Derivative Spectroscopy and its Application at Detecting the Weak Emission/Absorption Lines

The development of spectroscopic survey telescopes like Large Sky Area Multi-Object Fiber Spectroscopic Telescope(LAMOST),Apache Point Observatory Galactic Evolution Experiment and Sloan Digital Sky Survey has opened up unprecedented opportunities for stellar classification.Specific types of stars,such as early-type emission-line stars and those with stellar winds,can be distinguished by the profiles of their spectral lines.In this paper,we introduce a method based on derivative spectroscopy(DS)designed to detect signals within complex backgrounds and provide a preliminary estimation of curve profiles.This method exhibits a unique advantage in identifying weak signals and unusual spectral line profiles when compared to other popular line detection methods.We validated our approach using synthesis spectra,demonstrating that DS can detect emission signals three times fainter than Gaussian fitting.Furthermore,we applied our method to 579,680 co-added spectra from LAMOST Medium-Resolution Spectroscopic Survey,identifying 16,629 spectra with emission peaks around the Hαline from 10,963 stars.These spectra were classified into three distinct morphological groups,resulting in nine subclasses as follows.(1)Emission peak above the pseudo-continuum line(single peak,double peaks,emission peak situated within an absorption line,P Cygni profile,Inverse P Cygni profile);(2)Emission peak below the pseudo-continuum line(sharp emission peak,double absorption peaks,emission peak shifted to one side of the absorption line);(3)Emission peak between the pseudo-continuum line.

SFNet:Stellar Feature Network with CWT for Stellar Spectra Recognition

Stellar spectral classification is crucial in astronomical data analysis.However,existing studies are often limited by the uneven distribution of stellar samples,posing challenges in practical applications.Even when balancing stellar categories and their numbers,there is room for improvement in classification accuracy.This study introduces a Continuous Wavelet Transform using the Super Morlet wavelet to convert stellar spectra into wavelet images.A novel neural network,the Stellar Feature Network,is proposed for classifying these images.Stellar spectra from Large Sky Area Multi-Object Fiber Spectroscopic Telescope DR9,encompassing five equal categories(B,A,F,G,K),were used.Comparative experiments validate the effectiveness of the proposed methods and network,achieving significant improvements in classification accuracy.

Confirmation of a Sub-Saturn-size Transiting Exoplanet Orbiting a G Dwarf:TOI-1194 b and a Very Low Mass Companion Star: TOI-1251 B from TESS

We report the confirmation of a sub-Saturn-size exoplanet,TOI-1194 b,with a mass of about 0.456+0.055-0.051M_(J),and a very low mass companion star with a mass of about 96.5±1.5 MJ,TOI-1251 B.Exoplanet candidates provided by the Transiting Exoplanet Survey Satellite(TESS)are suitable for further follow-up observations by ground-based telescopes with small and medium apertures.The analysis is performed based on data from several telescopes worldwide,including telescopes in the Sino-German multiband photometric campaign,which aimed at confirming TESS Objects of Interest(TOIs)using ground-based small-aperture and medium-aperture telescopes,especially for long-period targets.TOI-1194 b is confirmed based on the consistent periodic transit depths from the multiband photometric data.We measure an orbital period of 2.310644±0.000001 days,the radius is 0.767+0.045-0.041RJ and the amplitude of the RV curve is 69.4_(-7.3)^(+7.9)m s^(-1).TOI-1251 B is confirmed based on the multiband photometric and high-resolution spectroscopic data,whose orbital period is 5.963054+0.000002-0.000001days,radius is 0.947+0.035-0.033 R_(J) and amplitude of the RV curve is 9849_(-40)^(+42)ms^(-1).

Digitization of Astronomical Photographic Plates of China and Astrometric Measurement of Single-exposure Plates

