J-PLUS: Support Vector Regression to Measure Stellar Parameters
Cunshi Wang, Yu Bai, Haibo Yuan, Jifeng Liu, J. A. Fernández-Ontiveros, Paula R. T. Coelho, F. Jiménez-Esteban, Carlos Andrés Galarza, R. E. Angulo, A. J. Cenar… 展开作者
Cunshi Wang, Yu Bai, Haibo Yuan, Jifeng Liu, J. A. Fernández-Ontiveros, Paula R. T. Coelho, F. Jiménez-Esteban, Carlos Andrés Galarza, R. E. Angulo, A. J. Cenarro, D. Cristóbal-Hornillos, R. A. Dupke, A. Ederoclite, C. Hernández-Monteagudo, C. López-Sanjuan, A. Marín-Franch, M. Moles, L. Sodré, H. Vázquez Ramió, J. Varela
Context. Stellar parameters are among the most important characteristics in studies of stars, which are based on atmosphere models in traditional methods. However, time cost and brightness limits restrain the efficiency of spectral observations. The J-PLUS is an observational campaign that aims to obtain photometry in 12 bands. Owing to its characteristics, J-PLUS data have become a valuable resource for studies of stars. Machine learning provides powerful tools to efficiently analyse large data sets, such as the one from J-PLUS, and enable us to expand the research domain to stellar parameters. Aims. The main goal of this study is to construct a SVR algorithm to estimate stellar parameters of the stars in the first data release of the J-PLUS observational campaign. Methods. The training data for the parameters regressions is featured with 12-waveband photometry from J-PLUS, and is cross-identified with spectrum-based catalogs. These catalogs are from the LAMOST, the APOGEE, and the SEGUE. We then label them with the stellar effective temperature, the surface gravity and the metallicity. Ten percent of the sample is held out to apply a blind test. We develop a new method, a multi-model approach in order to fully take into account the uncertainties of both the magnitudes and stellar parameters. The method utilizes more than two hundred models to apply the uncertainty analysis. Results. We present a catalog of 2,493,424 stars with the Root Mean Square Error of 160K in the effective temperature regression, 0.35 in the surface gravity regression and 0.25 in the metallicity regression. We also discuss the advantages of this multi-model approach and compare it to other machine-learning methods.
Stellar characterization with photometric colors from J-PLUS and 2MASS surveys
Universidad Autónoma de Madrid（马德里自治大学）；Universidad Militar Nueva Granada（新格兰纳达军事大学）；Centro de Astrobiología (CAB), CSIC-INTA（天体生物学中心 (CAB)，西班牙国家科学研究委员会-国家航空航天技术研究所）；Universidade de São Paulo, Instituto de Astronomia, Geofísica e Ciências Atmosféricas（圣保罗大学，天文、地球物理与大气科学研究所）；Centro de Estudios de Física del Cosmos de Aragón (CEFCA)（阿拉贡宇宙物理学研究中心 (CEFCA)）；NSF NOIRLab（美国国家科学基金会NOIR实验室）；Instituto de Astrofísica de Andalucía – Consejo Superior de Investigaciones Científicas (IAA-CSIC)（安达卢西亚天体物理研究所–西班牙国家科学研究委员会 (IAA-CSIC)）
Correction to the photometric colors of the Gaia Data Release 2 with the stellar color regression method
第二次盖亚数据发布（DR 2）提供了整个天空的精确和均匀的测光数据，达到了G，GRP和GBP通带前所未有的毫量级（mmag）水平。然而，在10 mmag水平上存在的星等依赖系统效应限制了其在科学开发中的力量。在这项工作中，使用大约50万颗与LAMOST DR 5相同的恒星，我们应用基于光谱的恒星颜色回归方法来校准Gaia G-GRP和GBP-GRP的颜色。以前所未有的精度约1毫等，系统的趋势与G星等揭示了两种颜色的非常详细，反映了仪器配置的变化。对GBP-GRP和亮度大于G~11.5等的恒星，发现了颜色依赖的趋势，刻度一般可达20 mmag，变化范围为几mmag/mag，给出了Gaia DR 2的修正色图，并简要讨论了一些应用。
Photometric calibration methods for wide-field photometric surveys
在当前和下一代宽视场成像测量中，均匀、准确的测光定标起着重要的作用。在此，我们回顾了现代光度校准方法，包括经典的标准星方法，“硬件/观测驱动”方法（如Ubercalibration，Hypercalibration和Forward Global Calibration方法）和“软件/物理驱动”方法（例如，恒星轨迹回归、恒星轨迹和恒星颜色回归方法）。此外，我们还讨论了它们的优点，局限性和未来的发展，以毫米级精度校准。
Analysis of spatially resolved stellar populations and emission line properties in nearby galaxies with J-PLUS data. II-Results for the M51 group and first comparison with the M101 group
Instituto de Astrofísica de Andalucía (CSIC)（安达卢西亚天体物理研究所）；Universidade Federal de Santa Catarina（圣卡塔琳娜联邦大学）
Stellar Density Classification and Regression for CSST Multi-color Imaging Using Deep Learning
National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；School of Astronomy and Space Science, University of Chinese Academy of Sciences（中国科学院大学天文与空间科学学院）；Institute for Frontiers in Astronomy and Astrophysics, Beijing Normal University（北京师范大学天文与前沿科学研究院）；School of Physics and Astronomy, China West Normal University（西华师范大学物理与天文学学院）
Photometric recalibration of the SDSS Stripe 82 to a few milimagnitude precision with the stellar color regression method and Gaia EDR3
通过结合LAMOST DR 7、SDSS DR 12的光谱数据和Gaia EDR 3的校正光度数据，我们应用恒星颜色回归（SCR; Yuan et al. 2015 a）方法重新校准了Ivezić et al.（2007）的SDSS Stripe 82标准星表。在总共约30，000颗光谱目标恒星的情况下，我们绘制出了星表中存在的相对较大和强相关的光度零点误差，在$u$波段中$\sim$为2.5%，在$griz$波段中$\sim$为1%。我们的研究还证实了一些小的，但重要的幅度依赖性的错误，在$z$带的一些电荷耦合器件。各种测试表明，我们已经实现了在$u$频带中约5 mmag和在$griz$频带中约2 mmag的内部精度，这比以前的结果好约5倍。我们还将该方法应用于最新版本的星表（V4.2; Thanjavur et al. 2021），并发现沿着R. A的适度系统校准误差高达$\sim$ 1%。方向和较小的误差沿十二月方向。结果表明，SCR方法的功率时，结合光谱数据和盖亚光度测量在突破1%的精度障碍的地面光度测量。我们的工作为整个SDSS光度测量的重新校准铺平了道路，并对未来测量的校准具有重要意义。最后讨论了SCR方法在不同情况下的应用和改进。
