Second Data Release of the COSMOS Lyman-alpha Mapping and Tomographic Observation: The First 3D Maps of the Detailed Cosmic Web at 2.05

Horowitz, B. Zane; Lee, Khee-Gan; Ata, M.; Müller, Thomas; Krolewski, Alex; Prochaska, J. Xavier; Schlegel, David J.; Rich, R. Michael; Nugent, P.; Suzuki, Noriyuki; Kashino, Daichi; Koekemoer, Anton M.; Lemaux, Brian C.

doi:10.48550/arxiv.2109.09660

Cited by 7 publications

(11 citation statements)

References 70 publications

(111 reference statements)

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“…Moreover, we thus far have not included any instrumental noise. In order to see if the results will still hold up under these conditions, we additionally create skewers that are smoothed and rebinned to the same spectral resolution, and have had noise introduced at a level consistent with the Lyα forest data from the CLAMATO survey (Lee et al 2018;Horowitz et al 2021b). Another possibility we examine is IGM tomography surveys using the future thirty-meter class extremely large telescopes (ELTs), namely the Thirty-Meter Telescope (Skidmore et al 2015), European Extremely Large Telescope (Evans et al 2014), and Giant Magellan Telescope (Johns et al 2012).…”

Section: Mock Observational Igm Mapsmentioning

confidence: 99%

“…Finally, the resulting skewers were processed with the Wiener-filtering code dachshund 2 described by Stark et al (2015) in order to obtain 3D tomographic maps of the Lyα forest transmission, that are realistic representations of observations with a survey such as CLAMATO or an ELT. Note that in terms of cosmic volume, CLAMATO has sampled a comoving volume of V = 4.1 × 10 5 h 3 Mpc −3 (Horowitz et al 2021b), which is 2.5× smaller than the V = 10 6 h 3 Mpc −3 volume sampled by the Nyx box but comparable to the EAGLE and Ilustris/IlustrisTNG simulation volumes (V = 3.1 × 10 5 h 3 Mpc −3 and V = 4.2 × 10 5 h 3 Mpc −3 , respectively). We therefore expect any variance from the different CLAMATO-like mock realizations to be dominated by the sightline sampling and pixel noise, rather than from sample variance caused the finite volume.…”

Section: Mock Observational Igm Mapsmentioning

confidence: 99%

“…While the individual spectra are of low signal-to-noise (S/N ∼ few per resolution element) and moderate spectral resolution (R ∼ 10 3 ), the information in these skewers can be combined to construct accurate three-dimensional (3D) maps of the large-scale HI absorption field through Wiener-filtering (Pichon et al 2001;Caucci et al 2008). This technique has become known as Lyα forest or IGM tomography and has been pioneered by the COSMOS Lyman Alpha Mapping And Tomographic Observations (CLAMATO) survey (Lee et al 2018;Horowitz et al 2021b). This large-scale tomographic survey probes the well-studied COSMOS field with 360 galaxy and quasar sightlines over an area of ∼ 0.2 deg 2 and a redshift range of 2.05 < z < 2.55.…”

Section: Introductionmentioning

confidence: 99%

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Constraining the Fluctuating Gunn-Peterson Approximation \\ Using Lyman-$α$ Forest Tomography at $z=2$

Kooistra¹,

Lee²,

Horowitz³

2022

Preprint

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The fluctuating Gunn-Peterson approximation (FGPA) is a commonly-used method to generate mock Lyman-α (Lyα) forest absorption skewers at Cosmic Noon (z 2) from the matter-density field of N -body simulations without running expensive hydrodynamical simulations. Motivated by recent developments in 3D IGM tomography observations as well as matter density field reconstruction techniques applied to galaxy redshift samples at z ∼ 2, we examine the possibility of observationally testing FGPA by directly examining the relationship between the Lyα transmission and the underlying matter density field. Specifically, we analyze the EAGLE, Illustris, IllustrisTNG and Nyx cosmological hydrodynamic simulations, that were run with different codes and sub-grid models. While FGPA is an excellent description of the IGM in lower-density regions, the slope of the transmission-density distribution at higher densities is significantly affected by feedback processes causing FGPA to break down in that regime. Even without added feedback, we find significant deviations caused by hydrodynamical effects arising from non-linear structure growth. We then proceed to make comparisons using realistic mock data assuming the sightline sampling and spectral properties of the recent CLAMATO survey, and find that it would be challenging to discern between FGPA and hydrodynamical models with current data sets. However, the improved sightline sampling from future extremely large telescopes or large volumes from multiplexed spectroscopic surveys such as Subaru PFS should allow for stringent tests of FGPA, and make it possible to detect the effect of galaxy feedback on the IGM.

