1. A Novel Technique for the Determination of IMF at Low Masses: A Few Results for Galaxies with Old Stellar Populations Nikolay Podorvanyuk 1 Igor Chilingarian 2,1 Ivan Katkov 1 1 Sternberg Astronomical Institute, Moscow State University (Russia) 2 Smithsonian Astrophysical Observatory, Harvard-Smithsonian Center for Astrophysics (USA)

2. 2008: The first trouble with the IMF For UCD3 the stellar mass derived using the Salpeter IMF becomes significantly larger than the dynamical estimate and shows “negative” dark matter fraction (Chilingarian, Cayatte, Bergond, 2008)

3. 2011: UCD and GC dynamics Massive GCs and UCDs where the dynamical relaxation timescale exceeds t Hubble suggest that Salpeter IMF is implausible in certain cases: again, M * > M dyn Kroupa IMF Chilingarian et al. (2011)

4. How can M * exceed M dyn ? Big fat black hole – NO (its mass must be negative) Dark matter – NO (again, it should be negative) Difference caused by IMF - YES Immediate consequence: if we take a sample of dark matter free objects with ( M/L) dyn obtained from internal dynamics, we can reliably determine the IMF shape using full spectral fitting, because the IMF-age degeneracy will be broken

5. Our technique: in general We developed a new technique for the determination of the low-mass slope (α 1 ; M * < 0.5M ⊙ ) of the present day stellar mass function (PDMF) using pixel space fitting of integrated light spectra This technique can be used to constrain the IMF of stellar systems with relaxation timescales exceeding the Hubble time (e.g., globular clusters where a mass segregation have not yet finished; UCD) It can be used for objects with older stellar populations (t > 4 Gyr; for example, massive elliptical galaxies) where massive stars affecting the potentially variable high-end IMF slope have already evolved into stellar remnants

6. An extension of NBURSTS Our technique was developed as an extension of the NBURSTS package (Chilingarian et al. 2007a,b) -- an approach to determine the parametrized line of-sight velocity distribution (LOSVD) and star formation history (SFH) of unresolved stellar populations by means of the full spectral fitting in the pixel space. First attempts made by I. Chilingarian in 2009: computed model grids for a range of  1 values using PEGASE-HR and attempted to fit Galactic GCs from Schiavon et al. (2005). The final version of the code presented in: Podorvanyuk N., Chilingarian I., Katkov I., A new technique for the determination of the initial mass function in unresolved stellar populations. MNRAS, 2013, 432 (4): 2632-2638. arXiv:1303.0897

7. An extension of NBURST We provide two versions of the technique: (1) a fully unconstrained determination of age, metallicity and α 1 : for high signal-to-noise data (2) a constrained fitting by imposing the externally determined mass-to-light ratio (M/L) of the stellar population.

8. Monte-Carlo simulations (a)age, metallicity and α 1 can be precisely determined by applying the unconstrained version of the code to high signal-to-noise datasets (S/N=100, R = 7000 yield α 1 ~0.1); (b)the M/L constraint significantly improves the precision and reduces the degeneracies, however its systematic errors will cause biased α 1 estimates (c)Lower signal-to-noise ratios are affected by the age-  1 degeneracy

9. The distribution of α 1 -sensitive information IMF-sensitive information is not associated with Lick indices Most of the IMF-sensitive spectral regions are those containing numerous but relatively faint absorption lines sensitive to the surface gravity in the atmospheres of late type (GKM) stars. These lines associated with CaI, VI, CoI, NiI and TiI absorption lines

10. Real data: UCD Application of our technique to real data for the massive ultracompact dwarf galaxy in the Fornax cluster UCD3 (high-resolution data were obtained with FLAMES- HR/Giraffe at ESO VLT and presented in Miske et al. 2008 and Chilingarian et al 2011). The top panel shows the spectrum of UCD3 (black) with the resolution reduced to R=10000, its best-fitting template (red) and fitting residuals (blue). The bottom panel displays the confidence levels of stellar population parameters and the IMF slope in the 3D parameter space in two projections (age- metallicity and age-IMF) for both versions of our approach. Blue contours are for the 1 st version (unrestricted 3D fit) and red contours are for the 2 nd version with the dynamical M/L=4.0 in solar units. Black and green contours correspond to 1  errors of the dynamical M/L. (2) high-resolution (R=17000) intermediate signal-to-noise spectra of UCDs in the Fornax cluster obtained at ESO VLT in narrow wavelength interval, 5120< λ <5450°A (Mieske et al. 2008; Chilingarian et al. 2011) downgraded to R=10000 Very short wavelength range without any strong IMF sensitive features makes the α 1 determination impossible for the unconstrained version of our technique bringing the statistical error to  1 =0.8 even for the brightest object, UCD 3

11. Real data: UCD For UCD 1 and VUCD 5 both, fully unconstrained fitting and the M/L-imposed version of the technique agree well within the statistical uncertainties of the unconstrained fitting. Intermediate resolution (R=1500–2000) high signal-to-noise ratio spectra (3900< λ <6800 A) of Fornax and Virgo cluster UCDs obtained with Gemini GMOS-N/S (Francis et al. 2012)

12. Real data: UCD For UCD 5 having low metallicity and therefore much weaker absorption lines, the unconstrained fitting results in very large uncertainties while the adopted (M/L) V value puts it close to the edge of the model grid on the MF slope (2.8).

13. Real data: UCD For VUCD 3 the situation is inverse, the unconstrained fitting yields α 1 ~2.9, however one has to keep in mind that this object has extremely high value of the [ α /Fe] chemical abundance ratio (Francis et al. 2012) resulting in the significant template mismatch, therefore the unconstrained measurements may get biased.

14. At present, we do not have empirical stellar population models required to study possible biases that may be introduced to the IMF measurements from integrated light spectra by significantly non-solar abundance ratios.

15. Another example: 3000 massive ellipticals We have chosen 3000 ellipticals with highest S/N ratios from Saulder et al. (2013) where ~80k elliptical galaxies from SDSS were used for the calibration of the fundamental plane.

16. Read data: 3000 massive elliptical galaxies Determination of the low-mass IMF slope using unconstrain version of our technique for 3000 giant elliptical galaxies form SDSS: the low-mass IMF for the most of objects is bottom-heavy (α 1 ~ 3.0-3.5). These estimates have statistical errors  1 = 0.1-0.3. Systematics (e.g. due to  /Fe and Na abundances) is unknown KEY POINT: We do not see any evidence that the low mass IMF slope in giant ellipticals depends on their mass.

17. Comparision with MILES models MILES – Bimodal and Unimodal PEGASE – α 1 as a free parameter (not just 1.3!)

18. PEGASE / MILES (SDSS) PEGASE.HR and MILES are both based on empirical stellar libraries with very few late type dwarfs (K, M): a clear source of problems dealing with the bottom-heavy IMF shape

19. MILES / MILES (SDSS) The pattern suggests interpolation problems in the population synthesis code: stellar library or evolutionary tracks / isochrones or both

20. Conclusions better quality of stellar population models is required for reliable quantitative IMF studies: – completeness of stellar libraries (K,M dwarfs) – better handling of interpolation in the codes a lot of troubles due to degeneracies especially if the wavelength coverage is not optimal: it can become really bad already for 3 parameters (age-[Fe/H]-  1 ) at moderate S/N=50-70 non-solar abundance ratios can become a serious problem that has to be studied in detail

21. Thank you for attention!