quan fying the diffuse con nuum contribu on from blr gas
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Quan%fying the diffuse con%nuum contribu%on from BLR gas: a - PowerPoint PPT Presentation

Quan%fying the diffuse con%nuum contribu%on from BLR gas: a modeling approach Mike Goad, Daniel Lawther, Kirk Korista, Otho Ulrich, Marianne Vestergaard Mike Goad Atlanta , USA 2017 Our approach: q Build a model BLR match the intensi:es (


  1. Quan%fying the diffuse con%nuum contribu%on from BLR gas: a modeling approach Mike Goad, Daniel Lawther, Kirk Korista, Otho Ulrich, Marianne Vestergaard Mike Goad Atlanta , USA 2017

  2. Our approach: q Build a model BLR match the intensi:es ( variability 9mescale/amplitude ) of strongest UV/op%cal emission lines in NGC 5548 (***For objects of interest, no such thing as a steady-state model***) q Compute wavelength-dependent (UV-op%cal-IR) flux and variability %mescale of DC arising from the same gas q Scale delays according to the frac%onal flux contribu%on DC/(INCIDENT + DC) q Drive with model con%nuum light-curves es%mate sta%s%cally likely delays (CCF/JAVELIN) + dependence on characteris9c %mescale & variability amplitude of driving con%nuum (MC approach) Mike Goad Atlanta 2017

  3. Types of model : q Pressure law model : Rees, Netzer and Ferland 1989, Goad, O’Brien, Gondhalekar 1993 Kaspi and Netzer 1999 Netzer 2000 q Local Op1mally emi4ng Clouds : Baldwin, Ferland, Korista, Verner 1995, Korista and Goad 2000, 2001, 2004. Mike Goad Atlanta 2017

  4. (1) Pressure Law models : Lawther, Goad, Korista, Ulrich, Vestergaard 2017, in prep Run of physical condi%ons with radius specified by simple radial pressure law const Temp U ( r ) ∝ r s − 2 n H ( r ) ∝ r − s P ( r ) ∝ r − s c ∝ r 2 s/ 3 A c ( r ) ∝ R 2 Assume mass conserva%on + spherical clouds N col ( r ) ∝ R c n H ∝ r − 2 s/ 3 Z r out ✏ ( r ) A c ( r ) n c ( r ) r 2 dr L = 4 ⇡ Line luminosity r in dC ( r ) ∝ A c ( r ) n c ( r ) dr ∝ r 2 s/ 3 − 3 / 2 dr Differen%al covering frac%on Mike Goad Atlanta 2017

  5. Normaliza%on condi%on : specify Φ H , n H , N col r at some + inner and outer radius, and total covering frac%on Ctot Choose line radia%on pagern – we assume clouds are spherical ✏ ( r, ✓ ) = ✏ totl [1 − (2 F ( r ) − 1) cos ✓ ] F ( r ) = ✏ inwd / ✏ totl Mike Goad Atlanta 2017

  6. 45 Mehdipour et al. 2015 log[ λ L λ / 1 erg s − 1 ] 44 43 42 41 40 − 2 0 2 4 6 log[Energy / 1Ryd] Two cases: s=0 , constant density nh, constant column density Nh + s=2 , constant ioniza%on parameter U Mike Goad Atlanta 2017

  7. Mike Goad Atlanta 2017

  8. R=1.48 lt-days R=14.8 lt-days R=148 lt-days 180 Ly - α 160 C IV emissivity-weighted radii 140 H α 120 H β can include effects of r � (lightdays) He II 4686 100 anisotropy/responsivity He II 1640 80 tends to increase 60 delays further 40 log( n col ) = 22 . 5 20 0 7 8 9 10 11 12 13 14 log( n H / 1cm − 3 ) Mike Goad Atlanta 2017

  9. Similarly – constant U models Ly - α 10 44 C IV H β He II 4686 Broad range in ioniza%on L line (erg s − 1 ) 10 43 for which we can exceed the measured line 10 42 luminosi%es 10 41 10 40 − 2 . 0 − 1 . 5 − 1 . 0 − 0 . 5 0 . 0 0 . 5 log (U) Mike Goad Atlanta 2017