From the mid-19th century to the end of the 20th century, photographic plates served as the primary detectors for astronomical observations. Astronomical photographic observations in China began in 1901, and over a century, a total of approximately 30,000 astronomical photographic plates were captured. These historical plates play an irreplaceable role in conducting long-term, time-domain astronomical research. To preserve and explore these valuable original astronomical observational data, Shanghai Astronomical Observatory has organized the transportation of plates, taken during nighttime observations from various stations across the country, to the Sheshan Plate Archive for centralized preservation. For the first time, plate information statistics were calculated. On this basis, the plates were cleaned and digitally scanned, and finally digitized images were acquired for 29,314 plates. In this study, using Gaia DR2 as the reference star catalog, astrometric processing was carried out successfully on 15,696 single-exposure plates, including object extraction, stellar identification,and plate model computation. As a result, for long focal length telescopes, such as the 40 cm double-tube refractor telescope, the 1.56 m reflector telescope at Shanghai Astronomical Observatory, and the 1m reflecting telescope at Yunnan Astronomical Observatory, the astrometric accuracy obtained for their plates is approximately 0."1–0."3. The distribution of astrometric accuracy for medium and short focal length telescopes ranges from 0."3 to 1."0. The relevant data of this batch of plates, including digitized images and a stellar catalog of the plates, are archived and released by the National Astronomical Data Center. Users can access and download plate data based on keywords such as station, telescope, observation year, and observed celestial coordinates.

A Hierarchical Method for Locating the Interferometric Fringes of Celestial Sources in the Visibility Data

In source detection in the Tianlai project,locating the interferometric fringe in visibility data accurately will influence downstream tasks drastically,such as physical parameter estimation and weak source exploration.Considering that traditional locating methods are time-consuming and supervised methods require a great quantity of expensive labeled data,in this paper,we first investigate characteristics of interferometric fringes in the simulation and real scenario separately,and integrate an almost parameter-free unsupervised clustering method and seeding filling or eraser algorithm to propose a hierarchical plug and play method to improve location accuracy.Then,we apply our method to locate single and multiple sources’interferometric fringes in simulation data.Next,we apply our method to real data taken from the Tianlai radio telescope array.Finally,we compare with unsupervised methods that are state of the art.These results show that our method has robustness in different scenarios and can improve location measurement accuracy effectively.

UWLPIPE:Ultra-wide Bandwidth Low-frequency Pulsar Data Processing Pipeline

For real-time processing of ultra-wide bandwidth low-frequency pulsar baseband data,we designed and implemented an ultra-wide bandwidth low-frequency pulsar data processing pipeline(UWLPIPE)based on the shared ringbuffer and GPU parallel technology.UWLPIPE runs on the GPU cluster and can simultaneously receive multiple 128 MHz dual-polarization VDIF data packets preprocessed by the front-end FPGA.After aligning the dual-polarization data,multiple 128M subband data are packaged into PSRDADA baseband data or multi-channel coherent dispersion filterbank data,and multiple subband filterbank data can be spliced into wideband data after time alignment.We used the Nanshan 26 m radio telescope with the L-band receiver at964~1732 MHz to observe multiple pulsars.Finally,we processed the data using DSPSR software,and the results showed that each subband could correctly fold out the pulse profile,and the wideband pulse profile accumulated by multiple subbands could be correctly aligned.

A Geometric Distortion Solution Specifically for Historical Observations and its Implementation

Geometric distortion(GD)critically constrains the precision of astrometry.Using well-established methods to correct GD requires calibration observations,which can only be obtained using a special dithering strategy during the observation period.Unfortunately,this special observation mode is not often used,especially for historical observations before those GD correction methods were presented.As a result,some telescopes have no GD calibration observations for a long period,making it impossible to accurately determine the GD effect.This limits the value of the telescope observations in certain astrometric scenarios,such as using historical observations of moving targets in the solar system to improve their orbits.We investigated a method for handling GD that does not rely on the calibration observations.With this advantage,it can be used to solve the GD models of telescopes which were intractable in the past.The method was implemented in Python and released on GitHub.It was then applied to solve GD in the observations taken with the 1 m and 2.4 m telescopes at Yunnan Observatory.The resulting GD models were compared with those obtained using well-established methods to demonstrate the accuracy.Furthermore,the method was applied in the reduction of observations for two targets,the moon of Jupiter(Himalia)and binary GSC 2038-0293,to show its effectiveness.After GD correction,the astrometric results for both targets show improvements.Notably,the mean residual between the observed and computed position(O-C)for binary GSC 2038-0293 decreased from 36 to 5 mas.

