Our understanding of reionization has advanced considerably over the past decade, with several results now demonstrating that the intergalactic medium transitioned from substantially neutral at z = 7 to largely reionized at z = 6. However, little remains known about the sizes of ionized bubbles at z ≳ 7 as well as the galaxy overdensities which drive their growth. Fortunately, rest-ultraviolet (UV) spectroscopic observations offer a pathway towards characterizing these ionized bubbles thanks to the resonant nature of Lyman-alpha photons. In a previous work, we presented Ly α detections from three closely separated Lyman-break galaxies at z ≃ 6.8, suggesting the presence of a large (R > 1 physical Mpc) ionized bubble in the 1.5 deg2 COSMOS field. Here, we present new deep Ly α spectra of 10 UV-bright ($\mathrm{\mathit{ M}}_{\mathrm{UV}}^{} \le -20.4$) z ≃ 6.6–6.9 galaxies in the surrounding area, enabling us to better characterize this potential ionized bubble. We confidently detect (S/N > 7) Ly α emission at z = 6.701–6.882 in nine of ten observed galaxies, revealing that the large-scale volume spanned by these sources (characteristic radius R = 3.2 physical Mpc) traces a strong galaxy overdensity (N/〈N〉 ≳ 3). Our data additionally confirm that the Ly α emission of UV-bright galaxies in this volume is significantly enhanced, with 40 per cent (4/10) showing strong Ly α emission (equivalent width >25 Å) compared to the 8–9 per cent found on average at z ∼ 7. The median Ly α equivalent width of our observed galaxies is also ≈2 times that typical at z ∼ 7, consistent with expectations if a very large (R ∼ 3 physical Mpc) ionized bubble is allowing the Ly α photons to cosmologically redshift far into the damping wing before encountering H i.
Deep forest: neural network reconstruction of intergalactic medium temperature
We explore the use of deep learning to infer the temperature of the intergalactic medium from the transmitted flux in the high-redshift Ly α forest. We train neural networks on sets of simulated spectra from redshift z = 2–3 outputs of cosmological hydrodynamic simulations, including high-temperature regions added in post-processing to approximate bubbles heated by He ii reionization. We evaluate how well the trained networks are able to reconstruct the temperature from the effect of Doppler broadening in the simulated input Ly α forest absorption spectra. We find that for spectra with high resolution (10 $\, {\rm km}\, {\rm s}^{-1}$ pixel) and moderate signal-to-noise ratio (20–50), the neural network is able to reconstruct the intergalactic medium temperature smoothed on scales of $\sim 6 \, h^{-1}\, {\rm Mpc}$ quite well. Concentrating on discontinuities, we find that high-temperature regions of width $25 \, h^{-1}\, {\rm Mpc}$ and temperature $20\, 000$ K can be fairly easily detected and characterized. We show an example where multiple sightlines are combined to yield tomographic images of hot bubbles. Deep learning techniques may be useful in this way to help us understand the complex temperature structure of the intergalactic medium around the time of helium reionization.
more » « less- Award ID(s):
- 2020295
- NSF-PAR ID:
- 10374461
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Monthly Notices of the Royal Astronomical Society
- Volume:
- 515
- Issue:
- 2
- ISSN:
- 0035-8711
- Page Range / eLocation ID:
- p. 1568-1579
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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