  10. 10 44 Diffuse continuum Ionizing continuum Continuum ν L ν [erg s − 1 ] Total continuum 80 CCF Peak 70 CCF Centroid 10 43 60 50 Lag (days) 40 Model 1 ( s =0 , log( n H ) = 10 . 75 ) 10 42 30 2000 4000 6000 8000 10000 Wavelength [ ˚ A] 20 1 . 0 Di ff use continuum fraction , F di ff 10 Model 1 ( s =0 , log( n H ) = 10 . 75 ) 0 . 8 0 2000 4000 6000 8000 10000 0 . 6 Wavelength [ ˚ A] 0 . 4 0 . 2 CCF Peak × F di ff Model 1 14 0 . 0 CCF Centroid × F di ff 2000 4000 6000 8000 10000 12 Wavelength[ ˚ A ] Diluted lag (days) 10 80 r � 8 70 r η τ � (anisotropic) 60 Centroid [lightdays] 6 τ η (anisotropic) 50 4 40 30 2 Model 1 20 0 10 2000 4000 6000 8000 10000 Model 1 ( s =0 , log( n H ) = 10 . 75 ) 0 Wavelength [ ˚ A] 2000 4000 6000 8000 10000 Wavelength [ ˚ A] Mike Goad Atlanta 2017

  11. 10 44 Diffuse continuum Ionizing continuum 80 Continuum ν L ν [erg s − 1 ] Total continuum CCF Peak 70 CCF Centroid 60 10 43 50 Lag (days) 40 Model 2 ( s =2 , log( U ) = − 1 . 23 ) 30 10 42 2000 4000 6000 8000 10000 20 Wavelength [ ˚ A] 10 1 . 0 Model 2 ( s =2 , log( U ) = − 1 . 23 ) Di ff use continuum fraction , F di ff 0 2000 4000 6000 8000 10000 0 . 8 Wavelength [ ˚ A] 0 . 6 0 . 4 0 . 2 Model 2 ( s =2 , log( U ) = − 1 . 23 ) 14 CCF Peak × F di ff 0 . 0 CCF Centroid × F di ff 2000 4000 6000 8000 10000 Wavelength[ ˚ 12 A ] Diluted lag (days) 10 80 r � 70 8 r η τ � (anisotropic) 60 Centroid [lightdays] 6 τ η (anisotropic) 50 4 40 30 2 Model 2 ( s =2 , log( U ) = − 1 . 23 ) 20 0 10 2000 4000 6000 8000 10000 Model 2 ( s =2 , log( U ) = − 1 . 23 ) Wavelength [ ˚ A] 0 2000 4000 6000 8000 10000 Wavelength [ ˚ A] Mike Goad Atlanta 2017

  12. Anisotropy , F = � in ( λ ) / � tot ( λ ) 0 . 9 0 . 8 Inward frac%ons 0 . 7 0 . 6 Model 1 ( s =0 , log( n H ) = 10 . 75 ) 0 . 5 2000 4000 6000 8000 10000 Wavelength[ ˚ A ] 1 . 0 Anisotropy , F = � in ( λ ) / � tot ( λ ) 0 . 9 0 . 8 0 . 7 0 . 6 0 . 5 Model 2 ( s =2 , log( U ) = − 1 . 23 ) 0 . 4 2000 4000 6000 8000 10000 Wavelength[ ˚ A ] Mike Goad Atlanta 2017

  13. 60 14 50 τ CCF , cent × F di ff . log( n H ) = 8 τ CCF , cent . [days] 12 40 10 Density 8 30 6 20 dependence - 4 10 2 0 0 constant 60 14 50 τ CCF , cent × F di ff . log( n H ) = 9 τ CCF , cent . [days] density models 12 40 10 8 30 6 20 4 10 2 0 0 60 14 50 τ CCF , cent × F di ff . log( n H ) = 10 τ CCF , cent . [days] 12 40 10 8 30 6 20 4 10 2 0 0 60 14 50 τ CCF , cent × F di ff . log( n H ) = 11 τ CCF , cent . [days] 12 40 10 8 30 6 20 4 10 2 0 0 60 14 50 τ CCF , cent × F di ff . log( n H ) = 12 τ CCF , cent . [days] 12 40 10 8 30 6 20 4 10 2 0 0 60 14 50 τ CCF , cent × F di ff . log( n H ) = 13 τ CCF , cent . [days] 12 40 10 8 30 6 20 4 10 2 0 0 60 14 50 τ CCF , cent × F di ff . log( n H ) = 14 τ CCF , cent . [days] 12 40 10 8 30 6 20 4 10 2 0 0 Mike Goad Atlanta 2017 2000 4000 6000 8000 10000 2000 4000 6000 8000 10000 Wavelength [ ˚ Wavelength [ ˚ A] A]

