1. The History of the M31 Disk from Resolved Stellar Populations as seen by PHAT
Hi. My name is Alexia Lewis. I’m working under Julianne Dalcanton at the University of Washington, and I’m going to tell you today about some of the work I’ve been doing mapping the SFH of a portion of M31’s stellar disk using the power of resolved stellar populations as seen by the Hubble Space telescope.
Alexia Lewis
University of Washington
The Structure and Dynamics of Disk Galaxies
12 August 2013
Julianne Dalcanton (UW)
Ben Williams (UW)
Dan Weisz (UW, UCSC)
Adam Leroy (NRAO)
Andy Dolphin (Raytheon)

2. Panchromatic Hubble Andromeda Treasury (PHAT)
HST multi-cycle program:
828 orbits
Data collection:
>100 million stars
Total area: 0.5 deg2
24 μm
24 μm
HST multi-cycle program, 1/3 of M31 star-forming disk in 6 bands UV-Optical-NIR
In the optical, reach to 27/28 mag in outer parts of disk – shallower toward bulge
First, a little bit about PHAT, the panchromatic Hubble Andromeda treasure. It is an HST multi-cycle program, awarded 828 orbits to map ~1/3 of the star-forming disk of M31, in 6 bands from the near UV to the near IR. The survey is split into 23 ‘bricks’, the footprint is shown here on an image of M31 in 24 microns. You can see that the bricks cover a wide variety of environments, from the bulge, to the dusty 10kpc ring, to outer much less crowded regions. Data collection started in 2009 and finishes up this year. In the end, we will have a catalog of more than 100 million individually resolved stars. Much of the work I’m going to talk about today has been done in B15, highlighted here, which falls on the 10 kpc ring.
Dalcanton+ 2012
Dalcanton+ 2012
Alexia Lewis
12 August 2013

3. B15 F336W + F475W + F814W + F160W approx. 1.5 x 3 kpc Dalcanton+ 2012
This is a 4 band image of B15. You can see that we can pick out many individually resolved stars. You can also see that there is a lot of structure in the gas and dust in this region, which makes it a very interesting place to do science.
approx. 1.5 x 3 kpc
Dalcanton+ 2012
Alexia Lewis
12 August 2013

4. Optical Color-Magnitude DiagramMS
BHeBs
RHeBs
AGB
RGB
SGB
Oldest MSTO
Lower MS
MATCH
Brighter
Ingredients
IMF
Binary Fraction
Stellar Models
Filter Convolution
Bin the CMD
To map the SFH of M31 with resolved stars, we use a CMD-based analysis. Encoded within the CMD is the region’s past pattern of star formation and metallicity evolution. This is an example CMD from an ideal synthetic galaxy, where the individual stars have been color-coded by age. In order to get a handle on the recent SFH of a region, you need to use main sequence stars or core helium burning stars, colored dark blue. If you want to get at the ancient SFH, you at least need the RGB, though if the data is deep enough, the ancient main sequence turnoff provides the most constraints.
This information can be extracted by comparing the observed CMD with syntheticCMDs generated from stellar evolution models. We do this using a routine called MATCH, described in Dolphin There are a number of user-defined parameters, namely the choice of IMF, the binary fraction, which stellar models to use, which filters to use, and how the cmd will be binned.
Dolphin 2002
Alexia Lewis
12 August 2013

5. Adjust SFH, Extinction until Model = Data
F475W – F814W
F475W
Ingredients
IMF
Binary Fraction
Stellar Models
Filter Convolution
Bin the CMD
Observational Errors
Extinction
Differential Extinction
Of course, this is an idealized CMD. In reality, the data is not that perfect. We have to also account for observational errors and photometric completeness, which are characterized by extensive artificial star tests, as well as the significant amounts of extinction present in M31. With these inputs, we build up a model CMD as a linear combination of simple stellar populations over a user-provided age and metallicity range. This means that many possible model CMDs can be created.
Model CMD = Linear combination of SSPs
Alexia Lewis
12 August 2013

6. Recovering SFHs “Observed” CMD Model CMD
observed density
in bin i: ni
model density
in bin i: mi
Each model CMD is compared to the observed CMD by examining the density of points in each bin. This information is combined to get the total likelihood (using a Poisson likelihood function) for the SFH of that particular CMD. This is repeated for each possible CMD representing different SFHs. The model that maximizes the likelihood of fitting the observed CMD is the most likely SFH.
For a given bin, the number density of observed points 'n' is compared to the number density of model points 'm' using the displayed likelihood function, and then done over the entire CMD to get the total likelihood function for the SFH of a particular synthetic CMD.  The process is then repeated for another SFH, and on and on, and in the end, the model that maximizes the likelihood of fitting the observed CMD is the most likely SFH.
Maximize likelihood to find most likely SFH
Alexia Lewis
12 August 2013

