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.

A Variable Parameter Linear Tracking Differentiator and Its Application in Large Ground-based Telescopes

This paper is concerned with the characteristics of a tracking differentiator(TD)in arranging transition process for the physical system.In order to overcome disadvantages of linear TD(LTD)in practice,a novel variable parameter linear tracking differentiator(VLTD)is proposed.By designing the speediness factor as a function of the tracking error,the VLTD can track a large range of set values with reasonable speed and acceleration.Analysis shows that VLTD can converge to its set value under certain conditions.Meanwhile,the speed and acceleration bounds are added to the VLTD,which guarantees that the proposed transition signal really plays a transitional role.The numerical simulation results emphasize necessity for adding speed and acceleration bounds to the VLTD.By comparing VLTD with the nonlinear TD(NLTD)in the simulations and experiments,VLTD can achieve almost the same performance as the NLTD while it is easier to be implemented.

A method to obtain the wind field characteristics of super-large aperture radio telescope site based on single-point wind tower and numerical simulation

The influence of wind on the pointing accuracy of large aperture radio telescopes is becoming increasingly serious, especially at high observing frequency. Obtaining the wind field characteristics efficiently is very important to reduce the wind disturbance on antenna structure. In this paper, an error evaluation of numerical simulation method is established based on the measured data of single point wind tower, and the wind field characteristics are obtained from the evaluated numerical simulation results combined with the measured data for the 110 m aperture Qi Tai radio Telescope(QTT) site. According to the simulation results, compared with the measured data, the root mean square error(RMSE) of wind speed is less than 1 m s^-1, and the minimum wind speed RMSE is 0.2 m s^-1. An analysis of the wind field characteristics of the QTT site suggests that the active wind resistance design of the antenna periphery should focus on the SSW(south-south-west) direction.

Stray light analysis of the Xinglong 2.16-m telescope

An effort towards understanding the problems associated with stray light related to the Xinglong2.16-m telescope is presented to estimate the stray light performance of the telescope itself and provide a method for improving stray light suppression.Stray light analysis for the 2.16-m telescope model,which consists of an onion shaped dome,telescope structure,equatorial mount and telescope optics,has been developed in two cases(1)pointing to 60?and(2)pointing to zenith,in both azimuth and elevation directions.The Point Source Normalized Irradiance Transmittance(PSNIT),which is generally used for assessing stray light and is uncorrelated to entrance aperture,is calculated with a series of off-axis angles.It shows that the PSNIT values are less than 10-7 when off-axis angles are larger than±20?.The dominant contributors to stray light(primary and secondary mirror,telescope structure and dome)are identified to guide performance improvement.The analyses indicate that significant benefit can be realized by adding only five vanes inside the bottom portion of the secondary baffle.In the case of pointing to zenith,the PSNIT values will decrease about 40%on average.

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.

Subreflector model depending on elevation for the Tianma 65 m Radio Telescope

A subreflector adjustment system for the Tianma 65 m radio telescope, administered by Shanghai Astronomical Observatory, has been installed to compensate for gravitational deformation of the main re- flector and the structure supporting the subreflector. The position and attitude of the subreflector are variable in order to improve the efficiency at different elevations. The subreflector model has the goal of improving the antenna＇s performance. A new fitting formulation which is different from the traditional formulation is proposed to reduce the fitting error in the Y direction. The only difference in the subreflector models of the 65 m radio telescope is the bias of a constant term in the Z direction. We have investigated the effect of movements of the subreflector on the pointing of the antenna. The results of these performance measure- ments made by moving the antenna in elevation show that the subreflector model can effectively improve the efficiency of the 65 m radio telescope at each elevation. An antenna efficiency of about 60% at the Ku band is reached in the whole angular range of elevation.

Pulsar timing observations with Haoping Radio Telescope

We report pulsar timing observations carried out in L-band with NTSC’s 40-meter Haoping Radio Telescope(HRT),which was constructed in 2014.These observations were carried out using the pulsar machine we developed.Timing observations toward millisecond pulsar J0437–4715 obtain a timing residual(r.m.s.)of 397 ns in the time span of 284 days.Our observations successfully detected Crab pulsar’s glitch that happened on 2019 July 23.

