Can the Experience From Helioseismology Help us With SONG?

1. Can the Experience From Helioseismology Help us With SONG?

2. Overview • Motivation • Observations • Data Analysis • Politics • Conclusion

3. 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. 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. 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. Modes With Error Bars Overall scale measured to one part in 51e6.

7. 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. 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. 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. 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. 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. 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 • But watch out for avoided crossings • Global fits • Fit asymptotics directly • Very poor approximation. • Substantial loss of information • Linearize around reference model • See work by Vorontsov and Jefferies • Should work well if you believe that inversions work well • Hard to test for quality of fit • Check your residuals! • Do independent analysis and encourage competition

13. 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 • Causes 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. 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. What Happens if you Don’t pay Attention

16. What Happens if you Don’t pay Attention

17. RLS Trade-off Curve

18. RLS Trade-off Curve - Continued

19. More bad Things

20. 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. 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 • But 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 • Papers tend to be compromises • No space for adequate details, so can’t tell what is really done • No truly independent review possible • Leads to fewer overall publications and thus less overall impact • Against the rules of journals and various societies • Forced credit to people who have contributed little or nothing to the intellectual content

22. 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. 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!