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Section: Mock Observational Igm Mapsmentioning

confidence: 99%

Section: Mock Observational Igm Mapsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Constraining the Fluctuating Gunn-Peterson Approximation \\ Using Lyman-$α$ Forest Tomography at $z=2$

Kooistra¹,

Lee²,

Horowitz³

2022

Preprint

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“…To date, there are only two tomography surveys dedicated to mapping the IGM with Mpc resolution at z ∼ 2.5. The COSMOS Lyman-α Mapping and Tomography (CLAMATO) project (Lee et al 2018;Horowitz et al 2021b), the pioneering survey of this type, created the first Mpc-resolution large-scale structure map in the redshift range z = 2.05 − 2.55 comprising a survey volume of 4.1 × 10 5 h −3 Mpc 3 . This map led to the successful detection of a protocluster with an estimated mass of 9 × 10 13 h −1 M at z ∼ 2.45 within the COS-MOS field along with a characterization of voids at the same redshift (Krolewski et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Characterizing Protoclusters and Protogroups at z $\sim$ 2.5 Using Ly$α$ Tomography

Qezlou,

Newman,

Rudie

et al. 2021

Preprint

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Lyman-α tomography surveys have begun to produce three-dimensional (3D) maps of the intergalactic medium (IGM) opacity at z ∼ 2.5 with Mpc resolution. These surveys provide an exciting new way to discover and characterize high-redshift overdensities, including the progenitors of today's massive groups and clusters of galaxies, known as protogroups and protoclusters. We use the IllustrisTNG-300 hydrodynamical simulation to build mock maps that realistically mimic those observed in the Lymanα Tomographic IMACS Survey (LATIS). We introduce a novel method for delineating the boundaries of structures detected in 3D Lyman-α flux maps by applying the watershed algorithm. We provide estimators for the dark matter masses of these structures (at z ∼ 2.5), their descendant halo masses at z = 0, and the corresponding uncertainties. We also investigate the completeness of this method for the detection of protogroups and protoclusters. Compared to earlier work, we apply and characterize our method over a wider mass range that extends to massive protogroups. We also assess the widely used fluctuating Gunn-Peterson approximation (FGPA) applied to dark-matter-only simulations; we conclude while it is adequate for estimating the Lyman-α absorption signal from moderate-to-massive protoclusters ( 10 14.2 h −1 M ), it artificially merges a minority of lower-mass structures with more massive neighbors. Our methods will be applied to current and future Lyman-α tomography surveys to create catalogs of overdensities and study environment-dependent galaxy evolution in the Cosmic Noon era.

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“…Encouraged by these theoretical pursuits, three-dimensional reconstruction of the density field from the Ly-𝛼 forest has already been successfully performed in observational surveys, notably with the CLAMATO program (see Horowitz et al 2021a, for the latest release) and eBOSS-Stripe 82 (Ravoux et al 2020). The Ly-𝛼 forest has proven to be a powerful tracer of the density field, particularly sensitive to intermediate densities: therefore tomographic reconstruction should allow us to characterise the geometry of the weakly over-and under-dense regions of the Universe, i.e.…”

mentioning

confidence: 99%

Forecasts for WEAVE-QSO: 3D clustering and connectivity of critical points with Lyman-$α$ tomography

Kraljic,

Laigle,

Pichon

et al. 2022

Preprint

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The upcoming WEAVE-QSO survey will target a high density of quasars over a large area, enabling the reconstruction of the 3D density field through Lyman-𝛼 tomography over unprecedented volumes smoothed on intermediate scales (≈ 16 Mpc/h). We produce mocks of the Lyman-𝛼 forest using LyMAS, and reconstruct the 3D density field between sightlines through Wiener filtering in a configuration compatible with the future WEAVE-QSO observations. The fidelity of the reconstruction is assessed by measuring one-and two-point statistics from the distribution of critical points in the cosmic web. In addition, initial Lagrangian statistics are predicted from first principles, and measurements of the connectivity of the cosmic web are performed. The reconstruction captures well the expected features in the auto-and cross-correlations of the critical points. This remains true after a realistic noise is added to the synthetic spectra, even though sparsity of sightlines introduces systematics, especially in the cross-correlations of points with mixed signature. Specifically, for walls and filaments, the most striking clustering features could be measured with up to 4 sigma of significance with a WEAVE-QSO-like survey. Moreover, the connectivity of each peak identified in the reconstructed field is globally consistent with its counterpart in the original field, indicating that the reconstruction preserves the geometry of the density field not only statistically, but also locally. Hence the critical points relative positions within the tomographic reconstruction could be used as standard rulers for dark energy by WEAVE-QSO and similar surveys.

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Second Data Release of the COSMOS Lyman-alpha Mapping and Tomographic Observation: The First 3D Maps of the Detailed Cosmic Web at 2.05

Cited by 7 publications

References 70 publications

Constraining the Fluctuating Gunn-Peterson Approximation \\ Using Lyman-$α$ Forest Tomography at $z=2$

Constraining the Fluctuating Gunn-Peterson Approximation \\ Using Lyman-$α$ Forest Tomography at $z=2$

Characterizing Protoclusters and Protogroups at z $\sim$ 2.5 Using Ly$α$ Tomography

Forecasts for WEAVE-QSO: 3D clustering and connectivity of critical points with Lyman-$α$ tomography

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