7. Recent SFH of the 10-kpc Ring
We are using this technique to look at the recent SFH across the PHAT survey. We have started with two bricks that cover part of the 10-kpc star-forming ring. These two bricks are outlined in red in the image. We split each brick into 450 regions, approx. 100 pc on a side and recover the SFH in each region independently.
Survey Area: 0.5 deg2
Each brick: ~1.5 x 3 kpc (12’ x 6’.5)
Alexia Lewis
12 August 2013

8. Recent SFHs Old stars = excluded region F475W F475W – F814W
As an example, this is the observed and modeled CMD for a region in B15. We are using the optical data from PHAT to examine the recent SFHs as they provide the most constraints on the youngest stars and reach the deepest CMD features of the three cameras. There is a significant amount of dust in M31, which can be seen on the CMD in the broadening of the MS and the elongation of the red clump along the reddening vector. The extinction profiles of the oldest and youngest populations will be different because the youngest stars reside closest to the midplane and the oldest stars are further away. So, in order to avoid some of the problems associated with dust in M31, we choose to exclude the oldest populations from, so that the resulting SFHs and extinction parameters are fit only to the youngest stars. The excluded region is set off in red. As a result, the timescale of the recent SFH is reliable back to 500 Myrs.
F475W: SDSS G
F814W: Johnson I
F475W – F814W
F475W – F814W
Old stars = excluded region
Lewis+ (in prep)
Alexia Lewis
12 August 2013

9. Recent SFH of the 10-kpc Ring
pixels
500 pc
SFR set such that the everything below 10-7 is black
We took each brick and divided it into 450 equal sized regions, approx. 100 pc on a side, and did this CMD-fitting process in each region. When the information is combined into a single image, we can look at the average SFR in different time ranges. This is a cut of the SFR from 0-10 Myrs, where lighter orange and white are higher SFR and black is lower. This image is scaled such that everything below 10e-7 M_sun per year is black. We fit a SFH separately for each region, so we can look individually at some of them in different environments. This first one is in the middle of an OB association, so it is actively forming stars. That is shown in the SFH with a large burst of SF at the current time. If we look at a region that does not appear to be forming stars, we see in the SFH that there was a period of SF 200 Myrs ago, but nothing since. As you can see, on small scales, there can be substantial changes in the SFH.
There are a wide variety of SFHs on small scales
Lewis+ (in prep)
Alexia Lewis
12 August 2013

10. Recent SFH of the 10-kpc Ring
Good spatial correspondence
Mean age of stellar populations contributing to emission (Kennicutt & Evans 2012) :
FUV: 10 Myr
Hα: 3 Myr
500 pc
Because we can only resolve individual stars in very nearby galaxies, much work done to measure SFRs of other galaxies must use other methods, generally broadband tracers of SF like FUV or Halpha emission. These are generally accurate for present day SF. Though FUV traces stellar populations back to 100 million years, the mean age of stars contributing to the emission in FUV is 10 Myrs. In Halpha, the mean age is 3 Myrs though it traces populations back to 10 Myrs. In this way, we can compare the SFR from our very recent time bins with these tracers, and in doing so see that there is pretty good spatial correspondence. This helps validate this method of SFH recovery.
Lewis+ (in prep)
Alexia Lewis
12 August 2013

11. Starting at 250 Myrs, moving toward present in 5 Myr increments
Output SFH from MATCH has been interpolated to 5 Myr scale
Low SF: black, high SF: orange, white
Low SF scale set to 1e-05 to show structure more distinctly
Notice ring structure: each pixel SFH was derived independently of the others – CMD fitting finds the ring structure well!
Stability of sf in ring – also seen on much larger scale by Williams 2003, Davidge+ 2012
Lewis+ (in prep)
Alexia Lewis
12 August 2013

12. Recent SFHs: Applications
Correlations between SFHs and gas and SFR tracers
Examine Kennicutt-Schmidt relation on small scales
No assumptions about constant SFR
Evolution of SF-ing events and the timescales on which they occur
log Σgas
log ΣSFR
So what can we do with all of these SFHs? One of the big things is to take a look at the relationship between SFH and the ism to see how well gas and dust trace out star forming regions and how well these other SF tracers actually estimate recent SF. In addition, we can examine the Kennicutt-Schmidt relation on small scales. There is an approximately power-law relationship between SFR surface density and gas surface density, with a break at lower SFR and gas. This is a measure that was calibrated on large scales and has often been found to break down on small scales because SFR is characterized by something like FUV or Halpha, and the assumptions about converting these tracers to SFRs, such as constant SFR, do not apply on small scales where the SFR surface density is very low. However, SFHs derived from CMD analysis do not suffer from these assumptions, so it may be possible to calibrate this relation on small scales. We also hope to examine individual SF-ing events and observe there movement across the disk.
Bigiel+ 2008
Alexia Lewis
12 August 2013

