High‐definition temporal/frequency analysis of sampled data sets

1997 ◽  
Vol 102 (5) ◽  
pp. 3145-3145
Author(s):  
George J. Frye
Keyword(s):  
Frequency Analysis ◽  
Temporal Frequency ◽  
Data Sets ◽  
Sampled Data ◽  
High Definition

scholarly journals Variability in the Power Spectrum of Solar Five-Minute Oscillations

1983 ◽  
Vol 66 ◽  
pp. 411-425
Author(s):  
Frank Hill ◽  
Juri Toomre ◽  
Laurence J. November
Keyword(s):  
Large Scale ◽  
Solar Rotation ◽  
Temporal Frequency ◽  
Power Spectra ◽  
Giant Cells ◽  
Spectral Lines ◽  
Data Sets ◽  
Data Set ◽  
Temporal Sampling ◽  
Velocity Changes

AbstractTwo-dimensional power spectra of solar five-minute oscillations display prominent ridge structures in (k, ω) space, where k is the horizontal wavenumber and ω is the temporal frequency. The positions of these ridges in k and ω can be used to probe temperature and velocity structures in the subphotosphere. We have been carrying out a continuing program of observations of five-minute oscillations with the diode array instrument on the vacuum tower telescope at Sacramento Peak Observatory (SPO). We have sought to establish whether power spectra taken on separate days show shifts in ridge locations; these may arise from different velocity and temperature patterns having been brought into our sampling region by solar rotation. Power spectra have been obtained for six days of observations of Doppler velocities using the Mg I λ5173 and Fe I λ5434 spectral lines. Each data set covers 8 to 11 hr in time and samples a region 256″ × 1024″ in spatial extent, with a spatial resolution of 2″ and temporal sampling of 65 s. We have detected shifts in ridge locations between certain data sets which are statistically significant. The character of these displacements when analyzed in terms of eastward and westward propagating waves implies that changes have occurred in both temperature and horizontal velocity fields underlying our observing window. We estimate the magnitude of the velocity changes to be on the order of 100 m s -1; we may be detecting the effects of large-scale convection akin to giant cells.

scholarly journals Radial-velocity jitter of stars as a function of observational timescale and stellar age

2019 ◽  
Vol 632 ◽  
pp. A37 ◽  
Author(s):  
Stefan S. Brems ◽  
Martin Kürster ◽  
Trifon Trifonov ◽  
Sabine Reffert ◽  
Andreas Quirrenbach
Keyword(s):  
Radial Velocity ◽  
Young Stars ◽  
Data Sets ◽  
Activity Levels ◽  
Sampled Data ◽  
Planet Detection ◽  
Dwarf Stars ◽  
Stellar Ages ◽  
Irregularly Sampled Data ◽  
Instrumental Precision

Context. Stars show various amounts of radial-velocity (RV) jitter due to varying stellar activity levels. The typical amount of RV jitter as a function of stellar age and observational timescale has not yet been systematically quantified, although it is often larger than the instrumental precision of modern high-resolution spectrographs used for Doppler planet detection and characterization. Aims. We aim to empirically determine the intrinsic stellar RV variation for mostly G and K dwarf stars on different timescales and for different stellar ages independently of stellar models. We also focus on young stars (≲30 Myr), where the RV variation is known to be large. Methods. We use archival FEROS and HARPS RV data of stars which were observed at least 30 times spread over at least two years. We then apply the pooled variance (PV) technique to these data sets to identify the periods and amplitudes of underlying, quasiperiodic signals. We show that the PV is a powerful tool to identify quasiperiodic signals in highly irregularly sampled data sets. Results. We derive activity-lag functions for 20 putative single stars, where lag is the timescale on which the stellar jitter is measured. Since the ages of all stars are known, we also use this to formulate an activity–age–lag relation which can be used to predict the expected RV jitter of a star given its age and the timescale to be probed. The maximum RV jitter on timescales of decades decreases from over 500 m s−1 for 5 Myr-old stars to 2.3 m s−1 for stars with ages of around 5 Gyr. The decrease in RV jitter when considering a timescale of only 1 d instead of 1 yr is smaller by roughly a factor of 4 for stars with an age of about 5 Myr, and a factor of 1.5 for stars with an age of 5 Gyr. The rate at which the RV jitter increases with lag strongly depends on stellar age and reaches 99% of the maximum RV jitter over a timescale of a few days for stars that are a few million years old, up to presumably decades or longer for stars with an age of a few gigayears.

