The GBT Diffuse Ionized Gas Survey (GDIGS) is a large ongoing survey to map radio recombination line emission from diffuse ionized gas in the Galactic plane.
Diffuse ionized gas known as the “Warm Ionized Medium (WIM)” is a major component of the interstellar medium, making up ∼20% of the total Milky Way gas mass and providing an important source of pressure at the midplane. Given that we have known about it for over 50 years, it is surprising that there remain major unanswered questions regarding the origin, distribution, and characteristics of the WIM.
Most WIM studies have been conducted by observing H-alpha emission. Although H-alpha is very bright compared to other ionized gas tracers, it suffers from extinction. This drastically limits the distance to which the WIM in the inner Galaxy can be studied. Radio observations give us an opportunity to investigate the Galactic mid-plane WIM distribution throughout the Galactic disk.
Due to its large collecting area and its ability to observe a large number of radio recombination line transitions simultaneously, the GBT is an ideal instrument to map emission from the WIM. By using the C-band receiver in combination with the VEGAS backend, we create fully-sampled, high-sensitivity maps of the WIM at a higher spatial and spectral resolution than previous radio recombination line surveys in a reasonable amount of telescope time. Our rms sensitivity in the final datacubes is ∼1 mJy per beam per 5 km/s channel.
The GDIGS survey range spans the inner Galaxy from 32 deg. > l > -5 deg. and |b| < 0.5 deg, as well as some explorations above and below the midplane near high-mass star-forming regions. With these data we will 1) determine the dynamical state and distribution of the WIM, 2) study the ionization state of the WIM, 3) explore the relationship between the WIM and HII regions, and 4) analyze the impact of leaking ionizing radiation from HII regions on dust emission.
We use a combination of the gbtgridder, GBTIDL, and Python to calibrate, grid, and average our data. We will make our data reduction scripts available on github with our final data release.
- Loren Anderson (West Virginia University, PI)
- Matteo Luisi (West Virginia University)
- Bin Liu (Chinese Academy of Sciences)
- Dana Balser (National Radio Astronomy Observatory)
- Tom Bania (Boston University)
- Trey Wenger (Dominion Radio Astrophysical Observatory)
- Matt Haffner (Embry-Riddle Aeronautical University)