An Update of the Catalog of Radial Velocity Standard Stars from the APOGEE DR17

We present an updated catalog of 46,753 radial velocity(RV)standard stars selected from the APOGEE DR17.These stars cover the Northern and Southern Hemispheres almost evenly,with 62%being red giants and 38%being main sequence stars.These RV standard stars are stable on a baseline longer than 200 days(with 54%longer than one year and 10%longer than five years)with a median stability better than 215 m s~(-1).The average number of observations of those stars is 5 and each observation is required to have signal-to-noise ratio greater than50 and RV measurement error smaller than 500 m s~(-1).Based on the new APOGEE RV standard star catalog,we have checked the RV zero-points(RVZPs)for current large-scale stellar spectroscopic surveys including RAVE,LAMOST,GALAH and Gaia.By careful analysis,we estimate their mean RVZP to be+0.149 kms~(-1),+4.574 km s~(-1)(for LRS),-0.031 km s~(-1)and+0.014 kms~(-1),respectively,for the four surveys.In the RAVE,LAMOST(for MRS),GALAH and Gaia surveys,RVZP exhibits a systematic trend with stellar parameters(mainly[Fe/H],T_(eff),log g,G_(BP)-G_(RP)and G_(RVS)).The corrections to those small but clear RVZPs are of vital importance for these massive spectroscopic surveys in various studies that require extremely high RV accuracies.

Generalization of PhaseNet in Shandong and its application to the Changqing M4.1 earthquake sequence

Waveforms of seismic events,extracted from January 2019 to December 2021 were used to construct a test dataset to investigate the generalizability of PhaseNet in the Shandong region.The results show that errors in the picking of seismic phases(P-and Swaves)had a broadly normal distribution,mainly concentrated in the ranges of−0.4–0.3 s and−0.4–0.8 s,respectively.These results were compared with those published in the original PhaseNet article and were found to be approximately 0.2–0.4 s larger.PhaseNet had a strong generalizability for P-and S-wave picking for epicentral distances of less than 120 km and 110 km,respectively.However,the phase recall rate decreased rapidly when these distances were exceeded.Furthermore,the generalizability of PhaseNet was essentially unaffected by magnitude.The M4.1 earthquake sequence in Changqing,Shandong province,China,that occurred on February 18,2020,was adopted as a case study.PhaseNet detected more than twice the number of earthquakes in the manually obtained catalog.This further verified that PhaseNet has strong generalizability in the Shandong region,and a high-precision earthquake catalog was constructed.According to these precise positioning results,two earthquake sequences occurred in the study area,and the southern cluster may have been triggered by the northern cluster.The focal mechanism solution,regional stress field,and the location results of the northern earthquake sequence indicated that the seismic force of the earthquake was consistent with the regional stress field.

The Fifth Edition of the General Catalogue of Variable Stars:experiences in the constellation Centaurus

We have recently announced that the General Catalogue of Variable Stars enters the stage of its fifth, purely electronic edition （GCVS 5.1）. We have included 1408 variable stars in the constellation Centaurus in this new version, GCVS 5.1. Working on this revision, we applied current possibilities from data mining, suggested new variability types for many variable stars and found new light elements for a large number of periodic variables. This paper describes the work completed during the preparation of GCVS 5.1 for Centaurus and discusses in detail a number of the most astrophysically significant cases.

A statistical classification of the unassociated gamma-ray sources in the second Fermi Large Area Telescope Catalog

With the assistance of the identified/associated sources in the second Fermi Large Area Telescope （LAT） catalog, we analyze and resolve the spatial distribution and the distributions of the gamma-ray spectral and variability indices of the remaining 575 unassociated Fermi LAT sources. Consequently, it is suggested that the unassociated sources could statistically consist of Galactic supernova remnants/pulsar wind nebulae, BL Lacertae objects, fiat spectrum radio quasars and other types of active galaxies with fractions of 25%, 29%, 41% and 5%, respectively.