Deep Neural Network Closed-loop with Raw Data for Optical Resident Space Object Detection

Optical survey is an important means for observing resident space objects and space situational awareness.With the application of astronomical techniques and reduction method,wide field of view telescopes have made significant contributions in discovering and identifying resident space objects.However,with the development of modern optical and electronic technology,the detection limit of instruments and infrastructure has been greatly extended,leading to an extensive number of raw images and many more sources in these images.Challenges arise when reducing these data in terms of traditional measurement and calibration.Based on the amount of data,it is particularly feasible and reliable to apply machine learning algorithms.Here an end-to-end deep learning framework is developed,it is trained with a priori information on raw detections and the automatic detection task is performed on the new data acquired.The closed-loop is evaluated based on consecutive CCD images obtained with a dedicated space debris survey telescope.It is demonstrated that our framework can achieve high performance compared with the traditional method,and with data fusion,the efficiency of the system can be improved without changing hardware or deploying new devices.The technique deserves a wider application in many fields of observational astronomy.

Development and application of high-precision multifunction astronomical plate digitizers in China

Before charge-coupled device detectors became widely employed in observational astronomy,for more than a hundred years,the main detection method was photography on astronomical glass plates.Recently,in order to preserve these historical data and maintain their usability,the International Astronomical Union has appealed to all countries for global digitization of astronomical plates by developing or adopting advanced digitization technology.Specialized digitizers with high precision and high measuring speed represent key equipment for this task.The Shanghai Astronomical Observatory and the Nishimura Co.,Ltd in Japan cooperated between 2013 and 2016 to develop the first Chinese high-precision astronomical plate digitizer,which was then used for complete digitization of all nighttime-observation astronomical plates in China.Then,in 2019–2021,the Shanghai Astronomical Observatory independently developed new models of plate digitizers that enabled countries such as Uzbekistan and Italy to digitize their astronomical plates.Additionally,a new high-precision and multifunction digitizer was also used to digitize valuable microscope slides from the Shanghai Natural History Museum,providing a successful example of cross-domain application of high-precision digitization technology.

An adaptive loop gain selection for CLEAN deconvolution algorithm

Radio interferometry significantly improves the resolution of observed images, and the final result also relies heavily on data recovery. The Cotton-Schwab CLEAN(CS-Clean) deconvolution approach is a widely used reconstruction algorithm in the field of radio synthesis imaging. However, parameter tuning for this algorithm has always been a difficult task. Here, its performance is improved by considering some internal characteristics of the data. From a mathematical point of view, a peak signal-to-noise-based(PSNRbased) method was introduced to optimize the step length of the steepest descent method in the recovery process. We also found that the loop gain curve in the new algorithm is a good indicator of parameter tuning.Tests show that the new algorithm can effectively solve the problem of oscillation for a large fixed loop gain and provides a more robust recovery.

Deep Learning Applications Based on WISE Infrared Data:Classification of Stars,Galaxies and Quasars

The Wide-field Infrared Survey Explorer(WISE)has detected hundreds of millions of sources over the entire sky.However,classifying them reliably is a great challenge due to degeneracies in WISE multicolor space and low detection levels in its two longest-wavelength bandpasses.In this paper,the deep learning classification network,IICnet(Infrared Image Classification network),is designed to classify sources from WISE images to achieve a more accurate classification goal.IICnet shows good ability on the feature extraction of the WISE sources.Experiments demonstrate that the classification results of IICnet are superior to some other methods;it has obtained 96.2%accuracy for galaxies,97.9%accuracy for quasars,and 96.4%accuracy for stars,and the Area Under Curve of the IICnet classifier can reach more than 99%.In addition,the superiority of IICnet in processing infrared images has been demonstrated in the comparisons with VGG16,GoogleNet,ResNet34,Mobile Net,EfficientNetV2,and RepVGG-fewer parameters and faster inference.The above proves that IICnet is an effective method to classify infrared sources.

A deep learning approach for detecting candidates of supernova remnants

Detecting supernova remnant(SNR) candidates in the interstellar medium is a challenging task because SNRs have weak radio signals and irregular shapes. The use of a convolutional neural network is a deep learning method that can help us extract various features from images. To extract SNRs from astronomical images and estimate the positions of SNR candidates, we design the SNR-Net model composed of a training component and a detection component. In addition, transfer learning is used to initialize the network parameters, which improves the speed and accuracy of network training. We apply a T-T plot(of the different brightness temperatures of map pixels at two different frequencies) to calculate the spectral index of SNR candidates. To accelerate the scientific computing process, we take advantage of innovative hardware architecture, such as deep learning optimized graphics processing units, which increases the speed of computation by a factor of 5. A case study suggests that SNR-Net may be applicable to detecting extended sources in the images automatically.