13. Ancient SFHs Recover SFH on larger spatial scales (500 pc)
Extend ancient SFH studies from outer disk (Bernard+ 2012, Brown+ 2006,2007,2008) to inner disk
But dust is a problem MS
F475W
RGB
Finally, we will expand our SFH mapping analysis to larger regions within which we hope to place constraints on the ancient SFH of the disk of M31. Previous studies have examined the ancient SFH of M31 in the far outer parts of the disk, but no one has attempted to look at the buildup of mass in the inner disk. This is primarily because dust is a severe problem in the inner disk (although Bernard et al did find significant reddening in their outer disk field). In order to do this analysis in the inner disk, we have to model extinction. Dust is a significant problem. You can see the broadening of the MS and especially the elongation of the RC along the reddening vector. To do this, we use an updated version of the same CMD analysis routine, which includes a new model for extinction.
We do this on larger scales, so expect a mixing of the older populations. We can also fit some of the dust parameters that should be more global.
While our inner disk data is limited in depth (we don’t get down to the ancient MSTO) we hope to be able to provide some constraints on the mass buildup of the inner disk over a Hubble time.
Median Av in this region: B15_3x6-004: 1.53, Assume Cardelli Extinction curve: E(B-V) = Av/Rv with Rv=3.1, –
Red Clump
F475W – F814W
Alexia Lewis
12 August 2013

14. Summary Recent SFHs at 100pc resolution Ancient SFHs
Stability of 10-kpc star-forming ring over ~200 Myr
Extend to more bricks
Propagation of SFing events, interplay between gas and SF on small scales
Ancient SFHs
Model extinction distributions of young and old populations
Look for radial trends in age, metallicity, SF, etc
PHAT publically available:
We will have the spatially resolved SFH of M31's star forming disk over it's entire lifetime and at high spatial resolution (much subkpc). I’ve shown you that the individually derived SFHs in 100 pc regions is able to reproduce the structure of the ring, and analysis of its evolution over the past 250 Myrs shows the remarkable stability of this ring. Expanding this analysis to more bricks will allow a study of recent star-forming events and their movement across the disk, and will allow us to look at the relation between SF and gas on small spatial scales.
And finally, we are extending this analysis to look at the ancient SFHs to examine the buildup of mass in the inner disk. This will allow us to examine radial trends in age, metallicity, and SFR and probe a wide variety of stellar environments.
Alexia Lewis
12 August 2013

15. Alexia Lewis
12 August 2013

16. B21 F336W + F475W + F814W + F160W Dalcanton+ 2012
For comparison, this is an image of brick 21 in the same 4 filters. The flux scaling is the same for this brick as for brick 15 which gives you a very good idea of the change in stellar density as well as the change of environment.
Dalcanton+ 2012
Alexia Lewis
12 August 2013

17. Measuring SFHs “Observed” CMD Model CMD
observed density
in bin i: ni
model density
in bin i: mi
Maximize to find most likely SFH
Dolphin 2002

18. Padova Models: Marigo 2008 with AGB updates in Girardi 2010
IMF: Salpeter
Binary Fraction: 0.35
50% completeness limit characterized by ASTs
Models: Marigo et al. 2008, updates to AGB tracks in Girardi et al. 2010, transformations to PHAT filters in Girardi et al. 2008
MATCH Dolphin 2002, More detailed age info, CMD built up of lots of ‘partial CMDs’ of SSPs – single age, single metallicity
Alexia Lewis
12 August 2013

19. Star Formation in UV-Bright Regions
Jake Simones (University of Minnesota)
PHAT B15
As I’ve mentioned, SFRs of galaxies are often measured using tracers such as FUV. One of the limitations of these tracers is that you must assume a constant SFR because there is no way to pick out if/when a burst of SF may have occurred over the last 100+ Myrs. Because CMD-based analysis does not have this limitation, it can be used to examine the assumptions made when using FUV flux to infer SFR to see where the SSP, constant SFR assumptions break down. This is work being led by Jake Simones, a graduate student at the University of Minnesota working under Evan Skillman. He is examining UV bright regions, as defined by Kang et al This is a GALEX 2-color image of B15 with the UV regions outlined in cyan. Most of the regions in this brick are quite small (< 100 pc).
UV bright regions from Kang catalog
Simones+
(submitted)
Alexia Lewis
12 August 2013