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.

New design of large fully-steerable radio telescope reflector based on homogenized mesh structure

The self-weight of a large fully-steerable radio telescope is one of the important factors affecting its performance.In the existing reflector system scheme,the problem of surface accuracy caused by its large and heavy structure has seriously restricted the application and implementation of large radio telescopes.Therefore,a new mesh structure scheme for a large fully-steerable radio telescope reflector is proposed in this paper.This scheme is based on a homogenized mesh back-up structure in the form of a quasi-geodesic grid and regular quasi-tri-prism or tetrahedron,which can significantly reduce the structural complexity and self-weight of the reflector under the condition that the reflector can meet the desired performance requirements.Finally,the feasibility and rationality of the scheme are evaluated by numerical simulation analysis,which has significant advantages and provides a new design for the reflector of a large fullysteerable radio telescope.

Fast radio burst search： cross spectrum vs. auto spectrum method

The search for fast radio bursts(FRBs) is a hot topic in current radio astronomy studies. In this work, we carry out a single pulse search with a very long baseline interferometry(VLBI) pulsar observation data set using both auto spectrum and cross spectrum search methods. The cross spectrum method,first proposed in Liu et al., maximizes the signal power by fully utilizing the fringe phase information of the baseline cross spectrum. The auto spectrum search method is based on the popular pulsar software package PRESTO, which extracts single pulses from the auto spectrum of each station. According to our comparison, the cross spectrum method is able to enhance the signal power and therefore extract single pulses from data contaminated by high levels of radio frequency interference(RFI), which makes it possible to carry out a search for FRBs in regular VLBI observations when RFI is present.

Selection of radio astronomical observation sites and its dependence on human generated RFI

We investigate the influence of population density on radio-frequency inter- ference （RFI） affecting radio astronomy. We use a new method to quantify the thresh- old of population density in order to determine the most suitable lower limit for site selection of a radio quiet zone （RQZ）. We found that there is a certain trend in the population density-RFI graph that increases rapidly at lower values and slows down to almost fiat at higher values. We use this trend to identify the thresholds for pop- ulation density that produce RFI. Using this method we found that, for frequencies up to 2.8 GHz, low, medium and high population densities affecting radio astronomy are below 150 ppl km-2, between i50 ppl km-2 and 5125 ppl km-~, and above 5125 ppl km-2 respectively. We also investigate the effect of population density on the environment of RFI in three astronomical windows, namely the deuterium, hydro- gen and hydroxyl lines. We find that a polynomial fitting to the population density produces a similar trend, giving similar thresholds for the effect of population density. We then compare our interference values to the standard threshold levels used by the International Telecommunication Union within these astronomical windows.

A new image processing method for discriminating internal layers from radio echo sounding data of ice sheets via a combined robust principal component analysis and total variation approach

Discriminating internal layers by radio echo sounding is important in analyzing the thickness and ice deposits in the Antarctic ice sheet.The signal processing method of synthesis aperture radar(SAR)has been widely used for improving the signal to noise ratio(SNR)and discriminating internal layers by radio echo sounding data of ice sheets.This method is not efficient when we use edge detection operators to obtain accurate information of the layers,especially the ice-bed interface.This paper presents a new image processing method via a combined robust principal component analysis-total variation(RPCA-TV)approach for discriminating internal layers of ice sheets by radio echo sounding data.The RPCA-based method is adopted to project the high-dimensional observations to low-dimensional subspace structure to accelerate the operation of the TV-based method,which is used to discriminate the internal layers.The efficiency of the presented method has been tested on simulation data and the dataset of the Institute of Electronics,Chinese Academy of Sciences,collected during CHINARE 28.The results show that the new method is more efficient than the previous method in discriminating internal layers of ice sheets by radio echo sounding data.

