We decompose the Lyman-α (Lyα) forest of an extensive sample of 75 high signal-to-noise ratio and high-resolution quasar spectra into a collection of Voigt profiles. Absorbers located near caustics in the peculiar velocity field have the smallest Doppler parameters, resulting in a low-b cutoff in the b-N HI distribution set primarily by the thermal state of intergalactic medium (IGM). We fit this cutoff as a function of redshift over the range 2.0 ≤ z ≤ 3.4, which allows us to measure the evolution of the IGM temperature-density (T = T 0 (ρ/ρ 0 ) γ−1 ) relation parameters T 0 and γ. We calibrate our measurements against mock Lyα forest data, generated using 26 hydrodynamic simulations with different thermal histories from the THERMAL suite, also encompassing different values of the IGM pressure smoothing scale. We adopt a forward-modeling approach and self-consistently apply the same algorithms to both data and simulations, propagating both statistical and modeling uncertainties via Monte Carlo. The redshift evolution of T 0 (γ) shows a suggestive peak (dip) at z = 2.9 (z = 3). Our measured evolution of T 0 and γ are generally in good agreement with previous determinations in the literature. Both the peak in the evolution of T 0 at z = 2.8, as well as the high temperatures T 0 15000 − 20000 K that we observe at 2.4 < z < 3.4, strongly suggest that a significant episode of heating occurred after the end of H I reionization, which was most likely the cosmic reionization of He II.
We present a new measurement of the Lyα forest power spectrum at 1.8 < z < 3.4 using 74 Keck/HIRES and VLT/UVES high-resolution, high-S/N quasar spectra. We developed a custom pipeline to measure the power spectrum and its uncertainty, which fully accounts for finite resolution and noise, and corrects for the bias induced by masking missing data, DLAs, and metal absorption lines. Our measurement results in unprecedented precision on the small-scale modes k > 0.02 s km −1 , unaccessible to previous SDSS/BOSS analyses. It is well known that these high-k modes are highly sensitive to the thermal state of the intergalactic medium, however contamination by narrow metal lines is a significant concern. We quantify the effect of metals on the small-scale power, and find a modest effect on modes with k < 0.1 s km −1 . As a result, by masking metals and restricting to k < 0.1 s km −1 their impact is completely mitigated. We present an end-to-end Bayesian forward modeling framework whereby mock spectra with the same noise, resolution, and masking as our data are generated from Lyα forest simulations. These mocks are used to build a custom emulator, enabling us to interpolate between a sparse grid of models and perform MCMC fits. Our results agree well with BOSS on scales k < 0.02 s km −1 where the measurements overlap. The combination of BOSS' percent level low-k precision with our 5 − 15% high-k measurements, results in a powerful new dataset for precisely constraining the thermal history of the intergalactic medium, cosmological parameters, and the nature of dark matter. The power spectra and their covariance matrices are provided as electronic tables.
We present a new method for determining the thermal state of the intergalactic medium based on Voigt profile decomposition of the Lyα forest. The distribution of Doppler parameter and column density (b-N HI distribution) is sensitive to the temperature density relation T = T 0 (ρ/ρ 0 ) γ−1 , and previous work has inferred T 0 and γ by fitting its low-b cutoff. This approach discards the majority of available data, and is susceptible to systematics related to cutoff determination. We present a method that exploits all information encoded in the b-N HI distribution by modeling its entire shape. We apply kernel density estimation to discrete absorption lines to generate model probability density functions, then use principal component decomposition to create an emulator which can be evaluated anywhere in thermal parameter space. We introduce a Bayesian likelihood based on these models enabling parameter inference via Markov chain Monte Carlo. The method's robustness is tested by applying it to a large grid of thermal history simulations. By conducting 160 mock measurements we establish that our approach delivers unbiased estimates and valid uncertainties for a 2D (T 0 , γ) measurement. Furthermore, we conduct a pilot study applying this methodology to real observational data at z = 2. Using 200 absorbers, equivalent in pathlength to a single Lyα forest spectrum, we measure log T 0 = 4.092 +0.050 −0.055 and γ = 1.49 +0.073 −0.074 in excellent agreement with cutoff fitting determinations using the same data. Our method is far more sensitive than cutoff fitting, enabling measurements of log T 0 and γ with precision on log T 0 (γ) nearly two (three) times higher for current dataset sizes.
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