Detecting weak spectral lines in interferometric data through matched filtering
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Modern radio interferometers enable observations of spectral lines with unprecedented spatial resolution and sensitivity. In spite of these technical advances, many lines of interest are still at best weakly detected and therefore necessitate detection and analysis techniques specialized for the low signal-to-noise ratio (S/N) regime. Matched filters can leverage knowledge of the source structure and kinematics to increase sensitivity of spectral line observations. Application of the filter in the native Fourier domain improves S/N while simultaneously avoiding the computational cost and ambiguities associated with imaging, making matched filtering a fast and robust method for weak spectral line detection. We demonstrate how an approximate matched filter can be constructed from a previously observed line or from a model of the source, and we show how this filter can be used to robustly infer a detection significance for weak spectral lines. When applied to ALMA Cycle 2 observations of CH3OH in the protoplanetary disk around TW Hya, the technique yields a ≈53% S/N boost over aperture-based spectral extraction methods, and we show that an even higher boost will be achieved for observations at higher spatial resolution. A Python-based open-source implementation of this technique is available under the MIT license at http://github.com/AstroChem/VISIBLE.
Loomis , R A , Öberg , K I , Andrews , S M , Walsh , C , Czekala , I , Huang , J & Rosenfeld , K A 2018 , ' Detecting weak spectral lines in interferometric data through matched filtering ' , Astronomical Journal , vol. 155 , no. 4 , 182 . https://doi.org/10.3847/1538-3881/aab604
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DescriptionFunding: R.A.L. and J.H. gratefully acknowledge funding from National Science Foundation Graduate Research Fellowships (Grant No. DGE-1144152). R.A.L. also acknowledges funding from the NRAO Student Observing Support Program. K.I.Ö. acknowledges funding from the Alfred P. Sloan Foundation and the David and Lucile Packard Foundation. C.W. acknowledges financial support from the Netherlands Organisation for Scientific Research (NWO, grant 639.041.335) and start-up funds from the University of Leeds, UK.
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