PHANGS–ALMA Data Processing and Pipeline

Abstract

We describe the processing of the PHANGS-ALMA survey and present the PHANGS-ALMA pipeline, a public software package that processes calibrated interferometric and total power data into science-ready data products. PHANGS-ALMA is a large, high-resolution survey of CO(2-1) emission from nearby galaxies. The observations combine ALMA's main 12 m array, the 7 m array, and total power observations, and use mosaics of dozens to hundreds of individual pointings. We describe the processing of the u − v data, imaging and deconvolution, linear

PHANGS–ALMA: Arcsecond CO(2–1) Imaging of Nearby Star-forming Galaxies

“…In one early set of observations targeting NGC 0628, also illustrated in Figure 19, we targeted CS(5-4) instead of C 18 O(2-1). The main practical difference is that this places the main CO(2−1) line in the lower sideband instead of the upper sideband, which affects the spectral variation of the noise (see Leroy et al 2021b).…”

“…The C43-2 main configuration recovers emission only from scales of 10″. Thus short-and zero-spacing information is important to accurately reconstruct the intensity distribution (e.g., see the case of M51; Koda et al 2009;Pety et al 2013; and see details for PHANGS-ALMA data processing in Leroy et al 2021b). Therefore, we observed all targets with both components of the Morita Atacama Compact Array (ACA), the compact array of 7 m dishes and the 12 m single-dish telescopes used for "total power.…”

“…By including the CO(1−0) and CO(3−2) lines and relaxing the areal coverage requirements, many more archival observations of molecular line emission in nearby galaxies could be included. The PHANGS-ALMA data processing pipeline (Leroy et al 2021b) can straightforwardly handle these data, and we intend to return to this in future work.…”

“…In Section 5, we describe the ALMA observations. A full description of our data processing pipeline is presented in a companion paper (Leroy et al 2021b), and we summarize our approach in Section 6. In Section 7, we describe the properties of the science-ready data products.…”

Star cluster classification in the PHANGS–HST survey: Comparison between human and machine learning approaches

“…We thus developed a new automated approach to star cluster classifications using neural networks (Wei et al 2020) as part of the Physics at High Angular Resolution in Nearby Galaxies with the Hubble Space Telescope project (PHANGS-HST); PI: J. C. Lee, GO-15654) (J. C. Lee et al, in preparation). 1 PHANGS-HST is a Cycle 26 Treasury program obtaining 5-band UV-optical, F275W (NUV), F336W (U), F438W (B), F555W (V), F814W (I), WFC3 (or ACS in some cases with existing data) imaging for 38 nearby spiral galaxies with previous CO(2-1) observations from the PHANGS-ALMA large program (Leroy et al 2021). 2 An early attempt to classify clusters using quantitative morphological parameters is described in Whitmore et al (2014).…”