scholarly journals The Impact of Non-uniform Sampling on Stratospheric Ozone Trends Derived from Occultation Instruments

2017 ◽  
Author(s):  
Robert P. Damadeo ◽  
Joseph M. Zawodny ◽  
Ellis E. Remsberg ◽  
Kaley A. Walker
Keyword(s):  
Stratospheric Ozone ◽  
Primary Source ◽  
Data Sets ◽  
Sampled Data ◽  
Uniform Sampling ◽  
Ozone Trends ◽  
Long Term Trends ◽  
Non Uniform Sampling ◽  
The Impact

Abstract. This paper applies a recently developed technique for deriving long-term trends in ozone from sparsely sampled data sets to multiple occultation instruments simultaneously without the need for homogenization. The technique can compensate for the non-uniform temporal, spatial, and diurnal sampling of the different instruments and can also be used to account for biases and drifts between instruments. These problems have been noted in recent international assessments as being a primary source of uncertainty that clouds the significance of derived trends. Results show potential recovery trends of ~ 2–3 %/decade in the upper stratosphere at mid-latitudes, which are similar to other studies, and also how sampling biases present in these data sets can create differences in derived "recovery" trends of up to ~ 1 %/decade if not properly accounted for. Limitations inherent to all techniques (e.g., relative instrument drifts) and their impacts (e.g., trend differences up to ~ 2 %/decade) are also described and a potential path forward towards resolution is presented.

scholarly journals Dispersion analysis of PoTRA ranked mRNA mediated dysregulated pathways in Breast Invasive Cancer from a TCGA Pan-Cancer study

2018 ◽  
Author(s):  
Margaret K Linan ◽  
Valentin Dinu
Keyword(s):  
Gene Expression ◽  
Sample Size ◽  
Gene Expression Data ◽  
Data Sets ◽  
Minimum Sample Size ◽  
Expression Data ◽  
Sampled Data ◽  
Average Rank ◽  
Minimum Sample ◽  
Google Search

Background. Our publication of the new pathways of topological rank analysis (PoTRA) algorithm demonstrated a novel approach for using the Google Search PageRank algorithm to analyze gene expression networks to identify biological pathways significantly disrupted in hepatocellular carcinoma. In order to apply the PoTRA algorithm to analyze other cancer gene expression data sets, of various sizes and normal:tumor ratio composition, two important questions must be answered: 1. What is the optimal normal:tumor sample ratio?; and 2. What is the minimum number of samples that should be used for PoTRA analysis? To address these questions, the average standard deviation (SD) in PoTRA-ranked mRNA mediated dysregulated pathways was studied using randomly sampled data sets with various normal:tumor ratios and sizes drawn from the TCGA Breast Invasive Carcinoma (TCGA-BRCA) project. Methods. To identify the optimal normal:tumor sample ratios, the SD analysis used random combinations of 1:N unbalanced normal:tumor data sets: (1:1, 1:2, 1:3, 1:5, 1:7, 1:9). To identify the minimum sample size, random resampling of normal and tumor samples of various sizes are used: (3 vs 3), (5 vs 5), (10 vs 10), (25 vs 25), (50 vs 50), (75 vs 75), (100 vs 100), and (113 vs 113). Results. This analysis suggests that the 1:1 ratio achieves the lowest average rank variation and that the minimum sample size of 50 normal and 50 tumor samples reaches a steady state in the average rank variation. Conclusion. In conclusion, future applications of the PoTRA algorithm to analyze gene expression data sets such as TCGA should use balanced data sets as well as a minimum sample size of 50 for both normal and tumor to ensure the most robust performance.

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