A New Precision Measurement of the Small-Scale Line-of-Sight Power Spectrum of the Ly$\alpha$ Forest

Abstract

We present a new measurement of the Ly$\alpha$ 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 $k>0.02\mathrm{s}\mathrm{k}{\mathrm{m}}^{-1}$, 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 $k<0.1\mathrm{s}\mathrm{k}{\mathrm{m}}^{-1}$. As a result, by masking metals and restricting to $k<0.1\mathrm{s}\mathrm{k}{\mathrm{m}}^{-1}$ 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 Ly$\alpha$ 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 $k<0.02\mathrm{s}\mathrm{k}{\mathrm{m}}^{-1}$ 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.

24 pages, 12 figures, accepted for publication in ApJ, machine readable tables will be made available after publication in the journal

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