view Abstract Citations (6) References (18) Co-Reads Similar Papers Volume Content Graphics Metrics Export Citation NASA/ADS Statistically Efficient Parallel Testing of the Friedmann and Chronometric Flux-Redshift Predictions in the Radio Band Segal, I. E. ; Nicoll, J. F. ; Blackman, E. Abstract The flux-redshift predictions of Friedmann and chronometric cosmology are subjected to statistically efficient parallel tests on the basis of the 3C catalog of radio sources, as updated by Spinrad et al. (1985). Sub-samples limited in flux, redshift, and spectral index are treated to this end. Luminosity functions are estimated by an optimal nonparametric procedure that does not postulate spatial homogeneity or require completeness in redshift. Directly observed quantities are compared with the predictions of the respective cosmologies, and objective statistical significance levels are determined. Within the frame of the Friedmann cosmology, the disperson in log flux invariably exceeds the observed value, and the same is true of the correlation of log luminosity with redshift. Over the full redshift range of the sample, extending to z ~ 2.5, the deviations are significant at the ~4 σ, level. On the parallel basis the chronometric predictions are highly consistent with the observations, without evolution or any adjustable cosmological parameter such as q_0_. Moreover, the chronometric cosmology predicts quite accurately the deviations of the Friedmann predictions from direct observation, while the Friedmann cosmology is unable to explain the excellent fit of the chronometric predictions. Publication: The Astrophysical Journal Pub Date: July 1994 DOI: 10.1086/174382 Bibcode: 1994ApJ...430...63S Keywords: Computational Astrophysics; Cosmology; Galactic Evolution; Prediction Analysis Techniques; Radio Sources (Astronomy); Red Shift; Statistical Tests; Astronomical Catalogs; Chronology; Luminosity; Astrophysics; COSMOLOGY: THEORY; GALAXIES: DISTANCES AND REDSHIFTS; GALAXIES: EVOLUTION; RADIO CONTINUUM: GALAXIES full text sources ADS |