The Quasar Candidate Catalogs of DESI Legacy Imaging Survey Data Release 9

Quasars can be used to measure baryon acoustic oscillations at high redshift, which are considered as direct tracers of the most distant large-scale structures in the universe. It is fundamental to select quasars from observations before implementing the above research. This work focuses on creating a catalog of quasar candidates based on photometric data to provide primary priors for further object classification with spectroscopic data in the future, such as the Dark Energy Spectroscopic Instrument(DESI) Survey. We adopt a machine learning algorithm(Random Forest, RF) for quasar identification. The training set includes 651,073 positives and 1,227,172 negatives, in which the photometric information are from DESI Legacy Imaging Surveys(DESI-LIS) and Wide-field Infrared Survey Explore(WISE), and the labels are from a database of spectroscopically confirmed quasars based on Sloan Digital Sky Survey and the Set of Identifications& Measurements and Bibliography for Astronomical Data. The trained RF model is applied to point-like sources in DESI-LIS Data Release 9. To quantify the classifier’s performance, we also inject a testing set into the to-be-applied data.Eventually, we obtained 1,953,932 Grade-A quasar candidates and 22,486,884 Grade-B quasar candidates out of425,540,269 sources(~5.7%). The catalog covers ~99% of quasars in the to-be-applied data by evaluating the completeness of the classification on the testing set. The statistical properties of the candidates agree with that given by the method of color-cut selection. Our catalog can intensely decrease the workload for confirming quasars with the upcoming DESI data by eliminating enormous non-quasars but remaining high completeness. All data in this paper are publicly available online.

A compilation of known QSOs for the Gaia mission

Quasars are essential for astrometry in the sense that they are spatially stationary because of their large distance from the Sun. The European Space Agency(ESA) astrometric satellite Gaia is scanning the whole sky with unprecedented accuracy up to the level of a few μas. However, Gaia's two-fields-ofview-observation strategy may introduce a parallax bias in the Gaia catalog. Since it presents no significant parallax, a quasar is the perfect natural object to detect such bias. More importantly, quasars can be used to construct a celestial reference frame in the optical wavelengths for the Gaia mission. In this paper, we compile a list of known quasars that have been published in the literatures. The final compilation(designated as Known Quasars Catalog for Gaia mission, KQCG) contains 1 842 076 objects, among which797 632 objects are found in Gaia DR1 after cross-identifications. Redshift, color information and position uncertainties of the objects are also provided if available. This catalog will be very useful for the Gaia mission.

The composite X-ray spectra of radio-loud and radio-quiet SDSS quasars

We present a study of the X-ray emission for a sample of radio-detected quasars constructed from the cross-matches between SDSS,FIRST catalogs and XMM-Newton archives.A sample of radio-quiet SDSS quasars without FIRST radio detection is also assembled for comparison.We construct the optical and X-ray composite spectra normalized at rest frame 4215 A(or 2200 A)for both radio-loud quasars(RLQs)and radio-quiet quasars(RQQs)at z≤3.2,with matched X-ray completeness of 19%,redshift and optical luminosity.While the optical composite spectrum of RLQs is similar to that of RQQs,we find that RLQs have a higher X-ray composite spectrum than RQQs,consistent with previous studies in the literature.By dividing the radio-detected quasars into radio loudness bins,we find the X-ray composite spectra are generally higher with increasing radio loudness.Moreover,a significant correlation is found between the optical-to-X-ray spectral index and radio loudness,and there is a unified multi-correlation between the radio and X-ray luminosities and radio loudness in radio-detected quasars.These results could be possibly explained with the corona-jet model,in which the corona and jet are directly related.

