Detecting weak spectral lines in interferometric data through matched filtering

Ryan A. Loomis*, Karin I. Öberg, Sean M. Andrews, Catherine Walsh, Ian Czekala, Jane Huang, Katherine A. Rosenfeld

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

55 Citations (Scopus)
1 Downloads (Pure)


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
Original languageEnglish
Article number182
Number of pages14
JournalAstronomical Journal
Issue number4
Publication statusPublished - 5 Apr 2018


  • Methods: data analysis
  • Protoplanetary disks
  • Radio lines: general
  • Submillimeter: planetary systems
  • Techniques: interferometric
  • Techniques: spectroscopic


Dive into the research topics of 'Detecting weak spectral lines in interferometric data through matched filtering'. Together they form a unique fingerprint.

Cite this