This study used spatially-resolved spectroscopy from the MaNGA survey to develop a computational method to identify extraplanar diffuse ionized gas (eDIG) in 550 nearby galaxies. The method used two indices based on emission line ratios and radii to select galaxies exhibiting eDIG signatures. 8% of galaxies showed eDIG, particularly those with active star formation and inclinations over 45 degrees. Ongoing analysis of eDIG morphology, incidence, and kinematics will provide insights into gas accretion and feedback processes governing galaxy evolution.

1. RESEARCH POSTER PRESENTATION DESIGN © 2015
www.PosterPresentations.com
The efficiency with which galaxies convert gas
into stars is driven by the continuous cycle of
accretion and feedback processes within the
circumgalactic medium. Extraplanar diffuse
ionized gas (eDIG) can provide insights into
the tumultuous processes that govern the
evolution of galactic disks because eDIG
emission traces both inflowing and outflowing
gas. With the help of state-of-the-art,
spatially-resolved spectroscopy from MaNGA
(Mapping Nearby Galaxies at Apache Point
Observatory), we developed a computational
method to identify eDIG based on the strength
of and spatial extent of optical emission lines
for a diverse sample of 550 nearby
galaxies. This sample includes roughly half of
the MaNGA galaxies that will become publicly
available in summer 2016 as part of the
Thirteenth Data Release of the Sloan Digital
Sky Survey.
Introduction
Science Questions
1. First, we examined a subset of 119 galaxies
– Individually reviewed each galaxy’s spectral
maps by eye
– Categorized galaxies qualitatively based on
degree of eDIG
2. Next, we wrote an algorithm to automatically identify
galaxies with evidence of eDIG
– The algorithm measured the [deprojected] radius
of each pixel and its various emission line ratios
– The algorithm was applied to the entire sample of
550 galaxies
3. The algorithm uses two indices which we defined as:
The “Ryan index” and “Hubbard index”
– The Ryan index represents the amount of pixels
each galaxy has with radius > 5 kpc and
log([SII]/Hα) > -0.5
– The Hubbard index represents the product of
pixels’ radius, [SII]/Hα and [NII]/Hα
4. Finally we analyzed the global characteristics of the
galaxies that were identified by the algorithm and verified
by visual inspection
MaNGA
(Mapping Nearby Galaxies at Apache Point Observatory)
• We examined spectra from a sample of 550 MaNGA
galaxies
• Our objective was to develop a code to automatically
select galaxies from the sample exhibiting eDIG layers
Spectral Maps
Conclusion
Selecting Galaxies with eDIG
 We identified signatures of eDIG in 8% of the galaxies
in this sample
 these signatures are particularly common among
galaxies with active star formation and inclination
angles >45 degrees.
 Our ongoing analysis of the morphology, incidence,
and kinematics of eDIG has important implications for
current models of accretion and feedback processes that
regulate star formation in galaxies.
Acknowledgements
We acknowledge support from the Astrophysics
REU program at the University of Wisconsin-
Madison, the National Astronomy Consortium, and
The Grainger Foundation.
Are there clear correlations between certain
global properties of galaxies (i.e. star-
formation rate) and the incidence of eDIG?
How does the cycle of accretion and
feedback affect the morphology of eDIG?
Howard University, University of Wisconsin-Madison
Ryan J. Hubbard, Aleksandar M. Diamond-Stanic, MaNGA collaboration
Identifying Extraplanar Diffuse Ionized Gas (eDIG) in a Sample of MaNGA Galaxies
• Will eventually survey approximately 10,000 galaxies
• so far MaNGA has surveyed over 1500 galaxies
• MaNGA uses integral field spectroscopy in order to
create a “spectral mosaic” of each galaxy.
• From MaNGA we obtain spatially resolved spectra at
R~2000 over a range of 3600 – 10300 A
Sample Number
of
Galaxies
Galaxies
with
eDIG
% eDIG
Training
Set
119 14 ~12%
Initial
Sample
550 45 ~8%
MaNGA
(predictions)
10,000 ~1,000 ~10%