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QSO proximity regions in Lyα emission (and absorption) during HI Reionization

QSO proximity regions in Lyα emission (and absorption) during HI Reionization. Sebastiano Cantalupo Kavli Institute for Cosmology, Cambridge & Institute of Astronomy. Outline. The IGM around bright quasar during Reionization

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QSO proximity regions in Lyα emission (and absorption) during HI Reionization

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  1. QSO proximity regions in Lyα emission (and absorption) during HI Reionization Sebastiano Cantalupo Kavli Institute for Cosmology, Cambridge & Institute of Astronomy

  2. Outline • The IGM around bright quasar during Reionization • Mapping HI with the Lyα emission from quasar I-fronts: • basic idea • new radiative transfer model and numerical results • The thermal state of the IGM in the quasar proximity regions and the topology of Reionization in the Lyα forest.

  3. QSO proximity regions in Lyα emission? HII recombination rate is too slow to detect gas at mean density (Hogan & Weymann 1987; Baltz, Gnedin & Silk 1998). Fluorescent emission (Gould & Weinberg 1986; SC+05; SC+07) potentially maps only overdense regions. A more efficient mechanism than HII recombination to produce Lyα photons: HI collisional excitation (CE) by energetic electrons. emissivity recombinations coll. excitations • CE dominates theLyα emissivity where: • neutral fraction (xHI) is ~ 0.5 • High temperatures: T>104 K Typical conditions of QSO Ionization-Fronts SC, Porciani & Lilly 2008

  4. Mapping HI through the I-Fronts of the highest-z QSOs HII proximity- region in QSO spectrum QSO GP-trough redshift basic idea: as the I-Front cross the IGM, Lyα photons are produced within the neutral patches via collisional excitations • The Lyα emission gives a“tomography”of the neutral hydrogen at the I-Front position (jLyα~xHI2) • From the I-Front position we also get: • additionalinformation on the average neutral fraction around the QSO • constraints on theQSO ageand on the emission shape SC, Porciani & Lilly 2008

  5. OK… but how bright is this signal? Let’s start with a simple spherical model - a QSO surrounded by an uniform IGM - including continuum and LyαRT (time-dependent, temperature-dependent, Helium, etc.). see SC, Porciani & Lilly 2008

  6. Expected SB: exploring a large parameter space -1.0 -1.7 SBLyα ~ 10-20 erg/s/cm2/arcsec2for a large range in QSO properties and expected lifetimes -2.5 (for α=-1.7) Lyα SB from a fully neutral and at mean density patch of IGM crossed by the I-Front SC, Porciani & Lilly 2008

  7. Is it detectable? • ~ 3 orders of magnitude below sky-background (better for JWST)‏ • but: Line and extended emission (hundreds of arcmin2!) • Possible detection strategy: long-slit (or multi-slit) spectroscopy + integration over the slit length. • neutral patch of IGM with few arcminutes scales may be already detected from the ground with current facilities. • good redshift dependence, good for (future) z>6.5 QSOs (Pan-STARRS) • with JWST: HI tomography below arcmin scales • But, we should try first to better understand the expected signal…

  8. Towards a more realistic modeling: basic questions What is the effect of density dishomogeneities? What is the effect of other (galactic) sources? Bright QSO are rare objects and should live in overdense environment. How does this change the expected signal? To answer we need: numerical models that follows large volumes (~100Mpc) and, at the same time, resolve the Ionization-fronts (~10Kpc) SOLUTION: Adaptive Mesh Refinement

  9. “Numerical” digression: building a new adaptive RT method for AMR simulations

  10. Propagating rays in a grid: “classical” Monte Carlo RT method Monte Carlo (MC) sampling of the solid angle (uniform rays or Healpix). Photons deposited while rays propagate through the grid. Algorithm scales with angular resolution (not optimal with multi-meshes; but see also: adaptive ray-splitting). Convergence depends on few cells (within the I-front), but all the cells have to be sampled (solid-angle constraint).

  11. Propagating rays in a grid: “StART” • Based on a new algorithm that tells you: • solid angle of each cell as seen by any point • how to draw rays with correct solid angle distribution within each cell Cell-by-Cell approach (or cell-by-cell MC) We are now free to choose the number of rays (depositing photons) per cell (e.g., set by convergence). Adaptive to the physical/grid properties: efforts concentrate where needed (i.e., for cells within the I-front). It scales like O(Nfront). Adaptive Mesh Refinement on the I-front. SC & Porciani 2010, TBS

  12. Tests (uniform grid): from the Code comparison project (Iliev+06): StART HI fraction StART Temperature StART Multi-sources SC & Porciani 2010, TBS

  13. Multi-grids and I-front AMR: SC & Porciani 2010, TBS

  14. StART: main features Adaptive in time and space. Cell-by-cell convergence of the radiation field. Non-equilibrium, temperature-dependent chemistry solver (variable order Radau method), including Helium. Fast conversion from Oct-tree grids or particles to patch-based AMR with clustering algorithm (Bergson & Rigoustsos). Implemented so far with: Enzo, RAMSES, AMRCharm, Gadget. I-front adaptive grid-refinement. Parallel (open-mp). 3D Lyα RT module. method paper: SC & Porciani, 2010, to be submitted

  15. We have now the “right” tool… Let’s improve our previous simple model with the help of large, high-resolution AMR hydro-simulations

  16. Simulating the proximity region of a bright QSO during the EoR • Series of 200 Mpc zoomed initial condition boxes. AMR hydro-runs performed with RAMSES. • Selected halo: 5x1012 M (z=6.5) • High resolution region of 100 Mpc, 5123 base grid + 5 levels of refinement (effective physical resolution of ~800pc). SC 2010, in prep

  17. Radiative Transfer Runs: before the QSO turns on • Performed on an extracted region of size 50 Mpc. • 2563 base grid + 5 levels of refinement (effective physical resolution: ~800pc). • + 3 additional levels on I-fronts • ~104 sources (>109 M), PopII spectrum + QSO. • Including Helium, 70 frequency bins (logarithmically spaced) from 13.6eV to 1keV. • Including finite light-speed.

  18. Radiative Transfer Runs: QSO on

  19. A look inside a QSO bubble

  20. Lyα RT results: Narrow Band Image 2D-spectrum arcmin arcmin Δλ (Å) Δλ (Å) arcmin SC 2010, in prep

  21. The temperature evolution within the proximity region: a thin slice

  22. The temperature within the proximity region: phase diagram • As expected (see e.g., Tittley & Meiksin07): bimodal temperature distribution within the qso proximity region: • - Hot regions: H+HeI+HeII ionized by the QSO • - Warm regions: H+HeI ionized by local sources. HeII ionized by the QSO • For a given spectrum, overall temperatures are mostly determined by cooling processes within the I-front. tQSO = 0 Myr tQSO = 2 Myr mostly ionized part. ionized mostly neutral tQSO = 5 Myr tQSO = 10 Myr SC 2010, in prep

  23. “Fossil evidences” of the Reionization process in the Lyα forest? Just a little example:

  24. Summary: • QSO proximity regions in Lyα emission: •  The Lyα emission from the QSO I-front expanding in a mostly neutral IGM can provide a direct “tomography” of the cosmic HI. • The expected signal (calculated with our new Adaptive Radiative Transfer, StART) should be already detectable now with current facilities if neutral IGM patches extend over few arcminutes scales. • QSO proximity regions in Lyα absorption: • The IGM temperature distribution in the proximity region can provide a “fossil record” of the Reionization process (topology and epoch). E.g., if QSO turned on before HI Reionization is completed -> bimodal distribution expected. • Traceable with the Lyα forest?

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