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DOE OSTI2020

APOGEE Data and Spectral Analysis from SDSS Data Release 16: Seven Years of Observations Including First Results from APOGEE-South

The spectral analysis and data products in Data Release 16 (DR16; 2019 December) from the high-resolution near-infrared Apache Point Observatory Galactic Evolution Experiment (APOGEE)-2/Sloan Digital Sky Survey (SDSS)-IV survey are described. Compared to the previous APOGEE data release (DR14; 2017 July), APOGEE DR16 includes about 200,000 new stellar spectra, of which 100,000 are from a new southern APOGEE instrument mounted on the 2.5 m du Pont telescope at Las Campanas Observatory in Chile. DR16 includes all data taken up to 2018 August, including data released in previous data releases. All of the data have been re-reduced and re-analyzed using the latest pipelines, resulting in a total of 473,307 spectra of 437,445 stars. Changes to the analysis methods for this release include, but are not limited to, the use of MARCS model atmospheres for calculation of the entire main grid of synthetic spectra used in the analysis, a new method for filling “holes” in the grids due to unconverged model atmospheres, and a new scheme for continuum normalization. Abundances of the neutron-capture element Ce are included for the first time. A new scheme for estimating uncertainties of the derived quantities using stars with multiple observations has been applied, and calibrated values of surface gravities for dwarf stars are now supplied. Compared to DR14, the radial velocities derived for this release more closely match those in the Gaia DR2 database, and a clear improvement in the spectral analysis of the coolest giants can be seen. The reduced spectra as well as the result of the analysis can be downloaded using links provided on the SDSS DR16 web page.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1993

Investigation of spectral analysis techniques for randomly sampled velocimetry data

It is well known that velocimetry (LV) generates individual realization velocity data that are randomly or unevenly sampled in time. Spectral analysis of such data to obtain the turbulence spectra, and hence turbulence scales information, requires special techniques. The 'slotting' technique of Mayo et al, also described by Roberts and Ajmani, and the 'Direct Transform' method of Gaster and Roberts are well known in the LV community. The slotting technique is faster than the direct transform method in computation. There are practical limitations, however, as to how a high frequency and accurate estimate can be made for a given mean sampling rate. These high frequency estimates are important in obtaining the microscale information of turbulence structure. It was found from previous studies that reliable spectral estimates can be made up to about the mean sampling frequency (mean data rate) or less. If the data were evenly samples, the frequency range would be half the sampling frequency (i.e. up to Nyquist frequency); otherwise, aliasing problem would occur. The mean data rate and the sample size (total number of points) basically limit the frequency range. Also, there are large variabilities or errors associated with the high frequency estimates from randomly sampled signals. Roberts and Ajmani proposed certain pre-filtering techniques to reduce these variabilities, but at the cost of low frequency estimates. The prefiltering acts as a high-pass filter. Further, Shapiro and Silverman showed theoretically that, for Poisson sampled signals, it is possible to obtain alias-free spectral estimates far beyond the mean sampling frequency. But the question is, how far? During his tenure under 1993 NASA-ASEE Summer Faculty Fellowship Program, the author investigated from his studies on the spectral analysis techniques for randomly sampled signals that the spectral estimates can be enhanced or improved up to about 4-5 times the mean sampling frequency by using a suitable prefiltering technique. But, this increased bandwidth comes at the cost of the lower frequency estimates. The studies further showed that large data sets of the order of 100,000 points, or more, high data rates, and Poisson sampling are very crucial for obtaining reliable spectral estimates from randomly sampled data, such as LV data. Some of the results of the current study are presented.

Sree, Dave↗
NASA NTRS1972

Spectral analysis program. Volume 1: User's guide

The spectral analysis program (SAP) was developed to provide the Manned Spacecraft Center with the capability of computing the power spectrum of a phase or frequency modulated high frequency carrier wave. Previous power spectrum computational techniques were restricted to relatively simple modulating signals because of excessive computational time, even on a high speed digital computer. The present technique uses the recently developed extended fast Fourier transform and represents a generalized approach for simple and complex modulating signals. The present technique is especially convenient for implementation of a variety of low-pass filters for the modulating signal and bandpass filters for the modulated signal.

Hayden, W. L.↗
NASA NTRS1973

Correlation and spectral analysis of daily solar radio flux.

Correlation and spectral analysis of solar radio flux density and sunspot number near the maximum of the sunspot cycle has indicated the existence of several effects. These include (1) long period amplitude modulation of the slowly varying component (SVC) of radio emission, (2) coronal storage over a period of the order of three solar rotations, (3) fast decay (one solar rotation period or less) of gyromagnetic emissions from radio sources, and (4) shift in location of chromospheric sources compared to those of either the upper corona or the photosphere.