20. Star Formation in UV-Bright Regions
Age (Myr)
200 50
100
150
UV flux-determined SFRs assume SSP or constant SFR
Not a good assumption
Purple line: SFR from FUV flux
Blue line: <SFR> over 100 Myr
Green line: <SFR> over 500 Myr
Single Age, Single SFR not good approximations
Caveat: Region sizes are small (< 100 pc)
SFR measured by FUV flux generally assumes SSP or constant SFr
Get age of SSP from FUV-NUV color – may not be a reliable estimate: none correspond to time of SF
Blue dashed line: SFR from integrated FUV flux
Kang+ estimated age of each region using FUV-NUV color and comparing with padova stellar models. Estimating age in this way necessarily assumes that the regions are SSPs. However, it is clear from these SFHs that the regions have a mixture of stars at different ages. They calculated that 6 of the 33 regions can be approximated by an SSP because >95% of the mass formed in the last 100 Myrs was formed in a single time bin. However, even for regions that resemble SSPs, only 3 of them have SFH derived ages within 10 Myrs of the UV color-derived ages. The age discrepancy in the other 3 regions that could be described as SSPs is unclear. While innacuracies in reddenning could affect UV color, which would then affect age estimates, the difference in reddening values found by Kang et al 2009 and those determined by Simones et al would have to be significantly more than it is. Even if that were the case, the difference is not systematic, i.e. one of the regions has the Kang value greater than the SFH value, while the other two are less. As a result, it is clear that the SSP assumption for deriving ages is unreliable, even in the most likely cases.
These are mostly less than 100pc size regions – will be increasing size to see at what scale the SFH resemble constant SFR
Simones+ (submitted)
Alexia Lewis
12 August 2013

21. Star Formation in UV-Bright Regions
Masses differ up to 2 orders of magnitude
If you measure the FUV flux of a UV bright region and convert it to a SFR using the Kennicutt SFR recipe, i.e., constant SFR over 100 Myr, it systematically overpredicts the SFRs from CMD based SFHs when averaged over the same timescale by a factor of 3-5. The scatter in the SFH derived mean SFR shows that regions with similar FUV SFRs can have very different SFHs., which produces the large spread. This illustrates the breakdown of the FUV flux-to-SFR recipes on small physical scales. No simple mapping between FUV flux and SFR
The SSP assumption also affects the inferred masses of each region. Kang et al. assume FUV bright regions to be SSPs, and used FUV-NUV colors to infer a mass.  Compared to the masses derived from integrating the CMD-based SFHs, we find they are off by up to 2 orders of magnitudes. They also see that UV-derived estimates depend strongly on color. So SSP assumption can underestimate masses of blue regions and overestimate masses of redder regions.
Expanding this analysis to more and larger regions will help to assess the use of UV color as an age estimate and determine how appropriate the SSP, single SFR assumption is for various stellar populations.
Integrated FUV flux over-predicts mean SFR from CMD-based SFH by a factor of 3- 5
Simones+ (submitted)
Alexia Lewis
12 August 2013

22. B15
Dalcanton+ 2012
Alexia Lewis
12 August 2013

23. Ancient SFHs Most mass forms between 10 and 6 Gyr ago. PRELIMINARY
B15F01
B15F04
Most mass forms between 10 and 6 Gyr ago.
Alexia Lewis
12 August 2013

24. Summary Recent SFHs at 100pc resolution SFH of UV-Bright Regions
Stability of 10-kpc star-forming ring over ~200 Myr
Extend to more bricks
Propagation of SFing events, interplay between gas and SF on small scales
SFH of UV-Bright Regions
UV-based estimates over-predict SFR, show mass discrepancy compared to SFH based quantities
Expand to larger regions
Ancient SFHs
Look for radial trends in age, metallicity, SF, etc
PHAT publically available:
We will have the spatially resolved SFH of M31's star forming disk over it's entire lifetime and at high spatial resolution (much subkpc). I’ve shown you that the individually derived SFHs in 100 pc regions is able to reproduce the structure of the ring, and analysis of its evolution over the past 250 Myrs shows the remarkable stability of this ring. Expanding this analysis to more bricks will allow a study of recent star-forming events and their movement across the disk, and will allow us to look at the relation between SF and gas on small spatial scales.
Using CMD-derived SFHs, we’ve shown that discrepancies exist between UV determined masses, ages, and SFRs, and those determined from the SFHs. CMD-based analysis should help to determine the reliability of using these estimates for various spatial scales and stellar populations.
And finally, we are extending this analysis to look at the ancient SFHs to examine the buildup of mass in the inner disk. This will allow us to examine radial trends in age, metallicity, and SFR and probe a wide variety of stellar environments.
Alexia Lewis
12 August 2013