JLA Type Ia Supernovae Data with Peculiar Velocity Corrections

A 2021 reanalysis of the JLA Type Ia supernovae dataset supplements Rahman et al. 2021. It provides covariance matrices incorporating new peculiar velocity corrections and adjustments for selection effects methodology. The data products are authored by Wahidur Rahman of Imperial College London.

Use Cases

• Calibrating supernova distance measurements based on the provided covariance matrices.
• Analyzing large-scale structure and peculiar velocity fields using the corrected supernova data.
• Testing cosmological models independent of the selection effects methodology described in the paper.
• Applying custom selection effects analysis using the alternative covariance matrix version.

Strengths

• Includes covariance matrices with updated peculiar velocity corrections.
• Provides an alternative covariance matrix version for analyses ignoring specific selection effects.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Imperial College London

Collection Method

Reanalysis of the JLA (Joint Light-curve Analysis) supernova dataset.