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]
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?
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
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
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
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
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?)
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
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
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
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
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.
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
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
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
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
Disadvantages of pre-‐computed models • Inflexible in which physics is included • Incomplete line lists • Erroneous oscillator strengths • Coarsely sampled parameters
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
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