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Investigating the Star
Formation History of Local
Interacting Galaxies
Kiyoaki Christopher Omori,
Tsutomu T. Takeuchi
Nagoya University
Laboratory of Galaxy Evolution
June 6, 2019
Kiyoaki Christopher Omori (Nagoya Univ.),
Galaxy Evolution Workshop 2019

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Introduction/Motivation• Galaxies that are
starbursting on theright diagram can bemergers• Is the high starformation rate actuallycaused by interaction?

Star formation rate [𝑀⊙ /yr]

• Galaxy interactions cancause starbursts andstar formation

Rodighiero et al. (2011)

Kiyoaki Christopher Omori (Nagoya Univ.),
Galaxy Evolution Workshop 2019

Stellar mass [𝑀⊙ ]2/17

Introduction/Motivation• Galaxies that are
starbursting on theright diagram can bemergers• Is the high starformation rate actuallycaused by interaction?

Star formation rate [𝑀⊙ /yr]

• Galaxy interactions cancause starbursts andstar formation

Rodighiero et al. (2011)

It isnecessary
Stellarmass [𝑀to⊙ ]study
Kiyoaki Christopher Omori (Nagoya Univ.),SFH!
Galaxy Evolution Workshop 2019

MaNGA – SpatiallyResolved Properties

3/17• 2-D maps ofvarious physical properties

Spaxel• Maps detailed composition
and kinematic structure of
nearby galaxies

Km/s• Mapping Nearby Galaxies
at APO (MaNGA)
Catalogue

SpaxelImage and Stellar Velocity Mapsof MaNGA 7443-12703

Kiyoaki Christopher Omori (Nagoya Univ.),
Galaxy Evolution Workshop 2019

MaNGA FIREFLY VAC – SED
Fitting

4/17• MaNGA FIREFLY VAC(Goddard et al. 2017)• Conducts spectral energy
distribution (SED) fitting on
spatially binned spectrausing the FIREFLY(Wilkinson et al. 2017)code• Provides measurements ofspatially resolved stellar
population properties suchas age and metallicity
Kiyoaki Christopher Omori (Nagoya Univ.),
Galaxy Evolution Workshop 2019

Goddard et al. (2017)

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Identification of Interacting Galaxies• We have identified interacting galaxies using
space.ml :: Transfer Learning – Using transfer
learning to detect galaxy mergers (Ackermann et al.
2018)• 2 step method to identify galaxies• Transfer Learning• Convolutional Neural Networks (CNN)
Kiyoaki Christopher Omori (Nagoya Univ.),
Galaxy Evolution Workshop 2019

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spaceML• Model is pre-trained using
IMAGENET (Deng et al. 2009)• Model is re-trained using Galaxy
Zoo (Darg et al. 2010) as trainingdata• Training Set – stratified sample from3003 GZ mergers and10000 random non-mergers• Outperforms previous methods inidentification

Kiyoaki Christopher Omori (Nagoya Univ.),
Galaxy Evolution Workshop 2019

Ackermann et al. (2018)

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Target Selection
https://www.sdss.org/instruments/• We have used this model on
images of MaNGA FIREFLY VACgalaxies• 4675 galaxies in the catalogue• 67 interacting objects• 18 Removed

67Classified• 49 interacting < - Our focus

18Removed
Kiyoaki Christopher Omori (Nagoya Univ.),
Galaxy Evolution Workshop 201949SampleWhere are the galaxies buildingmass?
• Apply PCA on galaxy optical
spectra of spatially resolved
spaxels in MaNGA catalogue• Follows the methodin Wild et al. (2007)• PC1 – related to strength of
4000 Å break• PC2 – total excess
Balmer absorption

PC2 (excess Balmer Absorption)• We have used the PCA
method taken by Rowlands etal. (2018)

PC1 (4000 Å break)Rowlands et al. (2018)

Kiyoaki Christopher Omori (Nagoya Univ.),
Galaxy Evolution Workshop 2019

8/17

Where are the galaxies building
Post-Starburstmass?
• Apply PCA on galaxy opticalspectra of spatially resolved
spaxels in MaNGA catalogue• Follows the methodin Wild et al. (2007)

PC2 (excess Balmer Absorption)• We have used the PCA
method taken by Rowlands etal. (2018)• PC1 – related to strength of
4000 Å break• PC2 – total excess
Balmer absorption
Starburst

8/17
Green Valley

Starforming

PC1 (4000 Å break)Rowlands et al. (2018)

Kiyoaki Christopher Omori (Nagoya Univ.),
Galaxy Evolution Workshop 2019

QuiescentSome Results of PCA

Kiyoaki Christopher Omori (Nagoya Univ.),
Galaxy Evolution Workshop 20199/17

Some Results of PCA
GENERAL OBSERVATIONS:• Galaxies show increased star formation ininteracting regions• No conclusive “trend” or “relation” asinteracting galaxies are all different• We will focus on a particular galaxy for thispresentation

