South Galactic Cap u-band Sky Survey(SCUSS):Project Overview

The South Galactic Cap u-band Sky Survey （SCUSS） was established in 2009 in order to provide a photometric input catalog for target selection of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope （LAMOST） project. SCUSS is an international cooperative project between National Astronomical Observatories, Chinese Academy of Sciences, and Steward Observatory at the University of Arizona, using the 90 inch （2.3 m） Bok telescope on Kitt Peak. The telescope is equipped with a prime focus camera that is composed of a mosaic of four 4096 × 4096 CCDs and has a field of view of about 1 deg2. From 2009 to 2013, SCUSS performed a sky survey of an approximately 5000 deg2 field of the South Galactic Cap in u band, including the Galactic anticenter area and the SDSS-IV extended imaging area. The limiting magnitude of SCUSS is deeper than 23 mag （at a signal-to-noise ratio of 5）. In this paper, we briefly describe the goals of this project, method of observations and data reduction, and we also introduce current and potential scientific activities related to the SCUSS project.

Profiling Astronomical Objects Using Unsupervised Learning Approach

Attempts to determine characters of astronomical objects have been one of major and vibrant activities in both astronomy and data science fields.Instead of a manual inspection,various automated systems are invented to satisfy the need,including the classification of light curve profiles.A specific Kaggle competition,namely Photometric LSST Astronomical Time-Series Classification Challenge(PLAsTiCC),is launched to gather new ideas of tackling the abovementioned task using the data set collected from the Large Synoptic Survey Telescope(LSST)project.Almost all proposed methods fall into the supervised family with a common aim to categorize each object into one of pre-defined types.As this challenge focuses on developing a predictive model that is robust to classifying unseen data,those previous attempts similarly encounter the lack of discriminate features,since distribution of training and actual test datasets are largely different.As a result,well-known classification algorithms prove to be sub-optimal,while more complicated feature extraction techniques may help to slightly boost the predictive performance.Given such a burden,this research is set to explore an unsupervised alternative to the difficult quest,where common classifiers fail to reach the 50%accuracy mark.A clustering technique is exploited to transform the space of training data,from which a more accurate classifier can be built.In addition to a single clustering framework that provides a comparable accuracy to the front runners of supervised learning,a multiple-clustering alternative is also introduced with improved performance.In fact,it is able to yield a higher accuracy rate of 58.32%from 51.36%that is obtained using a simple clustering.For this difficult problem,it is rather good considering for those achieved by well-known models like support vector machine(SVM)with 51.80%and Naive Bayes(NB)with only 2.92%.

Prospects for measuring dark energy with 21 cm intensity mapping experiments:A joint survey strategy

The 21 cm intensity mapping(IM)technique provides us with an efficient way to observe the cosmic large-scale structure(LSS).From the LSS data,one can use the baryon acoustic oscillation and redshift space distortion to trace the expansion and growth history of the universe,and thus measure the dark energy parameters.In this paper,we make a forecast for cosmological parameter estimation with the synergy of three 21 cm IM experiments.Specifically,we adopt a novel joint survey strategy,FAST(0<z<0.35)+SKA1-MID(0.35<z<0.8)+HIRAX(0.8<z<2.5),to measure dark energy.We simulate the 21 cm IM observations under the assumption of excellent foreground removal.We find that the synergy of three experiments could place quite tight constraints on cosmological parameters.For example,it providesσ(?m)=0.0039 andσ(H0)=0.27 km s^(-1) Mpc^(-1) in theΛCDM model.Notably,the synergy could break the cosmological parameter degeneracies when constraining the dynamical dark energy models.Concretely,the joint observation offersσ(w)=0.019 in the wCDM model,andσ(w0)=0.085 andσ(wa)=0.32 in the w0waCDM model.These results are better than or equal to those given by CMB+BAO+SN.In addition,when the foreground removal efficiency is relatively low,the strategy still performs well.Therefore,the 21 cm IM joint survey strategy is promising and worth pursuing.

The first comprehensive Milky Way stellar mock catalogue for the Chinese Space Station Telescope Survey Camera

The Chinese Space Station Telescope(CSST)is a cutting-edge two-meter astronomical space telescope currently under construction.Its primary Survey Camera(SC)is designed to conduct large-area imaging sky surveys using a sophisticated seven-band photometric system.The resulting data will provide unprecedented data for studying the structure and stellar populations of the Milky Way.To support the CSST development and scientific projects related to its survey data,we generate the first comprehensive Milky Way stellar mock catalogue for the CSST SC photometric system using the TRILEGAL stellar population synthesis tool.The catalogue includes approximately 12.6 billion stars,covering a wide range of stellar parameters,photometry,astrometry,and kinematics,with magnitude reaching down to g=27.5 mag in the AB magnitude system.The catalogue represents our benchmark understanding of the stellar populations in the Milky Way,enabling a direct comparison with the future CSST survey data.Particularly,it sheds light on faint stars hidden from current sky surveys.Our crowding limit analysis based on this catalogue provides compelling evidence for the extension of the CSST Optical Survey(OS)to cover low Galactic latitude regions.The strategic extension of the CSST-OS coverage,combined with this comprehensive mock catalogue,will enable transformative science with the CSST.