A semi-cosmographic approach to study cosmological evolution in phase space

dc.contributor.authorSarkar, Tapomoy Guha
dc.date.accessioned2025-03-21T03:58:34Z
dc.date.available2025-03-21T03:58:34Z
dc.date.issued2025-03
dc.description.abstractThe signature of Baryon Acoustic Oscillation in the clustering of dark-matter tracers allows us to measure (DA(z),H(z)) independently. Treating these as conjugate variables, we are motivated to study cosmological evolution in the phase space of dimensionless variables x=H0DA/c and p=dx/dz. The dynamical system (x(z),p(z)) can be integrated for a known set of equation of state parameters for different matter/energy components. However, to avoid any preference for specific dark energy models, we adopt a cosmographic approach. We consider two scenarios where the Luminosity distance is expanded as Padé rational approximants using expansion in terms of z and (1+z)1/2 respectively. However, instead of directly using the Padé ratios to fit kinematic quantities with data, we adopt an alternative approach where the evolution of the cold dark matter sector is incorporated in our analysis through a semi-cosmographic equation of state, which is then, used to solve the dynamical problem in the phase space. The semi-cosmographic (DA(z),H(z)), thus obtained, is fitted with BAO and SNIa data from DESI DR1 + eBOSS and Pantheon+ respectively. We also consider a futuristic 21-cm intensity mapping experiment for error projections. We further use the semi-cosmographic fitting to reconstruct some diagnostics of background cosmology and compare our results for the two scenarios of Padé expansions.en_US
dc.identifier.urihttps://arxiv.org/abs/2503.03288
dc.identifier.urihttp://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/18448
dc.language.isoenen_US
dc.subjectPhysicsen_US
dc.subjectBaryon Acoustic Oscillationen_US
dc.subjectCosmologyen_US
dc.subjectNongalacticen_US
dc.subjectAstrophysicsen_US
dc.titleA semi-cosmographic approach to study cosmological evolution in phase spaceen_US
dc.typePreprinten_US

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