arXiv subjects

Jin-Sheng Qiu

Publications and source records attributed to Jin-Sheng Qiu.

5 recordsLinked to original sources

arXiv2026

The atmospheric extinction curve at Lenghu site

In this study, we use a dedicated spectroscopic telescope to carry out low-resolution measurements of the optical atmospheric extinction curve at Lenghu astronomical site in Qinghai Province, China. Observations of A0-type stars are conducted over multiple nights between 2024 and 2026, covering airmasses from 1.0 to 2.0 and wavelengths in the range of 400 to 800 nm. We derive the extinction curve for the Lenghu site and compare it with those from Mauna Kea and Cerro Paranal.

astro-ph.IM↗

arXiv2025

The Statistical Analysis Of The Galactic Open Clusters' Structure

We present a systematic investigation of 1,481 Galactic open clusters (OCs) through the application of the Limepy dynamical model, from which we derive the fundamental structural parameters of OCs. We conduct the statistical analyses on the structural parameters with clusters' ages and locations within the Milky Way. Our results reveal the higher concentration in the cluster centeris associated with the sharper truncation at the periphery of cluster, which is consistent with previous findings for globular clusters(GCs). We further find the systematic increase of the lower limit of clusters' half-mass radius (Rh) with age. Our results also show that OCs located at larger vertical distances from the Galactic plane systematically display higher central concentrations. Our findings collectively suggest that the structural characteristics of OCs are shaped by both intrinsic evolutionary processes and interactions with the Galactic environment. During the evolution of star clusters, the combined effects of mass segregation and tidal stripping lead to the systematic pattern between central concentration and outer truncation. Clusters of different ages and locations within the Milky Way undergo different evolutionary histories, resulting in correlations between the Rh and age, as well as between central concentration and galactic location.

astro-ph.GA↗

arXiv2025

A Challenge of Developing a Classifier for Multi-Band Classification of Variable Stars

Variable stars play a very important role in our understanding of the Milky Way and the universe. In recent years, many survey projects have generated a large amount of photometric data, necessitating classifiers that can quickly identify various types of variable stars. However, obtaining these classifiers often requires substantial manpower and computational resources. To conserve these resources, it would be best to have a classifier that can be used across surveys. We explore the possibility that a classifier created in one optical band can also work in other bands, likely from different survey facilities. We construct a random forest classifier based on photometric data in ASAS-SN V-band and OGLE I-band, and apply the classifier on ASAS-SN V-band and ZTF r-band light curves of variable star samples. We explore the classification differences of using the magnitude light-curves or the normalized flux light-curves, the periods derived from single band light-curves or the periods derived from the multi-band combined light-curves, and with or without color-related features. We find it feasible to develop a classifier capable of working in both V and r bands for certain types of variable stars, such as RRAB variables. For other types of variable stars, like Cepheids, the classifier is unable to make accurate identifications.

astro-ph.IM↗

arXiv2024

Physical Parameters of 11,100 Short-Period ASAS-SN Eclipsing Contact Binaries

Starting from more than 11,200 short-period (less than 0.5 days) EW-type eclipsing binary candidates with the All-Sky Automated Survey for Supernovae (ASAS-SN) V-band light curves, we use MCMC and neural networks (NNs) to obtain the mass ratio ($q$), orbital inclination ($incl$), fill-out factor ($f$) and temperature ratio ($T_s/T_p$). After cross-matching with the Gaia DR3 database, the final sample contains parameters of 2,399 A-type and 8,712 W-type contact binaries (CBs). We present the distributions of parameters of these 11,111 short-period CBs. The mass ratio ($q$) and fill-out factor ($f$) are found to obey log-normal distributions, and the remaining parameters obey normal distributions. There is a significant period-temperature correlation of these CBs. Additionally, the temperature ratio (${T_s}$/${T_p}$) tends to increase as the orbital period decreases for W-type CBs. There is no significant correlation between them for A-type CBs. The mass ratio and fill-out factor ($q-f$) diagram suggest there is no significant correlation between these two parameters. A clear correlation exists between the mass ratio and radius ratio. The radius ratio increases with the mass ratio. Moreover, the deep fill-out CBs tend to fall on the upper boundary of the $q$$-$${R_s}$/${R_p}$ distribution, while the shallow fill-out CBs fall on the lower boundary.

astro-ph.SR↗

arXiv2022

A New Period Determination Method for Periodic Variable Stars

Variable stars play a key role in understanding the Milky Way and the universe. The era of astronomical big data presents new challenges for quick identification of interesting and important variable stars. Accurately estimating the periods is the most important step to distinguish different types of variable stars. Here, we propose a new method of determining the variability periods. By combining the statistical parameters of the light curves, the colors of the variables, the window function and the GLS algorithm, the aperiodic variables are excluded and the periodic variables are divided into eclipsing binaries and NEB variables (other types of periodic variable stars other than eclipsing binaries), the periods of the two main types of variables are derived. We construct a random forest classifier based on 241,154 periodic variables from the ASAS-SN and OGLE datasets of variables. The random forest classifier is trained on 17 features, among which 11 are extracted from the light curves and 6 are from the Gaia Early DR3, ALLWISE and 2MASS catalogs. The variables are classified into 7 superclasses and 17 subclasses. In comparison with the ASAS-SN and OGLE catalogs, the classification accuracy is generally above approximately 82% and the period accuracy is 70%-99%. To further test the reliability of the new method and classifier, we compare our results with the results of Chen et al. (2020) for ZTF DR2. The classification accuracy is generally above 70%. The period accuracy of the EW and SR variables is 50% and 53%, respectively. And the period accuracy of other types of variables is 65%-98%.

astro-ph.IM↗