1. Page 1 of 46SONG4, Charleston, Sep. 16, 2011 Can the Experience From Helioseismology Help us With SONG? Jesper Schou Stanford University schou@sun.stanford.edu

2. Page 2 of 46SONG4, Charleston, Sep. 16, 2011 Overview Motivation Observations Data Analysis Politics Conclusion

3. Page 3 of 46SONG4, Charleston, Sep. 16, 2011 Motivation Why am I here? –Got invited! But why? –Not clear Some theories –Tricking me into doing more asteroseismology –Share experiences from helioseismology –Data dump before being made redundant –Share good ideas (cause trouble) –Cause real trouble –Something else?

4. Page 4 of 46SONG4, Charleston, Sep. 16, 2011 Who is This Character? Used to analyze ground based single site data (Fourier Tach) Worked on MDI testing MDI data analysis –Also found first clear p-modes in other Sun-like star (alpha Cen) HMI instrument scientist –Calibration, testing, meetings, … –Minimal Kepler work HMI operations HMI data analysis I am totally out of the loop in asteroseismology I am not a Real Stanford Person ™ –Stanford restricts what non-real people can do

5. Page 5 of 46SONG4, Charleston, Sep. 16, 2011 Helioseismic Observations Used to do single site observations –Had sidelobes –Low duty cycle degrades S/N –Got systematic errors for modes where sidelobe spacing equals mode spacing Then got GONG and MDI –No significant sidelobes. –Got rid of many systematics But still have some –Got more modes and lower errors Use 72d for MDI/HMI and 108d for GONG –Modes are rapidly lost for length<lifetime Various analysis algorithms are used –Power spectrum versus Fourier transform fitting –One (n,l,m) at a time one (n,l) at a time and global fitting used We have resolved observations –So lots of issues not relevant to asteroseismology at this time

6. Page 6 of 46SONG4, Charleston, Sep. 16, 2011 Modes With Error Bars Overall scale measured to one part in 51e6.

7. Page 7 of 46SONG4, Charleston, Sep. 16, 2011 Asteroseismic Observations So what to do for SONG? Avoid peaks in window function in inconvenient places –Some scheduling flexibility with fixed resource use (total observing time) –May need to reconsider certain stars Quality almost always beats quantity! –An extra few modes may go a long way towards eliminating ambiguities –Modes are rapidly lost for length<lifetime Essential to observe for critical length of time –Variable quality can create a lot of noise and artifacts But if S/N is high then sigma depends weakly on S/N –See Libbrecht (1992, ApJ, 387, 712) –So low duty cycle may not be that bad Dangerous? Consider calculating information content –SVD techniques should work (see work by Brown and others) Do some sort of optimizination

8. Page 8 of 46SONG4, Charleston, Sep. 16, 2011 Data Analysis – Basic Reduction Use all information in the spectra –Doppler velocity is not the only observable! And it depends on wavelength and height in the atmosphere (position in line) –May also want to look at continuum, linedepth and/or equivalent width –Consider exploiting line and wavelength dependence Fit a good model of the spectra –You must know your instrument! –Physics must be correct –Statistics must be correct Use proper distribution Use correct noise –Understand your residuals –Understand your trends

9. Page 9 of 46SONG4, Charleston, Sep. 16, 2011 Data Analysis – Modeling of Temporal Spectra Model the data carefully –Leakage matrix (aka sensitivity to different modes) is non-trivial to calculate Limb darkening profile depends on wavelength, temperature, g, composition, etc. Not all lines are created equally – use proper average –Amplitudes and linewidths as a function of frequency are non-trivial Don’t be fooled by the Sun Neither need be smooth near avoided crossings Amplitude profile not simple Gaussian or some such! –Line profiles are not Lorentzian We see significant asymmetries for the Sun Asymmetry depends on frequency and observable (Doppler, intensity, …) –Background Sum of power laws? Implies global fit Watch the low frequencies. Detrending causes artifacts Watch high frequencies. Aliasing makes power law poor approximation

10. Page 10 of 46SONG4, Charleston, Sep. 16, 2011 Data Analysis – Fitting of Temporal Spectra Use proper statistics –See Anderson et al., (1990, ApJ, 364, 699) –Small improvements may seem small but are important Sqrt(T) grows slowly!

