1. Simona Gallerani Constraining reionization through quasar and gamma ray burst absorption spectra In collaboration with: T. Roy Choudhury, P. Dayal, X. Fan, A. Ferrara, A. Maselli, R. Salvaterra COSMOLOGICAL REIONIZATION CONFERENCE Harish-Chandra Research Institute, Allahabad, 16 February 2010 Astronomical Observatory of Rome

2. DAVID The Dark Ages VIrtual Department http://wiki.arcetri.astro.it/bin/view/DAVID/WebHome S. Bianchi INAF/Arcetri B. Ciardi MPA P. Dayal SISSA C. Evoli SISSA A. Ferrara SNS Pisa S. Gallerani INAF/Roma F. Iocco IAP F. Kitaura SNS Pisa A.Maselli INAF/Arcetri R. Salvaterra INAF/Milano S. Salvadori KAI Groningen R. Schneider INAF/Arcetri M. Valdes IPMU R. Valiante Univ. Firenze

3. QSOs constraints on cosmic reionization SDSS +CFHQS ~40 QSOs @ 5.7<z<6.4 Fan et al. (2005) in contrast with WMAP Komatsu et al. (2009 / 2010) Becker et al. (2003)

4. Modeling reionization Choudhury & Ferrara (2005/2006) Free parameters: Log-Normal modelQSOs, PopII, PopIII

5. Reionization models EARLY REIONIZATION (ERM)LATE REIONIZATION (LRM) Volume Filling Factor Photo- Ionization Rate ERM LRM Data from McDonald & Miralda-Escude’(2001); Bolton etal. (2005/2007); Fan etal.(2006) Highly ionized IGM at z=6Two-phase IGM at z >6

6. Statistics of the transmitted flux Data from Fan etal. (2002); Songaila (2004); Fan etal.(2006) ERM LRM Fan et al. (2006) Songaila (2004)

7. Gaps in the Lyα forest ERM LRM GAPS SG, Choudhury, Ferrara (2006) Largest gap width distribution

8. Comparison with 20 QSOs at 5.7 < z < 6.4 (Fan et al. 2006) ERM LRM SG, Ferrara, Fan, Choudhury 2008

9. Largest gap width distribution ERM LRM LR SG, Ferrara, Fan, Choudhury (2008) @ Comparison with 20 QSOs at 5.7 < z < 6.4 (Fan et al. 2006)

10. Transverse proximity effect Proximity effect along the line of sight Transverse proximity effect Gunn-Peterson through foreground QSO background QSO

11. First-ever detection of the Transverse Proximity Effect in the HI Lyα forest Mahabal et al. (2005) Fan et al. (2006) QSO1 QSO2 RD J1148+5252 Mpc Peak Spectral Density See also Worseck et al. 2007 TPE SG, Ferrara, Fan, Choudhury (2008)

12. Observed absorption spectrum of GRB050904 @ z=6.3 Kawai et al. (2006) 52 Å

13. Observed absorption spectrum of GRB050904 @ z=6.3 Kawai et al. (2006) 142 Å

14. Observed absorption spectrum of GRB050904 @ z=6.3 Kawai et al. (2006) 190 Å DLA Totani et al. (2006)

15. Largest gap probability isocontours: GRBs SG, Salvaterra, Ferrara, Choudhury (2008) The ERM is 10 times more probable wrt the LRM The gap sizes are consistent with x HI ~10 -4. 5% 10% 40% 5% 10% 40% In agreement with Totani et al. (2006)

16. Conclusions: An Early Reionization Model First-ever detection of the transverse proximity effect in the HI Lyα forest along the line of sight towards the highest–z QSO known.  The analysis of the GRB050904 at z=6.3 confirms the results found in QSO studies. In particular, the gap size along the observed line of sight is consistent with x HI ~10 -4.  Current observational data of QSO absorption spectra do not require any sudden change in the IGM ionization state @ z~6, instead favour a highly ionized IGM at these epochs.  Further applications of the Early Reionization Model: Quasar HII regions  see Maselli’s talk (in the afternoon) Lyα emitters luminosity function  see Dayal’s talk (tomorrow)  The overall result points towards an extended reionization process which starts at z>=11 and completes at z>=7, in agreement with WMAP data.

17. Transverse proximity effect: observations vs simulations Peak Spectral Density

18. PEAKS Conclusions Transverse proximity effect in the LOS towards the highest –z QSO.  Observed peaks are much larger than simulated ones   Lower limit on the foreground QSO lifetime

19. Log-Normal model: observational confirmation

20. (Becker et al. 2006) Miralda-Escude’ et al (2000)

21. Log-Normal model vs MHR00 at z=6 Miralda-Escude’ et al (2000)

22. Log-Normal model vs MHR00 Miralda-Escude’ et al (2000)

23. Gap width distribution SG, Choudhury, Ferrara (2006)

24. LARGEST Gap width distribution SG, Choudhury, Ferrara (2006)

25. Gap width distribution: LogNormal vs HYDROPM simulations SG, Choudhury, Ferrara (2006)

26. Modelling a late reionization scenario LRM random distribution of neutral regions LRMc clustering of neutral pixels redshift

27. Largest dark gap distribution Gallerani S., Choudhury T., Ferrara A. (2006)

28. 2D Maps of neutral hydrogen distribution (Ciardi, Ferrara & White 2003) Should the distribution of neutral regions depend on the clustering of ionizing sources? Clustering of ionizing sources might not be correlated significantly with neutral regions in the case of a very high filling factor. Ionizing sources Left over by reionization

29. Transmissivity windows from HII regions PEAK FREQUENCY & SIZE MASS OF DM HALOS HOSTING THE IONIZING SOURCES

30. Size Frequency Hints on the mass of DM halos hosting high-z QSOs Discrepancy It is unlik  ely that QSOs HII regions produce peaks consistent with data, unless they reside in highly overdense regions. Mo & White (2002)

31. Observations 5.99 J1306-0356 5.95 5.93 J1335+3533 J1411+1217 5.85 J0005-0006 5.82 J0836+0054 5.80 5.79 5.74 J0002-2550 J0927+2001 J1044-0125 6.42 J1148+5251 6.28 J1030+0524 6.25 J1623+3112 6.20 J1048+4637 6.13 6.07 J1250+3130 J1602+4228 6.01 6.00 J1137+3549 J0818+1722 Fan et al. (2006) Low Redshift (LR) High Redshift (HR)

32. Dark gaps statistics Dark gaps: “contiguous regions of the spectrum with  > 2.5 over rest frame wavelength intervals greater then 1Å”. Data from Songaila & Cowie (2002) Simulated spectra Observed spectra GAP

33. Transverse proximity effect: observations Mahabal et al. (2005) Fan et al. (2006) QSO1 QSO2 RD J1148+5252 Mpc

34. Transverse proximity effect: observations Mahabal et al. (2005) Fan et al. (2006) QSO1 QSO2 RD J1148+5252 Mpc Yu (2005) Shapiro et al. (2006) White et al. (2003) Wyithe et al. (2005)

35. Transverse proximity effect: simulations HII Regions (case B) Underdense Regions (case A) Peaks origin: SG, Ferrara, Fan, Choudhury (2007) Peak Spectral Density

36. Transverse proximity effect: observations vs simulations