1 / 8

Data conditioning for first analyses

This document outlines the goals and planned functionalities of data conditioning processes at the Penn State LSC. It identifies key needs, available resources, and coordinates activities essential for effective data analysis. The focus is on current and future functionalities including various data conditioning actions such as DFT, filtering, and statistical characterization. The agenda covers vital topics like data volume, channel specifications, and precision estimation necessary for optimal performance in data analysis for gravitational wave research.

oberon
Télécharger la présentation

Data conditioning for first analyses

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Data conditioning for first analyses Lee Samuel Finn Penn State Penn State LSC

  2. Goals • Identify needs • Identify resources • Coordinate activities Penn State LSC

  3. Agenda • Review • Current functionality • Planned functionality • Needs • Bursts • Inspiral • Periodic • Stochastic • Required resources & coordination Penn State LSC

  4. Conditioning (I)DFT Forward, reverse DFT Linear filtering Decimation Heterodyning Digital lock-in Characterization Descriptive statistics Max, min, mean, variance, skew, kurtosis of sequence Power spectrum estimation X-spectral density, coherence Current functionality Penn State LSC

  5. Conditioning Auto, cross-correlations Line removal Power-mains Violin modes Broad-band regression Time domain Frequency domain Characterization System ID (AR/ARX) Bispectrum/bicoherence Stationarity tests Broad, narrow band Histogram accumulation Parametric PSD estimation Planned functionality Penn State LSC

  6. Needs • Data conditioning actions • Drop-out fill-in? • Data volume • Number channels, duration, sample rates • Number simultaneous pipeline (per search) • Precision • E.g., of power spectral density estimate Penn State LSC

  7. Resources & Coordination • datacondAPI highly integrated into LDAS • Disconnected development won’t work • datacondAPI Team • Not in UL group or UL group unknown: Charlton, Ehrens, Maros, Ottewill, Searle • Burst • W. Anderson, Blackburn, Finn, Katsavounidis, Klimenko • Inspiral • Periodic • Stochastic • Finn, Landry, Lazzarini, Romano Penn State LSC

  8. Actions • Identify needs • Data conditioning actions • Regressions, correlations, power spectrum estimations, etc. • Data volumes • Number channels, duration, sample rates • Precision estimation • Identify resources • Who, from the different groups, will contribute to the development effort? • Need this information ASAP • First cut: 1 April • Final iteration:1 May Penn State LSC

More Related