Threshold Clustering Functions and Thermal Inhomogeneities in the Lyman-Alpha Forest
arXiv:1007.3734 · doi:10.1088/0004-637X/734/1/21
Abstract
We introduce to astrophysics the threshold probability functions S_2, C_2, and D_2 first derived by \citet{torq+88}, which effectively samples the flux probability distribution (PDF) of the Lya forest at different spatial scales. These statistics are tested on mock Lya forest spectra based on various toy models for HeII reionization, with homogeneous models with various temperature-density relations as well as models with temperature inhomogeneities. These mock samples have systematics and noise added to simulate the latest Sloan Digital Sky Survey Data Release 7 (SDSS DR7) data. We find that the flux PDF from SDSS DR7 can be used to constrain the temperature-density relation (where ) of the intergalactic medium (IGM) at z=2.5 to a precision of at confidence. The flux PDF is degenerate to temperature inhomogeneities in the IGM arising from HeII reionization, but we find S_2 can detect these inhomogeneities at , with the assumption that the flux continuum of the Lya forest can be determined to 9% accuracy, approximately the error from current fitting methods. If the quasar continuum can be determined to 3% accuracy, then S_2 is capable of constraining the characteristic scale of temperature inhomogeneities, with differentiation between toy models with hot bubble radii of 50 Mpc/h and 25 Mpc/h, comoving.
13 pages, 10 figures; Accepted by ApJ
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- Mean-flux Regulated PCA Continuum Fitting of SDSS Lyman-alpha Forest Spectra
- Improved reconstructions of random media using dilation and erosion processes
- The Signatures of Large-scale Temperature and Intensity Fluctuations in the Lyman-alpha Forest
- Neutrino Signatures on the High Transmission Regions of the Lyman-alpha Forest
- The impact of temperature fluctuations on the large-scale clustering of the Ly forest
- Constraining the Fluctuating Gunn-Peterson Approximation Using Lyman- Forest Tomography at
- Detecting preheating in proto-clusters with Lyman- Forest Tomography