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Record W4225668222 · doi:10.48550/arxiv.2111.05354

Synergies between the COMAP CO Line Intensity Mapping mission and a Lyα galaxy survey: How to probe the early universe with voxel based analysis of observational data

2021· preprint· en· W4225668222 on OpenAlexfundno aff
Marta B. Silva, Bernhard Baumschlager, Kieran Cleary, Patrick C. Breysse, Dongwoo T. Chung, H. T. Ihle, Hamsa Padmanabhan, Laura C. Keating, Junhan Kim, Liju Philip

Bibliographic record

VenuearXiv (Cornell University) · 2021
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsnot available
FundersUniversity of TorontoEuropean CommissionNorges ForskningsrådCalifornia Institute of TechnologySchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsPhysicsAstrophysicsGalaxyIntensity mappingRedshiftIntegral field spectrographReionizationSpectrographLuminosityUniverseAstronomySpectral line

Abstract

fetched live from OpenAlex

Line Intensity Mapping (LIM) offers a novel avenue to observe and characterize our universe. LIM data of CO spectral lines are becoming available, such as those obtained by the CO Mapping Array Project (COMAP). COMAP data can be used to probe the molecular gas content of the universe from the last stages of the Epoch of Reionization (EoR) ($z < 8.0$) to $z \sim 2.5$. In this work, we examine the prospects for deriving voxel-level statistical constraints on high-redshift galaxies from COMAP data by considering the additional information available from observations of LAEs galaxies using the Visible Integral-Field Replicable Unit Spectrograph (VIRUS) on the Hobby-Eberly Telescope (HET). We post-process the IllustrisTNG300 galaxy-formation simulation with a set of prescriptions to consistently determine CO and Ly$α$ line luminosities. The different line prescriptions span the uncertainty in the CO line luminosity according to current observations by the VLA high-z CO surveys and set the Ly$α$ emission to be compatible with observational LAE luminosity functions. We produce mock observations for the two surveys over a $(300\, {\rm Mpc})^3$ volume. These are then used to formulate and test methodologies for data analysis and to predict COMAP constraints on CO emission. We use combinations of masking, stacking, voxel intensity distribution (VID), and other statistics. We find that in combination with VIRUS/HET, a voxel-level analysis of the COMAP Pathfinder survey can detect and characterize the CO signal from $z\sim3$ and improve current constraints on the $z\sim6$ signal, identify individual voxels with bright CO(1-0) emission at $z\sim3$ and probe the redshift evolution of the CO emission. This study illustrates the potential of synergies between LIM and galaxy surveys both to improve the significance of a detection and to aid the interpretation of noisy LIM data.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.129
GPT teacher head0.211
Teacher spread0.083 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2021
Admission routes1
Has abstractyes

Explore more

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