Interpretation of the binned SNe Ia Master Sample data via a scalar quintessence component: phantom transition?

We study a modified cosmological scenario for the late Universe, involving an evolutionary dark energy model associated with the dynamics of a self-interacting scalar field in a potential-dominated regime. The new analysis still awaits peer review, but it already lays out the central claim clearly.

That matters because cosmology operates at the edge of what current instruments can measure, where systematic errors and model assumptions are never trivial. Small discrepancies between independent measurements have historically pointed toward missing physics rather than simple calibration errors, and the ongoing tension in the Hubble constant is a live example of how a persistent disagreement between methods can reshape the theoretical landscape. Each new dataset that approaches this territory with independent systematics adds real information to a problem that has resisted easy resolution for more than a decade. The resulting picture is that of an intrinsic quintessence contribution which, due to the bulk viscosity, admits an effective equation of state parameter that can also take values. Within this framework, we set up the diagnostic tool of the so-called "effective running Hubble constant", which allows us to trace possible deviations from a standard LambdaCDM.

We then compare this theoretical function with binned data from the Master Sample of Supernovae Ia, constructed assuming a LambdaCDM model in the MCMC procedure performed in each. We show that the self-interacting scalar field corresponding to the best fit satisfies a slow-rolling condition, since the kinetic energy remains small compared to the potential.

The key finding is that, when limiting the model to specific regions of the parameter space and fitting it to the data, the transition only occurs at redshifts significantly lower. Furthermore, for the parameter values ensuring the best fit, no quintessence-to-phantom transition occurs (i.

In other words, Supernovae data alone provide no indication of a change in the nature of the dark energy. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy.

The relevance goes beyond one dataset because even small shifts in measured parameters can matter when the field is testing the limits of the standard cosmological model. The Lambda-CDM framework describes the observable universe with remarkable economy, but its success rests on two components, dark matter and dark energy, whose physical nature remains entirely unknown. Any credible measurement that tightens or loosens the constraints on those components moves the entire theoretical enterprise forward, regardless of whether the immediate result looks dramatic on its own terms.

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Because this is still a preprint, the result should be read with genuine interest and proportionate caution. Peer review is not a guarantee of correctness, but it is a process that forces authors to respond to technical criticism from specialists who have no stake in a particular outcome. Preprints that survive that process, often with substantive revisions, emerge with a stronger evidential base than the version that first appeared. Until that stage is complete, the responsible reading keeps uncertainty explicitly visible rather than treating the claims as established findings.

The next step is to see whether the effect survives when independent surveys, different calibration strategies and tighter control of systematic uncertainties enter the picture. Programmes such as Euclid, DESI and the Rubin Observatory will deliver datasets over the next several years that cover the same parameter space with largely independent methods. If the current signal persists through those tests, its theoretical implications will become impossible to set aside. Until peer review and independent follow-up address those open questions, skepticism is not a failure of appreciation for the work; it is part of how science decides what to keep.

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