Interferometric data modelling: issues in realistic data generation

Details Interferometric data modelling: issues in realistic data generation Interferometric data modelling: issues in realistic data generation

Oct 2004

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2004cqgra..21s1783m&link_type=abstract

Classical and Quantum Gravity, Volume 21, Issue 20, pp. S1783-S1792 (2004).

Computer Science

2

Scientific paper

This study describes algorithms developed for modelling interferometric noise in a realistic manner, i.e. incorporating non-stationarity that can be seen in the data from the present generation of interferometers. The noise model is based on individual component models (ICM) with the application of auto regressive moving average (ARMA) models. The data obtained from the model are vindicated by standard statistical tests, e.g. the KS test and Akaike minimum criterion. The results indicate a very good fit. The advantage of using ARMA for ICMs is that the model parameters can be controlled and hence injection and efficiency studies can be conducted in a more controlled environment. This realistic non-stationary noise generator is intended to be integrated within the data monitoring tool framework.

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