Photometric IGM tomography: Efficiently mapping quasar light echoes with deep narrow-band imaging | Monthly Notices of the Royal Astronomical Society | Oxford Academic

2022-09-03 07:21:05 By : Ms. Coco Li

Koki Kakiichi, Tobias Schmidt, Joseph Hennawi, Photometric IGM tomography: Efficiently mapping quasar light echoes with deep narrow-band imaging, Monthly Notices of the Royal Astronomical Society, Volume 516, Issue 1, October 2022, Pages 582–601, https://doi.org/10.1093/mnras/stac2026

In the standard picture, episodes of luminous quasar activity are directly related to supermassive black hole (SMBH) growth. The ionizing radiation emitted over a quasar’s lifetime alters the ionization state of the surrounding intergalactic medium (IGM), enhancing the Lyα forest transmission – so-called proximity effect – which can be observed in absorption spectra of background sources. Owing to the finite speed of light, the transverse direction of the proximity effect is sensitive to the quasar’s radiative history, resulting in ‘light echoes’ that encode the growth history of the SMBH on Myr time-scales. In this paper, we introduce a new technique to photometrically map this quasar light echoes using Lyα forest tomography by using a carefully selected pair of narrow-band filters. A foreground narrow-band filter is used to measure Lyα forest transmission along background galaxies selected as Lyα emitters by a background narrow-band filter. This novel double narrow-band tomographic technique utilizes the higher throughput and wider field of view of imaging over spectroscopy to efficiently reconstruct a two-dimensional map of Lyα forest transmission around a quasar. We present a fully Bayesian framework to measure the luminous quasar lifetime of a SMBH from photometric IGM tomography, and examine the observational requirements. This new technique provides an efficient strategy to map a large area of the sky with a modest observing time and to identify interesting regions to be examined by further deep 3D follow-up spectroscopic Lyα forest tomography.

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