Forward Modeling IGRINS Spectra with Starfish

My vision for leveraging modern statistical and computational tools to analyze high resolution near infrared stellar spectroscopy from IGRINS.

Transcript

1. Forward  modeling  IGRINS   spectra  with     Starfish Michael  Gully-­‐San/ago   Kavli  Ins/tute  for  Astronomy  &  Astrophysics   IGRINS  Data  Workshop  and  Science  Mee7ng   Seoul  Na7onal  University,  Korea;  November  9-­‐14,  2015 Created by Muneer A.Safiah from the Noun Project Created by OliM from the Noun Project 0.36 0.32 0.28 0.24 [Fe/H] 6280 6320 6360 6400 Te↵ [K] 4.80 4.95 5.10 5.25 v sin i [km s 1] 0.35 0.30 0.25 [Fe/H] 4.80 4.95 5.10 5.25 v sin i [km s 1]
2. the problem: The  high  spectral  grasp  of  IGRINS  is  both  a  compe//ve  advantage  and  an   analysis  challenge:     How  do  we  use  all  the  informa-on  content  in  an  IGRINS  spectrum?
3. goal: Compare  IGRINS  spectra  of  young  stars  to  pre-­‐computed  stellar  model  grids,   to  derive  accurate  fundamental  stellar  proper7es.
4. why? Models  provide  a  physical  basis  for  the  interpreta7on  of  spectra.       ...besides,   Empirical  stellar  atlases  in  the  near-­‐IR  with  high  spectral  grasp  do  not  exist  yet
5. How? A  new  extensible  framework  for  spectroscopic  inference  that  uses     all the data.
6. Starfish is  a  tool  for  spectral  inference. Likelihood Function intrinsic stellar parameters flexible polynomials multiply model to adjust flux calibration data global and local kernels identify and downweight residuals in noise matrix + = Emulator reconstruction of model spectrum covariance matrix describing probability of spectra composite covariance matrix is sum of emulator and noise matrices model [Appendix A] extrinsic stellar parameters delivers [Section 2.2] [Section 2.3] [Section 2.3.1 & 2.3.2] [Section 2.3.3] [Section 2.2] [Section 2.1] P ✓? w C ⌅ M D ✓ext 0.6 1.2 1.8 2.4 data model 5140 5150 5160 5170 5180 5190 5200 [˚ A] 0.5 0.0 0.5 residuals f ⇥ 10 13 [erg cm 2 s 1 ˚ A 1 ] Czekala  et  al.  2015   hHp:/ /arxiv.org/abs/1505.01850  
7. One of the main virtues of Starfish is its ability to automatically identify and downweight spectral line outliers Czekala  et  al.  2015   hHp:/ /arxiv.org/abs/1505.01850  
8. Read the paper
9. Current  progress  of  Starfish  on  IGRINS I  reproduced  results  from  Czekala  et  al.  2015  on  the  high  resolu7on  op7cal   spectrum  of  WASP-­‐14.
10. Current  progress  of  Starfish  on  IGRINS I  have  iden7fied  and  corrected  minor  bugs  in  the  code,  and  submiWed  them  as  Pull   Requests  on  GitHub.
11. Current  progress  of  Starfish  on  IGRINS I  ran  Starfish  on  an  IGRINS  spectrum  of  GJ876,  a  known  planet  host  star.   The  result  was  poor  because  of  telluric  absorp/on  present  in  the  spectrum,  but   unaccounted  for  in  our  model. Residual
12. Current  progress  of  Starfish  on  IGRINS We  have  acquired  10000  SUs  of  supercompu7ng  7me  on  Maverick  at  the  Texas   Advanced  Compu7ng  Center.
13. The  path  forward  for  Starfish  on  IGRINS 1. Fix  the  telluric  absorp7on  in  the  atmosphere  (???????)   2. Coarsely  flaWen  spectra  in  the  pipeline  (Jae-­‐Joon?)   3. Examine  many  spectra  from  the  IGRINS  stellar  atlas  project   (Jeong-­‐Eun  Lee,  Sunkyung  Park,  Gully)   4. Examine  weak-­‐lined  T-­‐Tauri  Stars  for  veiling  (Gully,  Kidder?)   5. Recover  known  spectroscopic  binaries  (Gully,  Gullikson?)   6. Examine  weak-­‐lined  T-­‐Tauri  Stars  for  starspots  (Gully,  Herczeg)   7. Examine  Class  I  spectra  for  photospheric  lines.  (Gully,  KASI+)   8. Catalog  and  interpret  spectral  line  outliers.  (Gully,  Deen?,  Jaffe?,   Sneden?)   9. Improve  Starfish  local  covariance  kernels  (Gully,  Czekala?)   10.Compare  APOGEE,  IGRINS,  iSHELL  +  Starfish  (Gully,  iSHELL?)
