Statistical invalidation of the Hubble law

Area of Science:

• Cosmology
• Astrophysics
• Statistical Astronomy

Background:

• The Hubble law describes the universe's expansion using a linear redshift-distance relationship.
• Observational biases, such as redshift-magnitude cutoff, can affect cosmological measurements.
• Theoretical cosmology provides frameworks for understanding cosmic expansion and distances.

Purpose of the Study:

• To apply an optimal nonparametric technique to eliminate observation cutoff bias.
• To test the validity of linear versus square redshift-distance laws in galaxy samples.
• To statistically evaluate the foundation of the Hubble law.

Main Methods:

• Application of an optimal nonparametric technique for bias elimination.
• Analysis of redshift-magnitude and redshift-angular diameter relationships.
• Nonparametric crosstesting of linear and square redshift-distance law hypotheses using data and simulations.

Main Results:

• Estimates strongly favored a square redshift-distance law over a linear one.
• The square law accurately predicted the statistical outcomes of the linear law analyses.
• The linear law failed to predict the observed good fit of the square law.

Conclusions:

• The Hubble law, in its linear form, appears to lack an objective statistical foundation.
• The square redshift-distance law demonstrates superior statistical performance.
• Further validation of ancillary hypotheses is needed to support the linear model.