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Detector/Data Characterization Robot Towards Data Mining

This paper by Soumya D. Mohanty explores the complementary roles of data mining and data exploration within the context of gravitational wave research. It emphasizes the need for efficient handling of time series data from selected triggers, leveraging analytical methodologies to classify transients and examine their occurrence over time. The discussion includes strategies for controlling false alarm rates and highlights the challenges of raw data re-analysis. With a focus on statistical analysis and clean algorithms, this work contributes to enhancing the accuracy of potential detections in noisy astrophysical datasets.

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Detector/Data Characterization Robot Towards Data Mining

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  1. Detector/Data Characterization RobotTowards Data Mining Soumya D. Mohanty Max Planck Institut für Gravitationsphysik LIGO-G020036-00-Z Soumya D. Mohanty, AEI

  2. Using a database: Data Mining & Data Exploration • Different but complementary approaches. • Data exploration: • I want to see the time series corresponding to a bunch of triggers that I selected from a database. (Then do more analysis on this selected data.) • Typically, Follow up data is short, Quick look environment needed, no specific queries • Data Mining: • Can the transients seen over a month be classified into groups? What was the rate of transients in each group as a function of time (Maybe some types occur in the day, some occur in the night). (Then use this information to quantify the quality of long data stretches). • Purely database based; Re-analysis of raw data may be impractical Soumya D. Mohanty, AEI

  3. DCR: Control the false alarm rate DATABASE Raw Data to Database • Any such transformation will introduce errors • Spurious information • Missing genuine stuff Information Transformer Raw noisy data Soumya D. Mohanty, AEI

  4. Control on False Alarm Rate • Important for Data mining • Statistical analysis done on database itself since reanalysis of long stretch of data expensive • Need to put error bars • Not so important for Data exploration • Looking for information about specific events • Each explorer will work with his/her own short data stretch Soumya D. Mohanty, AEI

  5. Soumya Mohanty, Soma Mukherjee, CQG, 2002. Restricted DCR (rDCR) Initial Design of DCR Soumya D. Mohanty, AEI

  6. Implementation • C++ code for restricted DCR algorithm ready • It will run in GEO using the GEO++ library • Data mining issues under active investigation • Earlier talks on Automated classification, non-stationarity, line removal transient test • Data mining will be done using Matlab’s database toolbox http://www.aei.mpg.de/~mohanty/DCR/DCRindex.html Soumya D. Mohanty, AEI

  7. Must Use statistically clean algorithms Future View data as a single multivariate time series DCR Detect change points Database All channels Transform the multivariate data Example: construct cross-correlation of two channels Design Data Mining Soumya D. Mohanty, AEI

  8. DCR on the Web Soumya D. Mohanty, AEI

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