1. Statistical Tools applied to the Magellanic Bridge Statistical tools applied to the H I Magellanic Bridge Erik Muller (UOW, ATNF) Supervisors: Lister Staveley-Smith (ATNF) Bill Zealey (UOW)

2. Statistical Tools applied to the Magellanic Bridge Introduction Statistical tools provide a means to –compare populations of similar objects between different systems –Understand and model general trends and behaviours. –Distinguish between sub-populations Spectral correlation function (SCF): Measures spectral similarity as a function of radial separation Power spectrum analysis (PS): Measures power as a function of scale, and as a function of velocity range. Both SCF and PS have been used to infer information about the third spatial dimension.

3. Statistical Tools applied to the Magellanic Bridge Data set (ATCA +Parkes): Peak pixel H I map, Magellanic Bridge

4. Statistical Tools applied to the Magellanic Bridge Spectral Tools 1: Specral Correlation function: –Compares two spectra separated by Δr, and makes an estimate of their ‘similarity’ –A 2D map of mean SCF shows rate of change (or degree of corrleation) of SCF with Δr and θ –Has been used to confirm a characteristic length for the scale height of the LMC, by measuring the radius of decorrelation (Padoan et al. 2001) –In this case, SCF shows that MB spectra has a longer decorrelation length in the east-west direction. (Tidal stretching)

5. Statistical Tools applied to the Magellanic Bridge Spatial power spectrum –Used to show the range of spatial scales present in source –Highlights any process favouring a particular scale. (Eg. Elmegreen, Kim, Staveley-Smith, 2001) –Using velocity averaging, is can be used to show the relative contributions of density and velocity dominated fluctuations. (Lazarian & Pogosyan, 2001) Spectral Tools 2:

6. Statistical Tools applied to the Magellanic Bridge Spectral Correlation function How it works: Δr

7. Statistical Tools applied to the Magellanic Bridge SCF output maps:

8. Statistical Tools applied to the Magellanic Bridge T maps SCF maps 55 pixels 37 pixels

9. Statistical Tools applied to the Magellanic Bridge +ve and –ve fit departures +ve departures at ~250-380pc (14’-22’ at 60kpc) -ve departures for sub images where signal is lower and less well distributed throughout. Fits in E-W and N-S directions (central 5 rows/columns) ΣT=7.5x10 5 K.km/sΣT=8.4x10 5 K.km/sΣT=9.4x10 5 K.km/s ΣT=1.0x10 6 K.km/s ΣT=1.1x10 6 K.km/s

10. Statistical Tools applied to the Magellanic Bridge SCF summary: In general, decorrelation of spectra separated by Δr occurs at ~200-400pc Estimated thickness of MB is ~5kpc, based on distance measurements for two OB associations separated by ~7’ (Demers & Battinelli, 1998) Results of SCF are difficult to interpret in the same way for LMC, PS analysis may help. SCF behaves strangely for datacubes containing low S/N The line of minimum rate of change of SCF is points almost, but not quite, E-W, towards the SMC and LMC.

11. Statistical Tools applied to the Magellanic Bridge Spatial Power spectrum Measures the rate of change of power with spatial scale Works on Fourier inverted image data (edges are rounded by convol with a gaussian) Channels with significant signal selected (60 channels) Filtered to reduce leakage from low spatial frequencies (image convolved with 3x3 unsharp mask, then divided back into FFT data) Un-observed UV data is masked out. Power-law fit to dataset (γ) (IDL poly_fit). A range of velocity increments are examined to determine the relative contributions of density (thin regime) and velocity (thick regime) fluctuations.

12. Statistical Tools applied to the Magellanic Bridge Spatial Power spectrum cont. ATCA + Parkes data (+Gaussian rounding) FFT (im 2 +r 2 )

13. Statistical Tools applied to the Magellanic Bridge Power law fit for Brightness 2 [K 2 ] Spatial Power spectrum cont. γ – velocity binsize Transition from thin to thick regime (velocity to density dominated regime)

14. Statistical Tools applied to the Magellanic Bridge General result: All Power spectra, for all velocity bins are featureless and well fit with by a single power law: No processes present that lead to a dominant scale (c/w LMC) More ‘3 dimensional’ than the LMC (Similar to SMC). i.e. no characteristic thickness. Power spectra steepen for increasing velocity bin size (ΔV~<20km/s) Transition from ‘thin’ velocity dominated (spectral ΔV ~< integrated ΔV thickness) to thick, density dominated regime. γ changes from ~-2.90 - ~-3.35, consistent with Kolmogorov Turbulence. (Lazarian & Pogosyan, 2000) Source of turbulence? –Processes that do not show a scale preference: Stirring & instabilites from tidal force of LMC and SMC? Energy deposition into ISM from stellar population?

15. Statistical Tools applied to the Magellanic Bridge PS from other systems: LMC (Elmegreen, Kim & Staveley-Smith, 2001) much steeper; γ ~<2.7 (Entire velocity range, two linear fits) LMC spectra turns over at r~100pc –attributed to line-of-sight thickness of LMC. SMC (Stanimirovic, Lazarian, 2001) SMC and MB cover same range of γ: –γ SMC ~ 3.4 at ΔV ~100km/s –γ MB ~ 3.3 at ΔV ~100km/s linear (featureless) over entire range of Δv does not appear to approach a characteristic Δv Galaxy (Dickey et al. 2001) Analysed for smaller range of Δv (0-20 km/s) Inner Galaxy γ ~ -2.5 - -4, consistent with Kolmogorov turbulence. All systems show steepening of γ with ΔV.

16. Statistical Tools applied to the Magellanic Bridge SMC and Galaxy γ with ΔV SMC γ with ΔV. (Stanimirovic & Lazarian, 2001) Galaxy γ with ΔV. (Dickey et al 2001) (N.B. Inverted γ scale, linear ΔV scale)

17. Statistical Tools applied to the Magellanic Bridge Overall There is no suggestion of a departure from a power law fit to MB spatial power spectra, despite a decorrelation at ~200-400pc found using SCF. (c/w Padoan et al, 2001) SCF shows more persistent correlation in W-E direction (due to its tidal origin) PS shows transition from γ =~-2.9 to γ =-3.35, through thin to thick regime, consistent with Kolmogorov turbulence.