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 feasibility and flexibility of selecting quasars by variability using ensemble machine learning algorithms

In this work,we train three decision-tree based ensemble machine learning algorithms(Random Forest Classifier,Adaptive Boosting and Gradient Boosting Decision Tree respectively)to study quasar selection in the variable source catalog in SDSS Stripe 82.We build training and test samples(both containing 1:1 of quasars and stars)using the spectroscopic confirmed sources in SDSS DR14(including8330 quasars and 3966 stars).We find that when trained with variation parameters alone,all three models can select quasars with similarly and remarkably high precision and completeness(~98.5%and 97.5%),even better than trained with SDSS colors alone(~97.2%and 96.5%),consistent with previous studies.By applying the trained models on the variable sources without spectroscopic identifications,we estimate the spectroscopically confirmed quasar sample in Stripe 82 variable source catalog is~93%complete(95%for mi<19.0).Using the Random Forest Classifier we derive the relative importance of the observational features utilized for classifications.We further show that even using one-or two-year time domain observations,variability-based quasar selection could still be highly efficient.

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.

TeV cosmic-ray proton and helium spectra in the myriad model Ⅱ

Recent observations show that the spectra of cosmic ray nuclei start to harden above ～ 102 GeV, which contradicts the conventional steady-state cosmic ray model. We had suggested that this anomaly is due to the propagation effect of cosmic rays released from local young cosmic ray sources; the total flux of cosmic rays should be computed with the Myriad Model, where a contribution from sources in the local catalog is added to the background. However, although the hardening could be elegantly explained in this model, the model parameters obtained from the fit are skewed toward a region with fast diffusion and a low supernova rate in the Galaxy, in disagreement with other observations. We further explore this model in order to set up a concordant picture. Two possible improvements related to cosmic ray sources are considered. First, instead of the usual axisymmetric disk model, we examine a spiral model for the source distribution. Second, for nearby and young sources which are necessary to explain the hardening, we allow for an energy-dependent escape. We find that a major improvement comes from incorporating an energy-dependent escape time for local sources, and with both modifications not only are the cosmic ray proton and helium anomalies resolved, but also the parameters attain values in a reasonable range compatible with other analyses.

LAMOST Time-Domain survey:first results of four K2 plates

From Oct.2019 to Apr.2020,LAMOST performed a time-domain(TD)spectroscopic survey of four K2 plates with both low-and medium-resolution observations.The low-resolution spectroscopic survey acquired 282 exposures(≈46.6 h)over 25 nights,yielding a total of about 767000 spectra,and the medium-resolution survey took 177 exposures(≈49.1 h)over 27 nights,collecting about 478000 spectra.More than 70%/50%of low-resolution/medium-resolution spectra have signal-to-noise ratio higher than 10.We determine stellar parameters(e.g.,Teff,log g,[Fe/H])and radial velocity(RV)with different methods,including LASP,DD-Payne and SLAM.In general,these parameter estimations from different methods show good agreement,and the stellar parameter values are consistent with those of APOGEE.We use the Gaia DR2 RV values to calculate a median RV zero point(RVZP)for each spectrograph exposure by exposure,and the RVZP-corrected RVs agree well with the APOGEE data.The stellar evolutionary and spectroscopic masses are estimated based on the stellar parameters,multi-band magnitudes,distances and extinction values.Finally,we construct a binary catalog including about 2700 candidates by analyzing their light curves,fitting the RV data,calculating the binarity parameters from medium-resolution spectra and cross-matching the spatially resolved binary catalog from Gaia EDR3.The LAMOST TD survey is expected to represent a breakthrough in various scientific topics,such as binary systems,stellar activity,stellar pulsation,etc.

GeneticKNN:a weighted KNN approach supported by genetic algorithm for photometric redshift estimation of quasars

We combine K-nearest neighbors(KNN)with a genetic algorithm(GA)for photometric redshift estimation of quasars,short for GeneticKNN,which is a weighted KNN approach supported by a GA.This approach has two improvements compared to KNN:one is the feature weighted by GA;the other is that the predicted redshift is not the redshift average of K neighbors but the weighted average of median and mean of redshifts for K neighbors,i.e.p×zmedian+(1-p)×zmean.Based on the SDSS and SDSS-WISE quasar samples,we explore the performance of GeneticKNN for photometric redshift estimation,comparing with the other six traditional machine learning methods,i.e.the least absolute shrinkage and selection operator(LASSO),support vector regression(SVR),multi-layer perceptrons(MLP),XGBoost,KNN and random forest.KNN and random forest show their superiority.Considering the easy implementation of KNN,we make improvement on KNN as GeneticKNN and apply GeneticKNN on photometric redshift estimation of quasars.Finally the performance of GeneticKNN is better than that of LASSO,SVR,MLP,XGBoost,KNN and random forest for all cases.Moreover the accuracy is better with the additional WISE magnitudes for the same method.

