Details

Autor(en) / Beteiligte

• Kepley, Amanda A.
• Tsutsumi, Takahiro
• Brogan, Crystal L.
• Indebetouw, Remy
• Yoon, Ilsang
• Mason, Brian
• Meyer, Jennifer Donovan

Titel

AUTO-MULTITHRESH: A General Purpose Automasking Algorithm

Ist Teil von

• Publications of the Astronomical Society of the Pacific, 2020-02, Vol.132 (1008), p.1-21

Ort / Verlag

IOP Publishing Limited

Erscheinungsjahr

2020

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Producing images from interferometer data requires accurate modeling of the sources in the field of view, which is typically done using the CLEAN algorithm. Given the large number of degrees of freedom in interferometeric images, one constrains the possible model solutions for CLEAN by masking regions that contain emission. Traditionally this process has largely been done by hand. This approach is not possible with today’s large data volumes which require automated imaging pipelines. This paper describes an automated masking algorithm that operates within CLEAN called AUTO-MULTITHRESH. This algorithm was developed and validated using a set of ∼1000 Atacama Large Millimeter/submillimeter Array (ALMA) images chosen to span a range of intrinsic morphology and data characteristics. It takes a top-down approach to producing masks: it uses the residual images to identify significant peaks and then expands the mask to include emission associated with these peaks down to lower signal-to-noise noise. The AUTO-MULTITHRESH algorithm has been implemented in CASA and has been used in production as part of the ALMA Imaging Pipeline starting with Cycle 5. It has been shown to be able to mask a wide range of emission ranging from simple point sources to complex extended emission with minimal tuning of the parameters based on the point-spread function of the data. Although the algorithm was developed for ALMA, it is general enough to have been used successfully with data from other interferometers with appropriate parameter tuning. Integrating the algorithm more deeply within the minor cycle could lead to future performance improvements.