  14. Constant ioniza%on models 100 10 45 log( U ) = 0 . 52 80 L ν [erg s − 1 ] 10 44 τ η [days] 60 10 43 40 10 42 20 10 41 0 100 10 45 80 log( U ) = − 0 . 48 L ν [erg s − 1 ] 10 44 τ η [days] 60 10 43 40 10 42 20 10 41 0 100 10 45 log( U ) = − 1 . 48 80 L ν [erg s − 1 ] 10 44 τ η [days] 60 10 43 40 10 42 20 10 41 0 100 10 45 log( U ) = − 1 . 98 80 10 44 L ν [erg s − 1 ] τ η [days] 60 10 43 40 10 42 20 10 41 0 2000 4000 6000 8000 10000 2000 4000 6000 8000 10000 Wavelength [ ˚ Wavelength [ ˚ A] A] Mike Goad Atlanta 2017

  15. 2 . 5 2 . 5 DRW Continuum DRW Continuum C IV , � -weighted resp. Mg II , � -weighted resp. C IV , η -weighted resp. Mg II , η -weighted resp. 2 . 0 2 . 0 Relative luminosity Relative luminosity 1 . 5 1 . 5 1 . 0 1 . 0 0 . 5 0 . 5 0 . 0 0 . 0 0 100 200 300 400 500 0 100 200 300 400 500 Time [lightdays] Time [lightdays] Aside : Mike Goad Atlanta 2017

  16. 60 RAW DILUTED 14 T char = 5 50 12 τ CCF , cent . × F di ff . τ CCF , cent . [days] 40 10 8 30 6 20 4 10 2 0 0 60 14 T char = 10 50 12 τ CCF , cent . × F di ff . Dependence on Tchar τ CCF , cent . [days] 40 10 8 30 6 20 4 10 2 0 0 Tchar (days) 60 14 T char = 20 50 12 τ CCF , cent . × F di ff . τ CCF , cent . [days] 40 10 1989 ~ 80-120 8 30 6 20 4 1993 ~ 40 10 2 0 0 60 14 2014 ~ 10-20 T char = 40 50 12 τ CCF , cent . × F di ff . τ CCF , cent . [days] 40 10 8 30 6 20 4 10 2 0 0 60 14 T char = 80 50 12 τ CCF , cent . × F di ff . τ CCF , cent . [days] 40 10 8 30 6 20 4 10 2 0 0 60 14 T char = 160 50 12 τ CCF , cent . × F di ff . τ CCF , cent . [days] 40 10 8 30 6 20 4 10 2 0 0 2000 4000 6000 8000 10000 2000 4000 6000 8000 10000 Wavelength [ ˚ Wavelength [ ˚ A] A] Mike Goad Atlanta 2017

  17. (2) Local op%mally emilng clouds (LOC) models Korista and Goad 2000,2001 At any given radius there exists a range of gas densi%es/column densi%es (or simply …..more than one pressure-law!) Spectrum dominated by selec%on effects introduced by atomic physics and general radia%ve transfer within the large pool of line-emilng en%%es Strengths: Summa%on over cloud distribu%on leads to: (i) typical AGN spectrum (ii) Ioniza:on stra:fica:on (iii) Luminosity-Radius rela:on arises naturally Mike Goad Atlanta 2017

  18. Deriving the spectrum: Give each line emilng en%ty a weight in 2-dimensions: gas density and distance Assume : analy9c, separable, and a power-law in each variable g ( n H ) ∝ n β Baldwin 1997 – composite quasar spectra best fit H if indices in both are close to -1. f ( R ) ∝ R Γ β = − 1 In our models assume : and fit for Γ Korista and Goad (2000) found a value of -1.2 gives an Acceptable fit to the line luminosi%es For NGC~5548 Mike Goad Atlanta 2017

  19. Log Nh=22 Balmer Jump Mike Goad Atlanta 2017

  20. Log Nh=23 Mike Goad Atlanta 2017

  21. Log Nh=24 Mike Goad Atlanta 2017

  22. 14 Centroid Centroid Korista and Goad 2017 AGN STORM (Nh22) 10 Korista and Goad 1993 campaign 12 10 8 8 6 6 Korista and Goad 1993 campaign 4 Korista and Goad 2017 AGN STORM 4 2 2 2000 4000 6000 8000 2000 4000 6000 8000 4 4 2 2 0 0 2000 4000 6000 8000 2000 4000 6000 8000 Mike Goad Atlanta 2017

  23. Centroid 10 Korista and Goad 1993 campaign 8 6 Korista and Goad 2017 AGN STORM (Nh24) 4 2 2000 4000 6000 8000 4 2 0 2000 4000 6000 8000 Mike Goad Atlanta 2017

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