El-Raey, M.↗
NASA NTRS1991

Linear and circular digital spectral analysis of serial data

Two methods of digital spectral analysis of unevenly sampled data are developed and illustrated here. One method uses a linear function of time (or space), the other uses circular functions. The circular method turns out to be essentially equivalent to a least-squares sine-wave analysis. The linear, anharmonic method uses only the field of real numbers and elementary algebraic operations, and hence it can be made computationally very fast and accurate. Both methods are very general, properly handling all kinds of time series ranging from simple series consisting only of the times of events to complicated series consisting of pulses with long duty cycles. The two methods are here applied to the analysis of annual mean relative sunspot numbers.

Stothers, Richard B.↗
NASA NTRS1978

A fluctuation-induced plasma transport diagnostic based upon fast-Fourier transform spectral analysis

A diagnostic, based on fast Fourier-transform spectral analysis techniques, that provides experimental insight into the relationship between the experimentally observable spectral characteristics of the fluctuations and the fluctuation-induced plasma transport is described. The model upon which the diagnostic technique is based and its experimental implementation is discussed. Some characteristic results obtained during the course of an experimental study of fluctuation-induced transport in the electric field dominated NASA Lewis bumpy torus plasma are presented.

Powers, E. J.↗
NASA NTRS1981

Spectral analysis and diagnosis of nonlinear interactions of large-scale moving waves at 200 mb in the GLAS General Circulation Model

The space-time Fourier spectral analysis and the equations of kinetic energy and momentum transport in the wavenumber-frequency domain proposed by Kao (1968) and modified by Kao and Lee (1977) using Tukey's (1967) numerical spectral analysis are employed to analyze the 200 mb wind fields of a four-month climate experiment of the GLAS atmospheric circulation model. This climate run covers the period from Jan. 1 to Apr. 30, 1975. The current study analyzes a 90-day (Jan. 15-Apr. 14, 1975) wind field of this climate run. A comparison is conducted between the current study and observations reported by Kao and Lee. It is found that the 200 mb latitudinal distributions of model mean zonal wind, zonal and meridional kinetic energy and momentum transport of eddies agree fairly well with the observations.

Chen, T.-C.↗
NASA NTRS1990

Dynamic Power Spectral Analysis of Solar Measurements from Photospheric, Chromospheric, and Coronal Sources

An important aspect in the power spectral analysis of solar variability is the quasistationary and quasiperiodic nature of solar periodicities. In other words, the frequency, phase, and amplitude of solar periodicities vary on time scales ranging from active region lifetimes to solar cycle time scales. Here, researchers employ a dynamic, or running, power spectral density analysis to determine many periodicities and their time-varying nature in the projected area of active sunspot groups (S sub act). The Solar Maximum Mission/Active Cavity Radiometer Irradiance Monitor (SMM/ACRIM) total solar irradiance (S), the Nimbus-7 MgII center-to-wing ratio (R (MgII sub c/w)), the Ottawa 10.7 cm flux (F sub 10.7), and the GOES background x ray flux (X sub b) for the maximum, descending, and minimum portions of solar cycle 21 (i.e., 1980 to 1986) are used. The technique dramatically illustrates several previously unrecognized periodicities. For example, a relatively stable period at about 51 days has been found in those indices which are related to emerging magnetic fields. The majority of solar periodicities, particularly around 27, 150 and 300 days, are quasiperiodic because they vary in amplitude and frequency throughout the solar cycle. Finally, it is shown that there are clear differences between the power spectral densities of solar measurements from photospheric, chromospheric, and coronal sources.

Bouwer, S. D.↗
NASA NTRS1989

Autoregressive modeling for the spectral analysis of oceanographic data

Over the last decade there has been a dramatic increase in the number and volume of data sets useful for oceanographic studies. Many of these data sets consist of long temporal or spatial series derived from satellites and large-scale oceanographic experiments. These data sets are, however, often 'gappy' in space, irregular in time, and always of finite length. The conventional Fourier transform (FT) approach to the spectral analysis is thus often inapplicable, or where applicable, it provides questionable results. Here, through comparative analysis with the FT for different oceanographic data sets, the possibilities offered by autoregressive (AR) modeling to perform spectral analysis of gappy, finite-length series, are discussed. The applications demonstrate that as the length of the time series becomes shorter, the resolving power of the AR approach as compared with that of the FT improves. For the longest data sets examined here, 98 points, the AR method performed only slightly better than the FT, but for the very short ones, 17 points, the AR method showed a dramatic improvement over the FT. The application of the AR method to a gappy time series, although a secondary concern of this manuscript, further underlines the value of this approach.

Gangopadhyay, Avijit↗