blindly search for open star clusters in the Milky Way within the Galactic latitude range of|b|<20°.In addition to 2080 known clusters,74 new open cluster candidates are found.In this work,we present the positions,apparent radii,parallaxes,proper motions and member stars of these candidates.Meanwhile,to obtain the physical parameters of each candidate cluster,stellar isochrones are fit to the photometric data.The results show that the apparent radii and the observed proper motion dispersions of these new candidates are consistent with those of open clusters previously identified in Gaia DR2.

Generating a radioheliograph image from SDO/AIA data with the machine learning method

Radioheliograph images are essential for the study of solar short term activities and long term variations, while the continuity and granularity of radioheliograph data are not so ideal, due to the short visible time of the Sun and the complex electron-magnetic environment near the ground-based radio telescope. In this work, we develop a multi-channel input single-channel output neural network, which can generate radioheliograph image in microwave band from the Extreme Ultra-violet(EUV) observation of the Atmospheric Imaging Assembly(AIA) on board the Solar Dynamic Observatory(SDO). The neural network is trained with nearly 8 years of data of Nobeyama Radioheliograph(No RH) at 17 GHz and SDO/AIA from January 2011 to September 2018. The generated radioheliograph image is in good consistency with the well-calibrated No RH observation. SDO/AIA provides solar atmosphere images in multiple EUV wavelengths every 12 seconds from space, so the present model can fill the vacancy of limited observation time of microwave radioheliograph, and support further study of the relationship between the microwave and EUV emission.

Identifications of RR Lyrae Stars and Quasars from the Simulated Data of Mephisto-W Survey

We have investigated the feasibilities and accuracies of the identifications of RR Lyrae stars and quasars from the simulated data of the Multi-channel Photometric Survey Telescope(Mephisto)W Survey.Based on the variable sources light curve libraries from the Sloan Digital Sky Survey(SDSS)Stripe 82 data and the observation history simulation from the Mephisto-W Survey Scheduler,we have simulated the uvgriz multi-band light curves of RR Lyrae stars,quasars and other variable sources for the first year observation of Mephisto W Survey.We have applied the ensemble machine learning algorithm Random Forest Classifier(RFC)to identify RR Lyrae stars and quasars,respectively.We build training and test samples and extract~150 features from the simulated light curves and train two RFCs respectively for the RR Lyrae star and quasar classification.We find that,our RFCs are able to select the RR Lyrae stars and quasars with remarkably high precision and completeness,with purity=95.4%and completeness=96.9%for the RR Lyrae RFC and purity=91.4%and completeness=90.2%for the quasar RFC.We have also derived relative importances of the extracted features utilized to classify RR Lyrae stars and quasars.