DAYENU: A Simple Filter of Smooth Foregrounds for Intensity Mapping Power Spectra
We introduce DAYENU, a linear, spectral filter for HI intensity mapping that achieves the desirable foreground mitigation and error minimization properties of inverse co-variance weighting with minimal modeling of the underlying data. Beyond 21 cm power-spectrum estimation, our filter is suitable for any analysis where high dynamic-range removal of spectrally smooth foregrounds in irregularly (or regularly) sampled data is required, something required by many other intensity mapping techniques. We show that DAYENU enables the access of large-scale line-of-sight modes that are inaccessible to tapered DFT estimators. Since these modes have the largest SNRs, DAYENU significantly increases the sensitivity of 21 cm analyses over tapered Fourier transforms. Slight modifications allow us to use DAYENU as a linear replacement for iterative delay CLEANing (DAYENUREST). An interactive jupyter tutorial on using DAYENU can be found at https://github.com/HERA-Team/uvtools/blob/master/examples/linear_clean_demo.ipynb DAYENU's source code can be found at https://github.com/HERA-Team/uvtools/blob/master/uvtools/dspec.py
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