Low Surface Brightness Galaxies from BASS+MzLS with Machine Learning

From ∼5000 deg^(2) of the combination of the Beijing–Arizona Sky Survey and Mayall z-band Legacy Survey which is also the northern sky region of the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys, we selected a sample of 31,825 candidates of low surface brightness galaxies (LSBGs) with the mean effective surface brightness 24.2<μ_(eff.g)<28.8 mag arcsec^(−2) and the half-light radius 2.″5 < r_(eff)<20″ based on the released photometric catalog and the machine learning model. The distribution of the LSBGs is bimodal in the g−r color, indicating the two distinct populations of the blue (g−r< 0.60) and red (g−r> 0.60) LSBGs. The blue LSBGs appear spiral, disk or irregular while the red LSBGs are spheroidal or elliptical and spatially clustered. This trend shows that the color has a strong correlation with galaxy morphology for LSBGs. In the spatial distribution, the blue LSBGs are more uniformly distributed while the red ones are highly clustered, indicating that red LSBGs preferentially populate a denser environment than the blue LSBGs. Besides, both populations have a consistent distribution of ellipticity (median ∈∼ 0.3), half-light radius (median r_(eff) ∼ 4″) and Sérsic index (median n=1), implying the dominance of the full sample by the round and disk galaxies. This sample has definitely extended the studies of LSBGs to a regime of lower surface brightness, fainter magnitude and broader other properties than the previously Sloan Digital Sky Survey-based samples.

Kinematics and activity of M dwarfs in LAMOST DR1

We report on the first investigation into kinematics and chromospheric activity of M dwarfs from the Guo Shou Jing Telescope （also called the Large Sky Area Multi-Object Fiber Spectroscopic Telescope - LAMOST） data release one （DR1）. The sample comprises 71 304 M dwarfs. Their fundamental parameters such as spectral types, radial velocities, important molecular band indices and magnetic activities are measured. Their distances are determined by a spectroscopic parallax relation. Space motion （U, V, W） and Galactocentric cylindrical coordinates （R, θ, Z） for the M dwarfs are also computed. We examine velocity dispersion as a function of height from the Galactic plane and find that all three components of velocity dispersion in- crease with height as measured with respect to the Galactic plane. The investigation into chromospheric activities along the height from the Galactic plane confirms that M dwarfs closer to the Galactic plane are more likely to be active. We take a pure kinematical approach to select thin disk stars and thick disk stars from our sample, then to investigate the differences in properties between these two populations. Our analysis is in excellent agreement with previous studies and leads to a better understanding of the structure of the Galactic disk.

M Dwarf catalog of LAMOST general survey data release one

We present a spectroscopic catalog of 93 619 M dwarfs from the first data release of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope（LAMOST）general survey. During sample selection, M giant contamination was eliminated using2 MASS photometry and Ca H/TiO molecular indices. For each spectrum, the spectral subtype and values are provided including radial velocity, Hα equivalent width, a series of prominent molecular band indices, and the metal–sensitive parameter ζ, as well as distances and the space motions for high S/N objects. In addition, Hα emission lines are measured to examine the magnetic activity properties of M dwarfs and 7179 active ones are found. In particular, a subsample with significant variation in magnetic activity is revealed through observations from different epochs. Finally, statistical analysis for this sample is performed, including the metallicity classification, the distribution of molecular band indices and their errors.

Nearby M subdwarfs from LAMOST data release 2

We identify 108 M subdwarfs（sd Ms） out of more than two hundred thousand M type spectra from the second data release（DR2） of the LAMOST regular survey. This sample, among which 58 members are identified for the first time, includes 33 extreme subdwarfs（esd Ms） and 11 ultra subdwarfs（usd Ms）.The selection is based on the usual ratio of absorption depth of Ca H2, Ca H3 and TiO 5 band systems.We also emphasize the use of the Ca H1 band. We provide estimates of spectral subtype（SPT）, L′epine metallicity index ζ, effective temperature and [Fe/H]. Both ζ–[Fe/H] and SPT–Teff figures show reasonable consistency; compared to PHOENIX model spectra, average rounded values of [Fe/H] for sd Ms, esd Ms and usd Ms are respectively –0.5, –1 and –1.5. The photometric distances are estimated, indicating that most sources are located within 500 pc of the Sun and 350 pc of the Galactic disk. Velocities and 3D Galactic motions are also briefly discussed. Among the 108 subdwarfs, seven stars appear to be active with a significant Hα emission line. The source LAMOST J104521.52＋482823.3 is a white dwarf- M subdwarf binary, while LAMOST J123045.52＋410943.8, also active, exhibits carbon features in red.

A versatile framework for analyzing galaxy image data by incorporating Human-in-the-loop in a large vision model

The exponential growth of astronomical datasets provides an unprecedented opportunity for humans to gain insight into the Universe.However,effectively analyzing this vast amount of data poses a significant challenge.In response,astronomers are turning to deep learning techniques,but these methods are limited by their specific training sets,leading to considerable duplicate workloads.To overcome this issue,we built a framework for the general analysis of galaxy images based on a large vision model(LVM)plus downstream tasks(DST),including galaxy morphological classification,image restoration object detection,parameter extraction,and more.Considering the low signal-to-noise ratios of galaxy images and the imbalanced distribution of galaxy categories,we designed our LVM to incorporate a Human-in-the-loop(HITL)module,which leverages human knowledge to enhance the reliability and interpretability of processing galaxy images interactively.The proposed framework exhibits notable fewshot learning capabilities and versatile adaptability for all the abovementioned tasks on galaxy images in the DESI Legacy Imaging Surveys.In particular,for the object detection task,which was trained using 1000 data points,our DST in the LVM achieved an accuracy of 96.7%,while ResNet50 plus Mask R-CNN reached an accuracy of 93.1%.For morphological classification,to obtain an area under the curve(AUC)of~0.9,LVM plus DST and HITL only requested 1/50 of the training sets that ResNet18 requested.In addition,multimodal data can be integrated,which creates possibilities for conducting joint analyses with datasets spanning diverse domains in the era of multi-messenger astronomy.