Scientific Newsletter  March 5, 2002 

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Persistency Checking of Gaussians Steve Fulton Thus far, approximately 20 million distinct Gaussians have passed data integrity testing. For these signals we standardized the score such that there were on average an equal number of Gaussians in each workunit group with scores greater than or equal to 1.0. (See "An Explanation of Score Correction" below for details.) This standardization left 1.25 million Gaussians of interest to be examined further. From these signals we then determine which Gaussians have been detected in the same location of the sky on multiple occasions. This process is called persistency checking. For our first pass through the data, we performed an analysis with very restrictive bounds on matches. Gaussians were considered matches if they conformed to the following criteria:
1,397 multiplets (multiplydetected Gaussians) meet the above criteria. Future analyses will identify the best candidates from this group. An Explanation of Score Correction The SETI@home feed at Arecibo moves at varying rates as the opposing feed tracks objects in the sky. At faster rates, the SETI@home client obtains fewer points over a fixed angular separation. With fewer data points false detections are more likely, and hence the client detects more Gaussians and higher scoring Gaussians as the rate of telescopic movement increases. (See Figure 1 on the right, where (uncorrected) Gaussian score is on the yaxis and the width of the Gaussian (sigma) is on the xaxis  click on it to enlarge.) This slewrate detection dependence creates problems when looking for persistent signals. Persistency detection assumes that there is an equal chance of detecting a Gaussian each time the telescope passes a given point in the sky; the analysis uses this assumption to rank persistent multiplets and reject temporal rfi. To fix the problem, we corrected the factors used to calculate score such that the number of signals in a workunit group with significant scores is constant across slew rate. Statistically, the score of a Gaussian is defined as the peakpower/chisquare. The chisquare is a measure of the Gaussian's fit (i.e., how well the signal matches a classic Gaussian curve, with a lower score representing a better fit). Since both the peakpower and chisquare terms reported back by the client were dependent on the telescope slew rate, a simple correction function was needed for each of the terms. The resulting "flattened" peak power and chisquare were used to define the new (corrected) score (Figure 2 on the left  click on it to enlarge). (Note that the Yaxis in Figure 2 is mislabelledinstead of "Score uncorrected", it should read "Score corrected".) The normalization was set so that on average each workunit group would have 4 Gaussians with a score 1.0 or greater. This score approximates a chi_square cutoff of 8.8 (uncorrected) at low slew rates, which is the threshold used by the client. 
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