Full-frame Data Reduction Method:A Data Mining Tool to Detect the Potential Variations in Optical Photometry

A Synchronous Photometry Data Extraction(SPDE)program,performing indiscriminate monitoring of all stars appearing in the same field of view of an astronomical image,is developed by integrating several Astropy affiliated packages to make full use of time series observed by traditional small/medium aperture ground-based telescopes.The complete full-frame stellar photometry data reductions implemented for the two time series of cataclysmic variables:RX J2102.0+3359 and Paloma J0524+4244 produce 363 and 641 optimal light curves,respectively.A cross-identification with SIMBAD finds 23 known stars,of which 16 are red giant-/horizontal-branch stars,2 W UMa-type eclipsing variables,2 program stars,an X-ray source and 2 Asteroid Terrestrial-impact Last Alert System variables.Based on the data products from the SPDE program,a follow-up light curve analysis program identifies 32 potential variable light curves,of which 18 are from the time series of RX J2102.0+3359,and 14 are from that of Paloma J0524+4244.They are preliminarily separated into periodic,transient,and peculiar types.By querying for the 58 VizieR online data catalogs,their physical parameters and multi-band brightness spanning X-ray to radio are compiled for future analysis.

Research on Ultra-wide Bandwidth Low-frequency Signal Channelization for Xinjiang 110m Radio Telescope

Aiming at the subband division of ultra-wide bandwidth low-frequency(UWL)signal(frequency coverage range:704–4032 MHz)of the Xinjiang 110 m QiTai radio Telescope(QTT),a scheme of ultra-wide bandwidth signal is designed.First,we analyze the effect of different window functions such as the Hanning window,Hamming window,and Kaiser window on the performance of finite impulse response(FIR)digital filters,and implement a critical sampling polyphase filter bank(CS-PFB)based on the Hamming window FIR digital filter.Second,we generate 3328 MHz simulation data of ultra-wideband pulsar baseband in the frequency range of 704–4032 MHz using the ultra-wide bandwidth pulsar baseband data generation algorithm based on the 400 MHz bandwidth pulsar baseband data obtained from Parkes CASPSR observations.Third,we obtain 26 subbands of 128 MHz based on CS-PFB and the simulation data,and the pulse profile of each subband by coherent dispersion,integration,and folding.Finally,the phase of each subband pulse profile is aligned by non-coherent dedispersion,and to generate a broadband pulse profile,which is basically the same as the pulse profile obtained from the original data using DSPSR.The experimental results show that the scheme for the QTT UWL receiving system is feasible,and the proposed channel algorithm in this paper is effective.

Defocus spot detection of astronomical optical system

Defocusing spot size detection is especially essential for aberration analysis and correction of optical systems. In the case of far defocusing, the celestial forms a pupil image on the detector, and the size of the image is linearly changed with the defocusing distance, and can be used to correct the optical system and analyze the image quality. Based on the focal plane attitude detection of Large Sky Area MultiObject Fiber Spectroscopy Telescope(LAMOST), this paper uses a variety of methods to detect the size of the defocusing spot of LAMOST telescope. For the particularity of the spot, the average value spacing algorithm, the peak value spacing algorithm, the ellipse fitting algorithm, and the multi-peak Gaussian fitting algorithm are used to detect the spot size. This paper will introduce these four methods, in which the average value spacing algorithm is proposed by the author of this paper. The advantages and disadvantages of the four methods are compared. The experimental results show that the average value spacing algorithm can achieve better accuracy of spot size detection in the four algorithms.

Algorithm for extracting intersections in PPV space of filaments

A filament is an important structure for studying star formation,especially intersections of filaments which are believed to be more dense than other regions.Identifying filament intersections is the first step in studying them.Current methods can only extract two-dimensional intersections without considering the velocity dimension.In this paper,we propose a method to identify three-dimensional(3 D)intersections by combining Harris Corner Detection and Hough Line Transform,which achieve a precision of 98%.We apply this method for extracting intersection structures of the OMC-2/3 molecular cloud and to study its physical properties and obtain the associated PDF distribution.Results show denser gas is concentrated in those 3 D intersections.

Detection of Pulsation and Additional Components in Eclipsing Binary RS Sct

The eclipsing binary star RS Sct is a semi-detached system of theβLyrae type.This system was photometered for six nights in 2019 August,and 2020 June and August.The light and radial velocity curves were simultaneously analyzed to obtain the absolute physical and orbital parameters of the system,and the system geometry was determined.In this system,the primary component has filled its inner Roche lobe and the secondary component is close to filling it.Moreover,the change in the orbital period of this system was investigated.The presence of the third or fourth components and the mass transfer between the two components affect the orbital period of the system.In addition,the pulsation of the primary component was detected.Also,several frequencies with high signal-to-noise ratios were identified.According to the position of the primary component in the H-R diagram and the value of the obtained frequencies,this component is likely a delta-Scuti pulsator.