11. Page 11 of 46SONG4, Charleston, Sep. 16, 2011 Data Analysis – More Statistics Assuming 100% duty cycle and standard physics: –Different frequency points in Fourier transform are independent –Real and imaginary parts are normally distributed with equal variance –Assumptions include stochastically excited damped oscillator model And/or others –Follows that power spectra are exponentially distributed There is no information in the phase But assumptions do not hold –At low duty cycle different frequency points are not independent –Phases are not random –May be worth investigating/exploiting With more than one variable phase is also important

12. Page 12 of 46SONG4, Charleston, Sep. 16, 2011 Data Analysis – Fitting of Temporal Spectra Fitting strategy –Single mode fits Traditional method in helioseismology Fairly unbiased But modes are often lost –Multi mode fits Parameterize variation of mode parameters Linewidths and amplitudes vary slowly with frequency oBut watch out for avoided crossings –Global fits Fit asymptotics directly oVery poor approximation. oSubstantial loss of information Linearize around reference model oSee work by Vorontsov and Jefferies oShould work well if you believe that inversions work well oHard to test for quality of fit –Check your residuals! –Do independent analysis and encourage competition

13. Page 13 of 46SONG4, Charleston, Sep. 16, 2011 Data Analysis – Fitting of Temporal Spectra Gaps in time-series need to be considered –Not filling means sidelobes But filling may not be possible –Auto-regressive gap filler is likely the way to go Need detrending oCauses loss of low frequency power Time series must be uniform (eg. sites must be consistent) Only works well for spiky spectra –Gaps mean that points in power spectra are no longer independent Whether gaps are filled or not! Maximum likelihood estimator becomes complicated Errors become unreliable and correlated if careless

14. Page 14 of 46SONG4, Charleston, Sep. 16, 2011 Data Analysis – Other Issues Fit of multiple variables –Best for simultaneous observations Eg. two observables from the same observations –Learn about mode physics –Identify modes –Constrain geometry better Time variations –Fit all epochs simultaneously Mean frequencies plus parameterized time variation Testing is important –Many of these issues raised can be addressed using Monte Carlo methods Best done as hare and hounds But such methods are no better than the physics put in

15. Page 15 of 46SONG4, Charleston, Sep. 16, 2011 What Happens if you Don’t pay Attention

16. Page 16 of 46SONG4, Charleston, Sep. 16, 2011 What Happens if you Don’t pay Attention

17. Page 17 of 46SONG4, Charleston, Sep. 16, 2011 RLS Trade-off Curve

18. Page 18 of 46SONG4, Charleston, Sep. 16, 2011 RLS Trade-off Curve - Continued

19. Page 19 of 46SONG4, Charleston, Sep. 16, 2011 More bad Things

20. Page 20 of 46SONG4, Charleston, Sep. 16, 2011 Politics Data sharing –All MDI and HMI observables freely available –Must give proper credit Instrument paper Intermediate results papers (eg. frequencies) –Must send us copies of papers But only for accounting Most people forget –This has served us extremely well! Make sure to allow/encourage the independent/untraditional researcher! –Sometimes crazy ideas do work out Think solar far side imaging

21. Page 21 of 46SONG4, Charleston, Sep. 16, 2011 Politics Group vs. individual science (non instrument) publications –We have generally gone with voluntary collaborations We have no requirement for co-authorship or review Do encourage contacting PI team Less immediate credit to PI team oBut more long term (Scherrer et al. 1995 has >1000 citations in ADS) Encourages the bold, untraditional, creative, etc. researchers This is the approach most closely aligned with the Scientific Method ™ –Forced group publications (eg. KASC) have some advantages Gets people focused so some short term advantage But likely leads to damage in medium term oPapers tend to be compromises oNo space for adequate details, so can’t tell what is really done oNo truly independent review possible Leads to fewer overall publications and thus less overall impact Against the rules of journals and various societies oForced credit to people who have contributed little or nothing to the intellectual content

22. Page 22 of 46SONG4, Charleston, Sep. 16, 2011 Politics While tedious and boring to write it is essential to provide good documentation –Instrument paper –Calibration procedures –Analysis procedures –Well organized website Easy data access Documentation on calibration and analysis List of important events Known problems –Permanent data repository Good metadata. Use standard, if possible PR and Public Outreach is important –Somebody paid for this and want something in return –Improves funding –It is the right thing to do

23. Page 23 of 46SONG4, Charleston, Sep. 16, 2011 Conclusion The future looks bright for asteroseismology! Much work to do Watch systematics! Think carefully about the political issues Do as I say, not as I do!