1. Lighting up the light-shedding of illuminated enlightenment of bright light
– or –
Modeling Lya spectra
Peter Laursen,
with Jens-Kristian Krogager & Johan Fynbo
| Niels Bohr Institutet | Københavns Universitet

2. QSO
HST/UVIS with the F606W filter

3. QSO
VLT/X-Shooter (UVB arm)

4. QSO
VLT/X-Shooter (UVB arm)

5. Modeling Lya lines ⇒ …has been done before
Verhamme et al. (2008) with MCLYA

6. The model
MoCaLaTA

7. Input parameters MoCaLaTA Geometry: • Radius • Number of clouds
• Cloud size distribution
MoCaLaTA
rgal
Ncl
rcl,min; rcl,max; β
State of the clouds and the intercloud medium:
• Neutral hydrogen density
• Temperature
• Dust density ⇐ metallicity
nHI,cl; nHI,ICM
THI,cl; THI,ICM
ZHI,cl; ZHI,ICM
Kinematics:
• Cloud velocity dispersion
• Outflow velocity
sV,cl
Vout
Emission:
• Intrinsic line width
• Emission scale length
• Emission site/cloud correlation
• Systemic redshift
sline
Hem
Pcl z

8. Input parameters rgal Ncl rcl,min; rcl,max; β 10 kpc Kim+ 03 (LMC)
∼105
Dickey & Garwood 89; Williams & McKee 97
10–100 pc;
–1.6
nHI,cl; nHI,ICM
THI,cl; THI,ICM
ZHI,cl; ZHI,ICM
sV,cl
Vout
Smaller cloud sizes probably require further cooling, while larger clouds will get torn apart by large-scale motion, at least in rotation-dominated objects (Newton 1980).
sline
Hem
Pcl z

9. Input parameters rgal Ncl rcl,min; rcl,max; β 10 kpc
0.2–0.5 cm-3 from e.g. Carilli+ 98; Ferrière 01; Gloeckler & Geiss 04 (MW)
∼105
10–100 pc;
ntot = 10–3–10–2 cm-3 (Dopita & Sutherland 03; Ferrière 01),
xHI,ICM ∼ 10–8–10–5 (House 64; Sutherland & Dopita 93),
plus residual diffuse HI clouds.
10–10–10–5 cm–3
–1.6
nHI,cl; nHI,ICM
THI,cl; THI,ICM
ZHI,cl; ZHI,ICM
0.3 cm–3;
104 K;
106 K
e.g. Brinks+ 00; Tüllmann+ 06,08
0.31 Z
From Zn, Si, and Fe abs. lines, as well as from
[OII]/[OIII] and [NII]/Ha (R23 and N2 methods)
sV,cl
Vout
sline
Hem
Pcl z

10. Input parameters rgal Ncl rcl,min; rcl,max; β 10 kpc ∼105 10–100 pc;
–1.6
nHI,cl; nHI,ICM
THI,cl; THI,ICM
ZHI,cl; ZHI,ICM
0.3 cm–3;
10–10–10–5 cm–3
104 K;
106 K
0.31 Z
sV,cl
Vout
115±18 km s–1
From abs. line widths
100–200 km s–1
sline
Hem
Pcl z

11. Input parameters rgal Ncl rcl,min; rcl,max; β 10 kpc ∼105 10–100 pc;
–1.6
nHI,cl; nHI,ICM
THI,cl; THI,ICM
ZHI,cl; ZHI,ICM
0.3 cm–3;
10–10–10–5 cm–3
104 K;
106 K
0.31 Z
2.1 kpc
From HST imaging (reff = 1.09 kpc)
sV,cl
Vout
115±18 km s–1
100–200 km s–1
sline
Hem
Pcl z
130 km s–1
From [OII], [OIII], Ha, and Hb
n/a
0.2–0.5
2.35
From [OII], [OIII], Ha, and Hb

12. Input parameters rgal Ncl rcl,min; rcl,max; β 10 kpc
Set by observations
Standard values
Fitted for
∼105
10–100 pc;
–1.6
nHI,cl; nHI,ICM
THI,cl; THI,ICM
ZHI,cl; ZHI,ICM
0.3 cm–3;
10–10–10–5 cm–3
104 K;
106 K
0.31 Z
sV,cl
Vout
115±18 km s–1
100–200 km s–1
sline
Hem
Pcl z
130 km s–1
2.1 kpc
n/a
2.35

13. Finding the best fit ⇒ 1. Run trial models to get a rough fit
⇒ Ncl ∼ 105; Vout ∼ 150 km s-1
2. Run grid with Ncl ∈ [104.5,105.5] and
Vout ∈ [100,200] km s-1 ⇒

14. Finding the best fit 1. Run trial models to get a rough fit
3. Fit skewed Gaußians
1. Run trial models to get a rough fit
⇒ Ncl ∼ 105; Vout ∼ 150 km s-1
b) Peak separation
2. Run grid with Ncl ∈ [104.5,105.5] and
Vout ∈ [100,200] km s-1
c) Peak height ratio
4. Measure
a) Red peak FWHM
d) Peak flux ratio

15. Finding the best fit 1. Run trial models to get a rough fit
⇒ Ncl ∼ 105; Vout ∼ 150 km s-1
2. Run grid with Ncl ∈ [104.5,105.5] and
Vout ∈ [100,200] km s-1
and those for which all four fitting
parameters fall within 1σ
6. Identify best fitting model
3. Fit skewed Gaußians
4. Measure
a) Red peak FWHM
b) Peak separation
c) Peak height ratio
d) Peak flux ratio
5. Calculate number of std. dev.s between
synthetic and observed spectra

16. Results Best-fitting models give: Vout = 160 km s-1
log(NHI/cm-2) = 20.23
Ncl = 2±1

17. Results Best-fitting models give: Vout = 160 km s-1
log(NHI/cm-2) = 20.23
Ncl = 2±1

18. Conclusion
• Fitting Lya lines requires information about several parameters.
A simple spectrum isn’t really enough.