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The Gaussian phase noise of intensity time series is demonstrated to bedrastically reduced when the raw voltage data are digitally filtered through anarbitrarily large number $n$ of orthornormal bandpass profiles (eigen-filters)sharing the same intensity bandwidth, and the resulting intensity series areco-added. Specifically, the relative noise variance of the summed series at theresolution of one coherence time or less, goes down with increasing $n$ as$1/n$, although (consistent with the radiometer equation) the advantagegradually disappears when the series is bin averaged to lower resolution. Thusthe algorithm is designed to enhance the sensitivity of detecting transientsthat are smoothed out by time averaging and too faint to be visible in thenoisy unaveraged time series, as demonstrated by the simulation of a weakembedded time varying signal of either a periodic nature or a fast andunrepeated pulse. The algorithm is then applied to a 10 minute observation ofthe pulsar PSR 1937+21 by the VLA, where the theoretical predictions wereverified by the data. Moreover, it is shown that microstructures within thetime profile are better defined as the number $n$ of filters used increases,and a periodic signal of period $1.86 \times 10^{-5}$~s ($53.9$~kHz) isdiscovered in the pulse profile. Lastly, we apply the algorithm to the firstbinary black hole merger detected by LIGO, GW150914. We find the SNR of themean peak intensity increases as $\sqrt{n}$ and cross correlation of the eventbetween the LIGO-Hanford-Livingston detector pair increases with filter order$n$.

A method of enhancing the detection sensitivity of transient sources in time series with Gaussian stationary noise