Download Investigating the Low-Mass Stellar Initial Mass Function in

Investigating the LowMass Stellar Initial
Mass Function in
Draco
Soroush Sotoudeh
(University of Minnesota)
Daniel Weisz, Andrew Dolphin, Evan Skillman
06/29/2015 – STScI Workshop on IMF
Outline
 Introduction
 Observations and data
 IMF analysis and results
 Radial variations
 Model dependencies
 Ongoing and future work
Introduction
 There’s no doubt that understanding the stellar IMF is one of the most
important and sought after issues in astronomy, theoretically and
observationally!
 The majority of previous investigations have calculated the distribution of
masses from observations of stars near the Sun.
 IMF studies that are based on the local luminosity function offer both
advantages and disadvantages over alternative methods.
 Dwarf galaxies in which the low mass IMF has been directly measured
are the Milky Way satellites Ursa Minor (Wyse et al. 2002), SMC
(Kalirai et al. 2013), Draco (Grillmair et al. 1998), Hercules and Leo IV
(Geha et al. 2013).
 Deep and high-precision HST observations have made it possible to study
the stellar IMF beyond the Galaxy and in the Local Group.
Observations and Data
 Archival data from HST Advanced Camera for
Surveys (ACS) and Wide Field Camera 3 (WFC3)
for 3 different fields in Draco
 Point-Spread Function (PSF) photometry using the
DOLPHOT photometry package (Dolphin 2000)
 ~100k artificial star tests per field to quantify the
completeness and photometric uncertainties
Observations and Data
= Center
of Draco
Observations and Data
 Draco dwarf spheroidal (dSph) galaxy
(Wilson 1955) is among the very faint
and low surface brightness known
members of the Local Group of
galaxies (Mateo 1998).
 It appears to be dominated by very
old (age greater than 8-10 Gyr) and
metal deficient stellar populations
(Aparicio et al. 2001)

Moreover, it is reported to be highly
dark matter dominated (e.g. even up
to M/L = 340 - 610: see Kleyna et al.
(2002))
 No corrections for dynamical
evolution is required.
Observations and Data
Green regions
correspond to
a mass range of
~0.5 – 0.8 M☉
IMF Analysis and Results

We fit powerlaw IMFs to our data using the CMD fitting program MATCH
(Dolphin 2002).
 To test for systematics, we have used
Dartmouth,
BaSTI,
Padova
and Victoria
stellar evolutionary tracks and models in our studies.
IMF Analysis and Results: LFs
for Field 1
IMF Analysis and Results
IMF Analysis and Results:
Confidence Contours
1) BF remains
constant for a
given library
2) Γ shows a
slight radial
gradient for
all models
Radial Variations
Radial Variations
Model Dependencies
Model Dependencies
Single-sloped Powerlaw IMF
conclusions
 For a single-sloped power-law IMF model, we find that the
IMF slope steepens by up to 0.7 dex for radii between 150
and 300pc, while the binary fraction remains approximately
constant.
 The absolute values of the IMF slopes and BFs at any radius
depend strongly on the adopted stellar models, suggesting
that current knowledge of the lower-main sequence stars is
uncertain.
 We emphasize the importance of including variations in IMF
slope measurements due to multiple stellar models as a
means of estimating systematic uncertainties.
Ongoing and Future Work
Ongoing and Future Work
 Our lognormal analysis shows strong degeneracy in the
Characteristic mass-Dispersion plane.
 We need to hold one of these two parameters fixed and then
fit for the other one and the BF.
 We believe that this degeneracy can only be reduced with
data that extends to lower stellar masses.
Single-sloped Powerlaw IMF
conclusions
 For a single-sloped power-law IMF model, we find that the
IMF slope steepens by up to 0.7 dex for radii between 150
and 300pc, while the binary fraction remains approximately
constant.
 The absolute values of the IMF slopes and BFs at any radius
depend strongly on the adopted stellar models, suggesting
that current knowledge of the lower-main sequence stars is
uncertain.
 We emphasize the importance of including variations in IMF
slope measurements due to multiple stellar models as a
means of estimating systematic uncertainties.