An Active Flexure Compensation Method for LAMOST spectrograph based on BP-Neural Network

The Large sky Area Multi-Object fiber Spectroscopic Telescope(LAMOST)is a Chinese national scientific research facility operated by National Astronomical Observatories,Chinese Academy of Sciences(NAOC).The LAMOST survey for the Milky Way Galaxy and extra-galactic objects has been carried out for several years.The accuracies in measuring radial velocity are expected to be 5 km s-1 for the low resolution spectroscopic survey(R=1800),and 1 km s-1 for the medium resolution mode.The stability of spectrograph is the main factor affecting the accuracies in measuring radial velocity,so an Active Flexure Compensation Method(AFCM)based on Back Propagation Neural Network(BPNN)is proposed in this paper.It utilizes a deep BP(4-layer,5-layer etc.)model of thermal-induced flexure to periodically predict and apply flexure corrections by commanding the corresponding tilt and tip motions to the camera.The spectrograph camera system is adjusted so that the positions of these spots match those in a reference image.The simulated calibration of this compensation method analytically illustrates its performance on LAMOST spectrograph.

Optical fiber positioning based on four-quadrant detector with Gaussian fitting method

With the development of large-scale spectral surveys, fiber positioning technology has been developing rapidly. Because of the performance advantages of a four-quadrant（4Q） detector, a fiber positioning and real-time monitoring system based on the 4Q detector is proposed. The detection accuracy of this system is directly determined by the precision of the center of the spot. A Gaussian fitting algorithm based on the 4Q detector is studied and applied in the fiber positioning process to improve the calculated accuracy of the spot center. The relationship between the center position of the incident spot and the detector output signal is deduced. An experimental platform is built to complete the simulated experiment. Then we use the Gaussian fitting method to process experimental data, compare the fitting value with the theoretical one and calculate the corresponding error.

Investigations of Thermal Deformation Based on the Monitoring System of Tianma 13.2 m VGOS Telescope

The thermal expansion of radio telescopes has been recognized as a significant systematic error in Very Long Baseline Interferometry(VLBI) data analysis. Although the thermal expansion model recommended by International Earth Rotation Service(IERS) Conventions 2010 can achieve millimeter accuracy for the International VLBI Service for Geodesy and Astrometry(IVS) routine telescopes, both the International Terrestrial Reference Frame 2020(ITRF2020) in preparation and the VLBI2010 project encourage the scientific community to reconsider its modeling. To this end, we developed a monitoring system for the Tianma 13.2 m VLBI Global Observing System(VGOS) telescope. Based on the observed data, we refined the IERS expansion model, with results showing that the accuracy of our modified model was improved by 1.9 times. It suggested that the IERS thermal expansion model can achieve the declared millimeter accuracy, and refining its modeling can meet the requirement of 0.3 mm rms stability of the VGOS antenna reference point for the VLBI2010 project.

Forecast daily indices of solar activity, F10.7, using support vector regression method

The 10.7 cm solar radio flux （F10.7）, the value of the solar radio emission flux density at a wavelength of 10.7 cm, is a useful index of solar activity as a proxy for solar extreme ultraviolet radiation. It is meaningful and important to predict F10.7 values accurately for both long-term （months-years） and short-term （days） forecasting, which are often used as inputs in space weather models. This study applies a novel neural network technique, support vector regression （SVR）, to forecasting daily values of F10.7. The aim of this study is to examine the feasibility of SVR in short-term F10.7 forecasting. The approach, based on SVR, reduces the dimension of feature space in the training process by using a kernel-based learning algorithm. Thus, the complexity of the calculation becomes lower and a small amount of training data will be sufficient. The time series of F10.7 from 2002 to 2006 are employed as the data sets. The performance of the approach is estimated by calculating the norm mean square error and mean absolute percentage error. It is shown that our approach can perform well by using fewer training data points than the traditional neural network.