Detection Capability Evaluation of Lunar Mineralogical Spectrometer:Results from Ground Experimental Data

The Chang’E-6 mission will first land on the far side of the moon and bring lunar samples back.As a hyperspectral imager aboard the Chang’E-6 lander,the Lunar Mineralogical Spectrometer(LMS),will achieve the goal of spectral detection and mineral composition analysis in the sampling area,and the data of LMS will also be compared with the results of the returned sample laboratory measurements.Visible and near-infrared hyperspectral remote sensing is an effective tool for lunar minerals identification and quantification.The ground validation experiment can be used to evaluate the detection ability of the LMS.According to the modal abundances of lunar minerals and glasses of APOLLO samples,binary mixed samples,ternary mixed samples,and seven-membered mixed samples were prepared.The samples were ground and stirred homogeneous to about 200 mesh(median particle size about 75μm),to simulate the soil state of the lunar surface.Under the laboratory ambient condition,the 480–3200 nm spectral data of the samples were acquired using the Engineering Qualification Model(EQM)of Chang’E-5 LMS,the performance of which is consistent with the flight model of Chang’E-6 LMS.By fitting the mixed samples’spectral data of the EQM using the Modified Gaussian Methods,the following conclusions can be drawn:The subtle spectral changes of mixed samples can be detected.The modal abundance of low-Ca pyroxene,high-Ca pyroxene,and plagioclase can be derived based on the spectral parameters such as absorption position,depth or width of the mixed samples,and the correlation coefficients R2are better than 82%,indicating that the LMS has good quantitative detection capability.

The Co-alignment of Winged HαData Observed by the New Vacuum Solar Telescope

The New Vacuum Solar Telescope(NVST)has been releasing its novel winged Hαdata(WHD)since 2021 April,namely the Hαimaging spectroscopic data.Compared with the prior released version,the new data are further coaligned among the off-band images and packaged into a standard solar physics community format.In this study,we illustrate the alignment algorithm used by the novel WHD,which is mainly based on the optical flow method to obtain the translation offset between the winged images.To quantitatively evaluate the alignment results of two images with different similarities,we calculate the alignment accuracies between the images of different off-band and line center,respectively.The result shows that our alignment algorithm could reach up to the accuracy of about 0.″1 when the off-band of winged image is lower than 0.6?.In addition,we introduce the final product of the WHD in detail,which can provide convenience for the solar physicists to use high-resolution Hαimaging spectroscopic data of NVST.

A search for metal-poor stars pre-enriched by pair-instability supernovae I. A pilot study for target selection from Sloan Digital Sky Survey

We report on a pilot study on identifying metal-poor stars pre-enriched by Pair-Instability Supernovae（PISNe）.Very massive,first generation（Population Ⅲ） stars（140 M⊙≤M≤260 M⊙）end their lives as PISNe,which have been predicted by theories,but no relics of PISNe have been observed yet.Among the distinct characteristics of the yields of PISNe,as predicted by theoretical calculations,are a strong odd-even effect,and a strong overabundance of Ca with respect to iron and the solar ratio.We use the latter characteristic to identify metal-poor stars in the Galactic halo that have been pre-enriched by PISNe,by comparing metallicites derived from strong, co-added Fe lines detected in low-resolution（i.e.,R=λ/△λ～2000）spectra of the Sloan Digital Sky Survey（SDSS）,with metallicities determined by the SDSS Stellar Parameters Pipeline（SSPP）.The latter are based on the strength of the CaⅡ K line and assumptions on the Ca/Fe abundance ratio.Stars are selected as candidates if their metallicity derived from Fe lines is significantly lower than the SSPP metallicities.In a sample of 12 300 stars for which SDSS spectroscopy is available,we have identified 18 candidate stars.Higher resolution and signal-to-noise ratio spectra of these candidates are being obtained with the Very Large Telescope of the European Southern Observatory and the XSHOOTER spectrograph,to determine their abundance patterns,and to verify our selection method.We plan to apply our method to the database of several million stellar spectra to be acquired with the Guo Shou Jing Telescope （LAMOST）in the next five years.