Kiyoaki Christopher Omori (Nagoya Univ.),
Galaxy Evolution Workshop 2019

9/17

MaNGA 7443-12703

10/17• Mrk 848
• z ≅ 0.049• Binary AGN and SF-composite pair
(Fu et al. 2018)• Two cores are approximately 4.9 kpc
apart (HST)• Thought to be in process of merger(HST)
https://www.spacetelescope.org/images/heic0810bw/Kiyoaki Christopher Omori (Nagoya Univ.),
Galaxy Evolution Workshop 2019

MaNGA 7443-12703 – SFH

11/17• We have obtained SFH using MaNGA FIREFLY VAC
12345678• We have plotted the spatially resolved stellar ages, from youngest (top left) to oldest (bottomright) (Note:Not all regions require 8 SSPs to resolve!)• It can be seen that there exists recent star formation episodes, particularly in interacting
locations, as shown by the blue patches in the top left plot
Kiyoaki Christopher Omori (Nagoya Univ.),
Galaxy Evolution Workshop 2019

MaNGA 7443-12703 – SFH

12/17• We have obtained SFH using MaNGA FIREFLY VAC
12345678• This plots the mass-weighted contribution of each SSP for each region, withblue regions having very little contribution and red regions having a high contribution
Kiyoaki Christopher Omori (Nagoya Univ.),
Galaxy Evolution Workshop 2019

MaNGA 7443-12703 – SFH

12/17• We have obtained SFH using MaNGA FIREFLY VAC
1523• Preliminary conclusions from SFH

4• Recent star formation in interacting region
678• The stellar populationoccurring
due to interaction
is dominant in interacting region• Regions excluding the interacting regions:• Can be resolved using less SSPs
• This plots•themass-weightedcontributionof eachisSSP
for each region, withDominant
stellarpopulation
older
blue regions having very little contribution and red regions having a high contribution
Kiyoaki Christopher Omori (Nagoya Univ.),
Galaxy Evolution Workshop 2019

MaNGA 7443-12703 – PCA Results

Kiyoaki Christopher Omori (Nagoya Univ.),
Galaxy Evolution Workshop 2019

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MaNGA 7443-12703 – PCA Results

Kiyoaki Christopher Omori (Nagoya Univ.),
Galaxy Evolution Workshop 2019

13/17

MaNGA 7443-12703 – PCA Results

13/17

Starburst here as
well…what
process here?

Starburst in interacting region

Kiyoaki Christopher Omori (Nagoya Univ.),
Galaxy Evolution Workshop 2019

MaNGA 7443-12703 – PCA Results

13/17• Preliminary conclusions from PCA• PCA results shows consistency with SFH dataobtained using MaNGA FIREFLY
Starburst here as
well…what
process here?

• However there exists another starbursting region• Other data will be required for further analysis
Starburst in interacting region

Kiyoaki Christopher Omori (Nagoya Univ.),
Galaxy Evolution Workshop 2019

14/17Issues• Issues with CNN• False Positives and False Negatives –classification is still questionable onaccuracy• Is transfer learning really the mostefficient?

• Issues with PCA• PCA itself is a WIP – edge-on galaxies
have poor results,
not all data is reliable• Inconsistencies with PCA andobservational data
Kiyoaki Christopher Omori (Nagoya Univ.),
Galaxy Evolution Workshop 2019

False Positive (Above) andFalse Negative (Below)
classified galaxies

MaNGA 8568-12704 – Inconsistent 15/17• Example of inconsistentdata• MaNGA r-band shows noobservation in bottomleft region

Kiyoaki Christopher Omori (Nagoya Univ.),
Galaxy Evolution Workshop 2019

MaNGA 8568-12704 – Inconsistent 16/17• However PCA shows the bottom left region as green valley• Other MaNGA related data (for example WHAN diagram)show no observations in area
Kiyoaki Christopher Omori (Nagoya Univ.),
Galaxy Evolution Workshop 2019

17/17

Conclusions• Using MaNGA FIREFLY VAC information andPCA method highlighted in Rowlands et al. (2018),
we have taken a look into theSFH of interacting galaxies• For many interacting galaxies, there exists anincreased star formation in interacting regions,which confirms that higher SFR is a result ofinteraction• However issues and inconsistencies exist which
need to be lookedatKiyoaki Christopher Omori (Nagoya Univ.),
Galaxy Evolution Workshop 2019

Appendix• CNN Information (Ackermann et al. 2018)

Left: Probability distribution of classifier
Kiyoaki Christopher Omori (Nagoya Univ.),
Right: ROC curve of classifier
Galaxy Evolution Workshop 2019

Appendix – Other Data
• Reddening Data(Dust Extinction)• Dust Attenuation ininteracting regions

Kiyoaki Christopher Omori (Nagoya Univ.),
Galaxy Evolution Workshop 2019