BoxCox multi-output linear regression for 10.7 cm solar radio flux prediction

We consider the problem of predicting the mid-term daily 10.7 cm solar radio flux(F10.7),a widely-used solar activity index.A novel approach is proposed for this task,in which BoxCox transformation with a proper parameter is first applied to make the data satisfy the property of homoscedasticity that is a basic assumption of regression models,and then a multi-output linear regression model is used to predict future F10.7 values.The experiment shows that the BoxCox transformation significantly improves the predictive performance and our new approach works substantially better than the prediction from the US Airforce and other alternative methods like Auto-regressive Model,Multi-layer Perceptron,and Support Vector Regression.

A solar radio dynamic spectrograph with flexible temporal-spectral resolution

Observation and research on solar radio emission have unique scientific values in solar and space physics and related space weather forecasting applications, since the observed spectral structures may carry important information about energetic electrons and underlying physical mechanisms. In this study, we present the design of a novel dynamic spectrograph that has been installed at the Chashan Solar Radio Observatory operated by the Laboratory for Radio Technologies, Institute of Space Sciences at Shandong University. The spectrograph is characterized by real-time storage of digitized radio intensity data in the time domain and its capability to perform off-line spectral analysis of the radio spectra. The analog signals received via antennas and amplified with a low-noise amplifier are converted into digital data at a speed reaching up to 32 k data points per millisecond. The digital data are then saved into a high- speed electronic disk for further off-line spectral analysis. Using different word lengths （1-32k） and time cadences （5 ms-10 s） for off-line fast Fourier transform analysis, we can obtain the dynamic spectrum of a radio burst with different （user-defined） temporal （5 ms-10 s） and spectral （3 kHz-320kHz） resolutions. This enables great flexibility and convenience in data analysis of solar radio bursts, especially when some specific fine spectral structures are under study.

Wavelet Cleaning of Solar Dynamic Radio Spectrograms

By applying the state-of-the-art mathematical apparatus, the wavelet transformation, we explore the possibility of a dynamic cleaning of raw data ob- tained with the Chinese solar radio spectrographs over a wide wavelength range (from 0.7 to 7.6 GHz). We consider the problem of eliminating the interference caused by combination rates of data sampling (10-20 ins), and the low-frequency interference (4-30 s) caused by the receiving equipment changing its characteristics with time. It is shown that the best choice to reconstruct a signal suffering from amplitude, frequency and phase instabilities, is by means of wavelet transformation at both high and low frequencies. We analysed observational data which contained interferences of nonsolar origin such as instrumental effects and other man-made signals. A subsequent comparison of the reference data obtained with the acousto- optical receiver of the Siberian Solar Radio Telescope (SSRT) with the 'cleaned' spectra confirms the correctness of this approach.

Statistical properties of radio flux densities of solar flares

Short timescale flux variations are closely related to the energy release process of magnetic reconnection during solar flares.Radio light curves at 1,2,3.75,9.4,and 17 GHz of 209 flares observed by the Nobeyama Radio Polarimeter from 2000 to 2010 are analyzed with a running smooth technique.We find that the impulsive component(with a variation timescale shorter than 1 s)of 1 GHz emission of most flares peaks at a few tens of solar flux unit and lasts for about 1 minute and the impulsive component of 2 GHz emission lasts a shorter period and peaks at a lower flux level,while at the three high frequency channels the occurrence frequency of flares increases with the decrease of the flux density up to the noise level of the corresponding background.However,the gradual components of these emissions have similar duration and peak flux density distributions.We also derive the power spectrum on different timescales and a normalized wavelet analysis is used to confirm features on short timescales.At a time resolution of 0.1 s,more than^60%of these radio light curves show significant flux variation on 1 s or shorter time scales.This fraction increases with the decrease of frequency and reaches^100%at 1 GHz,implying that short timescale processes are universal in solar flares.We also study the correlation between the impulsive radio flux densities and soft X-ray fluxes obtained with the GOES satellites and find that more than 65%of the flares with an impulsive component have their impulsive radio emission reach a peak value ahead of the soft X-ray fluxes and this fraction increases with the radio frequency.