A new strategy for estimating photometric redshifts of quasars

Based on the SDSS and SDSS-WISE quasar datasets, we put forward two schemes to estimate the photometric redshifts of quasars. Our schemes are based on the idea that the samples are firstly classified into subsamples by a classifier and then a photometric redshift estimation of different subsamples is performed by a regressor. Random Forest is adopted as the core algorithm of the classifiers, while Random Forest and k NN are applied as the key algorithms of regressors. The samples are divided into two subsamples and four subsamples, depending on the redshift distribution. The performances based on different samples, different algorithms and different schemes are compared. The experimental results indicate that the accuracy of photometric redshift estimation for the two schemes generally improves to some extent compared to the original scheme in terms of the percents in |△z|1+zi< 0.1 and |△z|1+zi<0.2 and mean absolute error. Only given the running speed, k NN shows its superiority to Random Forest. The performance of Random Forest is a little better than or comparable to that of k NN with the two datasets. The accuracy based on the SDSS-WISE sample outperforms that based on the SDSS sample no matter by k NN or by Random Rorest. More information from more bands is considered and helpful to improve the accuracy of photometric redshift estimation. Evidently, it can be found that our strategy to estimate photometric redshift is applicable and may be applied to other datasets or other kinds of objects. Only talking about the percent in |△z|1+zi<0.3, there is still large room for further improvement in the photometric redshift estimation.

Photometric redshift estimation for quasars by integration of KNN and SVM

The massive photometric data collected from multiple large-scale sky surveys offer significant opportunities for measuring distances of celestial objects by photometric redshifts. However, catastrophic failure is an unsolved problem with a long history and it still exists in the current photometric redshift estimation approaches （such as the k-nearest neighbor （KNN） algorithm）. In this paper, we propose a novel two-stage approach by integration of KNN and support vector machine （SVM） methods together. In the first stage, we apply the KNN algorithm to photometric data and estimate their corresponding Zphot. Our analysis has found two dense regions with catastrophic failure, one in the range of Zphot E [0.3, 1.2] and the other in the range of Zphot E [1.2, 2.1]. In the second stage, we map the photometric input pattern of points falling into the two ranges from their original attribute space into a high dimensional feature space by using a Gaussian kernel function from an SVM. In the high dimensional feature space, many outliers resulting from catastrophic failure by simple Euclidean distance computation in KNN can be identified by a classification hyperplane of SVM and can be further corrected. Experimental results based on the Sloan Digital Sky Survey （SDSS） quasar data show that the two-stage fusion approach can significantly mitigate catastrophic failure and improve the estimation accuracy of photometric redshifts of quasars. The percents in different /△z/ ranges and root mean square （rms） error by the integrated method are 83.47%, 89.83%, 90.90% and 0.192, respectively, compared to the results by KNN （71.96%, 83.78%, 89.73% and 0.204）.

Machine learning classification of Gaia Data Release 2

Machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculate suggestions for large amounts of data. We apply machine learning clas-sification to 85 613 922 objects in the Gala Data Release 2, based on a combination of Pan-STARRS I and AI1WISE data. The classification results are cross-matched with the Simbad database, and the to-tal accuracy is 91.9%. Our sample is dominated by stars, ~98%, and galaxies make up 2%. For the objects with negative parallaxes, about 2.5% are galaxies and QSOs, while about 99.9% are stars if the relative parallax uncertainties are smaller than 0.2. Our result implies that using the threshold of 0 〈 σπ/π 〈 0.2 could yield a very clean stellar sample.

