The Jiao Tong University Spectroscopic Telescope Project

The Jiao Tong University Spectroscopic Telescope(JUST)is a 4.4-meter f/6.0 segmented-mirror telescope dedicated to spectroscopic observations.The JUST primary mirror is composed of 18 hexagonal segments,each with a diameter of 1.1 m.JUST provides two Nasmyth platforms for placing science instruments.One Nasmyth focus fits a field of view of 10′and the other has an extended field of view of 1.2°with correction optics.A tertiary mirror is used to switch between the two Nasmyth foci.JUST will be installed at a site at Lenghu in Qinghai Province,China,and will conduct spectroscopic observations with three types of instruments to explore the dark universe,trace the dynamic universe,and search for exoplanets:(1)a multi-fiber(2000 fibers)medium-resolution spectrometer(R=4000-5000)to spectroscopically map galaxies and large-scale structure;(2)an integral field unit(IFU)array of 500 optical fibers and/or a long-slit spectrograph dedicated to fast follow-ups of transient sources for multi-messenger astronomy;(3)a high-resolution spectrometer(R~100000)designed to identify Jupiter analogs and Earth-like planets,with the capability to characterize the atmospheres of hot exoplanets.

Preparation for CSST:Star-galaxy Classification using a Rotationally Invariant Supervised Machine Learning Method

Most existing star-galaxy classifiers depend on the reduced information from catalogs,necessitating careful data processing and feature extraction.In this study,we employ a supervised machine learning method(GoogLeNet)to automatically classify stars and galaxies in the COSMOS field.Unlike traditional machine learning methods,we introduce several preprocessing techniques,including noise reduction and the unwrapping of denoised images in polar coordinates,applied to our carefully selected samples of stars and galaxies.By dividing the selected samples into training and validation sets in an 8:2 ratio,we evaluate the performance of the GoogLeNet model in distinguishing between stars and galaxies.The results indicate that the GoogLeNet model is highly effective,achieving accuracies of 99.6% and 99.9% for stars and galaxies,respectively.Furthermore,by comparing the results with and without preprocessing,we find that preprocessing can significantly improve classification accuracy(by approximately 2.0% to 6.0%)when the images are rotated.In preparation for the future launch of the China Space Station Telescope(CSST),we also evaluate the performance of the GoogLeNet model on the CSST simulation data.These results demonstrate a high level of accuracy(approximately 99.8%),indicating that this model can be effectively utilized for future observations with the CSST.

Establishing HI mass vs. stellar mass and halo mass scaling relations using an abundance matching method

We combined data from the Sloan Digital Sky Survey(SDSS) and the Arecibo Legacy Fast ALFA Survey(ALFALFA) to establish the HI mass vs. stellar mass and halo mass scaling relations using an abundance matching method that is free of the Malmquist bias. To enable abundance matching, a cross-match between the SDSS DR7 galaxy group sample and the ALFALFA HI sources provides a catalog of 16520 HI-galaxy pairs within 14270 galaxy groups(halos). By applying the observational completeness reductions for both optical and HI observations, we used the remaining 8180 ALFALFA matched sources to construct the model constraints. Taking into account the dependence of HI mass on both the galaxy and group properties, we establish two sets of scaling relations: one with a combination of stellar mass,(g-r) color and halo mass, and the other with stellar mass,specific star-formation rate(sSFR), and halo mass. We demonstrate that our models can reproduce the HI mass component as both stellar mass and halo mass. Additional tests showed that the conditional HI mass distributions as a function of the cosmic web type and the satellite fractions were well recovered.

DESI Legacy Imaging Surveys Data Release 9:Cosmological constraints from galaxy clustering and weak lensing using the minimal bias model

We present a tentative constraint on cosmological parameters Ω_(m) and σ_(8) from a joint analysis of galaxy clustering and galaxygalaxy lensing from DESI Legacy Imaging Surveys Data Release 9(DR9),covering approximately 10000 square degrees and spanning the redshift range of 0.1 to 0.9.To study the dependence of cosmological parameters on lens redshift,we divide lens galaxies into seven approximately volume-limited samples,each with an equal width in photometric redshift.To retrieve the intrinsic projected correlation function w_(p)(r_(p))from the lens samples,we employ a novel method to account for redshift uncertainties.Additionally,we measured the galaxy-galaxy lensing signal ΔΣ(r_(p))for each lens sample,using source galaxies selected from the shear catalog by applying our Fourier Quad pipeline to DR9 images.We model these observables within the flatΛCDM framework,employing the minimal bias model.To ensure the reliability of the minimal bias model,we apply conservative scale cuts:r_(p)>8 and 12 h^(-1)Mpc,for w_(p)(r_(p))and ΔΣ(r_(p)),respectively.Our findings suggest a mild tendency that S_(8)=σ_(8)√Ω_(m)/0.3increases with lens redshift,although this trend is only marginally significant.When we combine low redshift samples,the value of S8is determined to be 0.84±0.02,consistent with the Planck results but significantly higher than the 3×2 pt analysis by 2-5σ.Despite the fact that further refinements in measurements and modeling could improve the accuracy of our results,the consistency with standard values demonstrates the potential of our method for more precise and accurate cosmology in the future.