TL;DR
This paper introduces a new catalogue of Damped Lyman-alpha absorbers from SDSS DR16Q using Gaussian process models, enabling unbiased statistical analysis of DLA populations even in noisy data.
Contribution
The study develops an improved Gaussian process model for DLA detection and statistical estimation, accounting for uncertainties and biases in SDSS spectra.
Findings
Measured the column density distribution function for $2 < z < 5$
Estimated the line density and neutral hydrogen density consistent with previous studies
Demonstrated the model's effectiveness even in the Lyman-$\beta$ forest region
Abstract
We present a new catalogue of Damped Lyman- absorbers from SDSS DR16Q, as well as new estimates of their statistical properties. Our estimates are computed with the Gaussian process models presented in Garnett et al. (2017); Ho et al. (2020) with an improved model for marginalising uncertainty in the mean optical depth of each quasar. We compute the column density distribution function (CDDF) at , the line density (), and the neutral hydrogen density (). Our Gaussian process model provides a posterior probability distribution of the number of DLAs per spectrum, thus allowing unbiased probabilistic predictions of the statistics of DLA populations even with the noisiest data. We measure a non-zero column density distribution function for with confidence…
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