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Dark energy equation of state parameter and its variation at low redshifts

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arxiv 1611.01899 v3 pith:EG3YYYY4 submitted 2016-11-07 astro-ph.CO

Ashutosh Tripathi , Archana Sangwan , H. K. Jassal This is my paper
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keywords energydarkdataequationstatecombinedconstraintsmeasurements
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In this paper, we constrain dark energy models using a compendium of observations at low redshifts. We consider the dark energy as a barotropic fluid, with the equation of state a constant as well the case where dark energy equation of state is a function of time. The observations considered here are Supernova Type Ia data, Baryonic Acoustic Oscillation data and Hubble parameter measurements. We compare constraints obtained from these data and also do a combined analysis. The combined observational constraints put strong limits on variation of dark energy energy density with redshift. For varying dark energy models, the range of parameters preferred by the supernova type Ia data is in tension with the other low redshift distance measurements.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Data-Driven Discovery of a Simple Phantom-Crossing Dark Energy Parametrization

    astro-ph.CO 2026-06 unverdicted novelty 7.0 of 10

    Bayesian reconstruction and exhaustive symbolic regression on CMB, BAO, and supernova data yield the one-parameter dark energy parametrization w(a) = w0 / sqrt(a) that fits observations comparably to CPL and better th...

  2. Defocusing dark energy: Raychaudhuri diagnostics beyond $w<-1/3$ and the phantom divide

    gr-qc 2026-07 accept novelty 6.0 of 10

    For sign-changing effective dark energy, smooth negative-to-positive density crossings make ρ+3p and ρ+p the correct diagnostics, give w=p/ρ a universal pole n(1+z†)/3, and force repulsion onset z_rep>z† while ρ<0.

  3. Revisiting Heat Flux Analysis of Tungsten Monoblock Divertor on EAST using Physics-Informed Neural Network

    cs.LG 2025-08 unverdicted novelty 5.0 of 10

    A physics-informed neural network matches finite element accuracy for EAST divertor heat flux while running about 40 times faster.

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