14. Longer  7me-­‐horizon  projects 11.Semi-­‐empirical  model  grids  from  spectral  outliers   12.Fold  in  many  more  calibra7on  parameters:  infer  blaze  func7on,   telluric   13.Line-­‐by-­‐line  Starfish  to  get  log g 14.Line-­‐by-­‐line  Starfish  to  get  individual  elemental  abundances   15.Coordinate  with  modelers  on  new  line  iden7fica7on,  log gf's   16.Accre7on  models?   17.Cool  stars  and  Brown  dwarfs:  BT  SeWl  models
15. P ✓? w C ⌅ M D ✓ext How  will  we  actually  accomplish  all  of  this?   It  will  be  easy  to  model  physical  phenomena  that  are  linear  superpositions.   e.g.     Star  1  +  Star  2  =  net  spectrum  (binaries)   Star  1  +  Star  2  =  net  spectrum  (starspots)   Star  1  +  disk  =  net  spectrum  (veiling)   Star  1  +  accretion  =  net  spectrum  (but  what  is  the  accretion  spectrum?)   Existing  model Revised  model θ∗2 w 2 P ✓? w C ⌅ M D ✓ext
16. P ✓? w C ⌅ M D ✓ext It  will  be  hard  to  model  physical  phenomena  that  effect  the  emergent  photosphere   e.g.     Star  1  w/  Magnetic  fields  =  net  spectrum    ???   Existing  model Revised  model? P ✓? w C ⌅ M D ✓ext
17. Questions
18. Starfish: Normalization “  We  discussed  the  (frequently  arising)  point  that  the  spectra  have  bad  continuum   normalization  (or,  equivalently,  bad  calibration)  and  so  it  is  hard  to  compare  the   models  to  the  data  at  the  precision  of  the  data.     This  problem  is  not  easily  solved;  many  investigators  "do  the  same  thing"  to  the  data   and  the  models  to  match  the  continuum  normalizations.  However,  these  continuum   procedures  are  usually  signal-­‐to-­‐noise-­‐dependendent;  models  are  rarely  at  the  same   signal-­‐to-­‐noise  as  the  data!     Anyway,  we  proposed  a  simple  plan  […]  We  will  instantiate  many  nuisance   parameters  (to  cover  calibration  issues),  infer  them  simultaneously,  and  marginalize   them  out.”   -­‐  D.  Hogg,  Hogg  Blog  2015/04/22
19. Marley  &  Robinson  ARA&A  2015 H08-Marley ARI 16 July 2015 12:23 Table 3 Ultracool modeling schools School Key characteristics (chemistry; cloud; opacity) Selected papers Barman True chem. eq.; defined clouds; sampling Barman et al. 2011 Burrows True chem. eq.; defined cloud; sampling Burrows et al. 2002, Currie et al. 2011 Marley/Saumon Rainout eq.; eddyseda; correlated-k Saumon & Marley 2008, Stephens et al. 2009 PHOENIX True chem. eq.; various cloudsb; sampling Witte et al. 2011 Tokyo True chem. eq.; UCMc; band model Sorahana & Yamamura 2012, Tsuji 2005 aEddy-sedimentation, a cloud physics model (Ackerman & Marley 2001). bVarious cloud physics models, including DUSTY (Allard et al. 2001) and DRIFT (Witte et al. 2011). cThe Unified Cloud Model, a defined cloud model (Tsuji 2002). data. Because we know from the comparisons with the cloudless T dwarfs that the atmospheric I  am  using  the  PHOENIX  model  grid.   Model  inter-­‐comparisons  are  a  likely  avenue  for  future  work.
