This paper documents the 16th data release (DR16) from the Sloan Digital Sky Surveys (SDSS), the fourth and penultimate from the fourth phase (SDSS-IV). This is the first release of data from the Southern Hemisphere survey of the Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2); new data from APOGEE-2 North are also included. DR16 is also notable as the final data release for the main cosmological program of the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), and all raw and reduced spectra from that project are released here. DR16 also includes all the data from the Time Domain Spectroscopic Survey and new data from the SPectroscopic IDentification of ERosita Survey programs, both of which were co-observed on eBOSS plates. DR16 has no new data from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey (or the MaNGA Stellar Library “MaStar”). We also preview future SDSS-V operations (due to start in 2020), and summarize plans for the final SDSS-IV data release (DR17).
The Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey is currently acquiring integral-field spectroscopy for the largest sample of galaxies to date. By 2020, the MaNGA Survey—which is one of three core programs in the fourth-generation Sloan Digital Sky Survey (SDSS-IV)—will have observed a statistically representative sample of 104 galaxies in the local universe (z ≲ 0.15). In addition to a robust data-reduction pipeline (DRP), MaNGA has developed a data-analysis pipeline (DAP) that provides higher-level data products. To accompany the first public release of its code base and data products, we provide an overview of the MaNGA DAP, including its software design, workflow, measurement procedures and algorithms, performance, and output data model. In conjunction with our companion paper (Belfiore et al.), we also assess the DAP output provided for 4718 observations of 4648 unique galaxies in the recent SDSS Data Release 15 (DR15). These analysis products focus on measurements that are close to the data and require minimal model-based assumptions. Namely, we provide stellar kinematics (velocity and velocity dispersion), emission-line properties (kinematics, fluxes, and equivalent widths), and spectral indices (e.g., D4000 and the Lick indices). We find that the DAP provides robust measurements and errors for the vast majority (>99%) of analyzed spectra. We summarize assessments of the precision and accuracy of our measurements as a function of signal-to-noise. We also provide specific guidance to users regarding the limitations of the data. The MaNGA DAP software is publicly available and we encourage community involvement in its development.
Deformation monitoring is the main program in the area of dam safety. Because statistical model is simple and intuitive, it is widely used in dam safety monitoring. However, in dam's displacement statistic model, there is a high degree of linear relationship between influence factors. Due to the influence of multicollinearity, models calculated with traditional methods are not accurate and stable. Besides, because of dam integrity, each part of dam is interrelated and interactive. Currently, single point or multipoints displacement monitoring models cannot accurately reflect the actual dam running state. In this paper, the theory of panel data is introduced to dam deformation analysis. Panel data contain time series data and cross section data, which is able to solve serious multicollinearity problem of traditional regression method. Moreover, all measuring points are classified into several groups according to their similar deformation law. Based on the random-coefficient model of panel data, potential relationship between different measuring points is built. Take 1 hydropower station, for example, to examine that random-coefficient model is able to improve the modeling situation that estimators are not significant and simultaneously provide a stable model, which explores a new approach for the research of dam displacement monitoring. [4] used statistical regression method to research dam prototype observation data. He set deformation measured value as explained variable and set appropriate explanation variables at the same time. The unbiased efficient estimator of each explanation variable was obtained through the traditional least squares method (ordinary least squares, OLS).After that, influence factors of deformation-measured values were analyzed quantitatively. On the basis of Chen JY's study,
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