A Search for Exoplanets in Open Clusters and Young Associations based on TESS Objects of Interest

We report the results of our search for planet candidates in open clusters and young stellar associations based on the Transiting Exoplanet Survey Satellite(TESS) Objects of Interest Catalog.We find one confirmed planet,one promising candidate,one brown dwarf and three unverified planet candidates in a sample of 1229 open clusters from the second Gaia data release.We discuss individual planet-star systems based on their basic parameters,membership probability and the observation notes from the ExoFOP-TESS website.We also find ten planet candidates(P> 95%) in young stellar associations by using the BANYAN ∑ Multivariate Bayesian Algorithm.Among the ten candidates,five are known planetary systems.We estimate the rotation periods of the host stars using the TESS light curves and estimate their ages based on gyrochronology.Two candidates with periodic variations are likely to be young planets,but their exact memberships to young stellar associations remain unknown.

Galactic coordinate system based on multi-wavelength catalogs

The currently used Galactic coordinate system（GalC S） is based on the FK5 system at J2000.0, which was transformed from the FK4 system at B1950.0.The limitations and misunderstandings related to this transformed GalC S can be avoided by defining a new GalC S that is directly connected to the International Celestial Reference System（ICRS）. With more data at various wavelengths released by large survey programs, a more appropriate GalC S consistent with features associated with the Milky Way can be established. We try to find the best orientation of the GalC S using data from two all-sky surveys, AKARI and WISE, at six wavelengths between 3.4 μm and 90 μm, and synthesize results obtained at various wavelengths to define an improved GalC S in the framework of the ICRS. The revised GalC S parameters for defining the new GalC S in the ICRS are summarized as：αp= 192.777°, δp= 26.9298°, for the equatorial coordinates of the north Galactic pole and θ = 122.95017° for the position angle of the Galactic center. As one of the Galactic substructures, the Galactic warp exhibits different forms in different GalC Ss that are constructed with various data and methods, which shows the importance of re-defining the GalC S by the relative commission of the International Astronomical Union that can lead to a better understanding of Galactic structure and kinematics.

Candidate members of star clusters from LAMOST DR2

In this work, we provide 2189 photometrically- and kinematically-selected candidate members of 24 star clusters from the LAMOST DR2 catalog. We perform two-step membership identification： selection along the stellar track in the colormagnitude diagram, i.e., photometric identification, and selection from the distribution of radial velocities, i.e. the kinematic identification. We find that the radial velocities from the LAMOST data are very helpful in the membership identification. The mean probability of membership is 40% for the sample selected with radial velocity. With these 24 star clusters, we investigate the performance of the radial velocity and metallicity estimated with the LAMOST pipeline. We find that the systematic offsets in radial velocity and metallicity are 0.85 ± 1.26 km s-1and-0.08 ± 0.04 dex, with dispersions of 5.47＋1.16-0.71 km s-1and 0.13＋0.04-0.02 dex, respectively. Finally, we propose that the photometrically-selected candidate members of the clusters covered by the LAMOST footprint should be assigned higher priority so that more candidate stars can be observed.

Sharp Chandra View of ROSAT All-Sky Survey Bright Sources I. Improvement of Positional Accuracy

The ROSAT All-Sky Survey （RASS） represents one of the most complete and sensitive soft X- ray all-sky surveys to date. However, the deficient positional accuracy of the RASS Bright Source Catalog （BSC） and subsequent lack of firm optical identifications affect multi-wavelength studies of X-ray sources. The widely used positional errors σpos based on the Tycho Reference Catalog （Tycho-1） have previously been applied for identifying objects in the optical band. The considerably sharper Chandra view covers a fraction of RASS sources, whose ~rpos could be improved by utilizing the sub-arcsec positional accuracy of Chandra observations. We cross-match X-ray objects between the BSC and Chandra sources extracted from the Advanced CCD Imaging Spectrometer （ACIS） archival observations. A combined list of counter- parts （BSCxACIS） with Chandra spatial positions weighted by the X-ray flux of multiple counterparts is employed to evaluate and improve the former identifications of BSC when used with other surveys. Based on these identification evaluations, we suggest that the point-source likeness of BSC sources and INS （iso- lated neutron star） candidates should be carefully reconsidered.