20. PHOENIX  model  grid Husser  et  al.  2013 A&A 553, A6 (2013) stars with has been s of NLTE 000). The have been extra-solar prehensive ed for use following PHOENIX he current of state as is allowed lly of cool A full dis- ublication cal geom- sequence and reso- g existing Table 1. Parameter space of the grid. Variable Range Step size Teff [K] 2300–7000 100 7000–12 000 200 log g 0.0–+6.0 0.5 [Fe/H] −4.0−−2.0 1.0 –2.0–+1.0 0.5 [α/Fe] –0.2–+1.2 0.2 Notes. Alpha element abundances [α/Fe] 0 are only available for 3500 K ≤ Teff ≤ 8000 K and −3 ≤ [Fe/H] ≤ 0. Table 2. Sampling of the spectra in the grid. Range [Å] Sampling 500–3000 ∆λ = 0.1Å 3000–25 000 R ≈ 500 000 25 000–55 000 R ≈ 100 000
21. 73  effective  temperature  values
22. 73  effective  temperature  values,   13  surface  gravity  values
23. 73  effective  temperature  values,   13  surface  gravity  values,   9  metallicity  values
24. [α/Fe]  =  0 73  effective  temperature  values,   13  surface  gravity  values,   9  metallicity  values,   8  alpha-­‐enchancement  values  (for  a  restricted  volume)
25. !  8541  synthetic  spectra  with  [α/Fe]  =  0 73  effective  temperature  values,   13  surface  gravity  values,   9  metallicity  values,   8  alpha-­‐enchancement  values  (for  a  restricted  volume) 22386  synthetic  spectra  with  [α/Fe]  ≠  0    ➔ 30927 synthetic spectra
26. Tools  for  comparing  spectra  to  models. • MOOG  (Sneden  1973)   • SPC  (Buchave  et  al.  2012)   • SME  (ValenE  &  Piskunov  1996)   • Forward  model/χ2  (CoHaar  et  al.  2014)   • Starfish  (Czekala  et  al.  2015)
27. Created by useiconic.com from the Noun Project SUPPLEMENTARY INFORMATION RESEARCH  Teff 0.75 0.80 0.85 0.90 0.95 1.00 CCF median (+- 0.05) 0 20 40 60 80 100 120 140 Error in Teff (K) log(g) 0.75 0.80 0.85 0.90 0.95 1.00 CCF median (+- 0.05) 0.00 0.05 0.10 0.15 0.20 0.25 Error in log(g) [m/H] 0.75 0.80 0.85 0.90 0.95 1.00 CCF median (+- 0.05) 0.00 0.02 0.04 0.06 0.08 0.10 0.12 Error in [m/H] vsin(i) 0.75 0.80 0.85 0.90 0.95 1.00 CCF median (+- 0.05) 0.0 0.2 0.4 0.6 0.8 1.0 Error in vsin(i) (km/s) Supplementary Figure 1. Internal error estimates for SPC as a function of normalized cross correlation peak height (CC for effective temperature, surface gravity, metallicity and rotational velocity. The uncertainties are estimated following 29, Section 6, by determining empirical uncertainty estimates for targets with multiple observations. Each point in the diagram is the 1 σ uncertainty of the parameter for a subset selected by using a moving average centered around the me • SPC is cross correlation. Teff log(g) Buchhave  et  al.  2012 see  also  K.  Gullikson  PhD  thesis  in  prep
28. The Astrophysical Journal, 794:125 (18pp), 2014 October 20 Cottaar et al. Figure 3. Sample fits of young stars in IC 348 with from top to bottom 2MASS J03442398+3211000 (∼6000 K), 2MASS J03443916+3209182 (∼4500 K), 2MASS J03445096+3216093 (∼3500 K), and 2MASS J03425395+3219279 (∼2900 K). The blue lines show one of the observed spectra for these stars and the red lines the best-fit model spectrum to each observed spectrum. Although high S/N spectra were selected as our example, the S/N clearly increases toward the spectra of fainter CoHaar  et  al.  2014 Forward  Model/χ2  -­‐  APOGEE  Spectrograph   (IN-­‐SYNC)  INfrared  Spectra  of  Young  Nebulous  Clusters -­‐Uses  BT  SeWl  models,  solves  for  5  parameters.   -­‐8859  spectra  of  3493  stars  at  R~22500
29. Advantages  of  pre-­‐computed  models • Absolute  physical  basis   • Lots  of  physics  included   • Large  spectral  grasp  (encompasses  IGRINS)   • Low  computa7onal  cost  to  the  end  user   • Standardized:  easily  reproducible/shareable
30. Disadvantages  of  pre-­‐computed  models • Inflexible  in  which  physics  is  included   • Incomplete  line  lists   • Erroneous  oscillator  strengths   • Coarsely  sampled  parameters
31. noun  project  credits analy7cs  by  Syafiqa  Fickle  from  the  Noun  Project   Graph  by  Crea7ve  Stall  from  the  Noun  Project   Error  Bars  by  Severino  Ribecca  from  the  Noun  Project   Nega7ve  Regression  Graph  by  Aenne  Brielmann  from  the  Noun  Project   trend  by  OliM  from  the  Noun  Project   grid  by  useiconic.com  from  the  Noun  Project   grid  by  Ates  Evren  Aydinel  from  the  Noun  Project   ScaWer  Plot  by  Severino  Ribecca  from  the  Noun  Project   grid  by  Ates  Evren  Aydinel  from  the  Noun  Project   bubble  chart  by  Severino  Ribecca  from  the  Noun  Project