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Jorge Enrique Garcia-Farieta

Publications and source records attributed to Jorge Enrique Garcia-Farieta.

2 recordsLinked to original sources

J-PAS and PFS surveys in the era of dark energy and neutrino mass measurements

Fisher-matrix forecasts are presented for the cosmological surveys of the Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) and the Subaru Prime Focus Spectrograph (PFS). The wide, low-redshift coverage of J-PAS and the high-density, high-redshift mapping of PFS are strongly complementary: combining the two reduces marginalized uncertainties on all primary parameters compared with either survey individually. Adding the joint J-PAS+PFS data to next-generation CMB measurements from the Simons Observatory (SO) and \textsc{LiteBird} yields an expected precision of $σ(\sum m_ν)=0.017\,$eV in the $Λ$CDM$+\sum m_ν+N_{\rm eff}$ framework, sufficient to disfavour the inverted neutrino hierarchy at $2.34\,σ$ if the true mass sum equals the normal-ordering minimum. Motivated by recent DESI results, we also forecast within a $w_0w_a$CDM$+\sum m_ν+N_{\rm eff}$ cosmology, adopting the DESI\,DR2 best-fit values ($w_0=-0.758$, $w_a=-0.82$) as fiducial. The combination CMB+J-PAS+PFS then delivers $σ(w_0)=0.044$ and $σ(w_a)=0.18$, corresponding to a $5.1\,σ$ preference for a time-varying dark-energy equation of state. These findings show that J-PAS and PFS, especially when coupled with Stage-IV CMB observations, will provide competitive tests of neutrino physics and the dynamics of cosmic acceleration.

astro-ph.CO↗

Conditional Diffusion-Flow models for generating 3D cosmic density fields: applications to f(R) cosmologies

Next-generation galaxy surveys promise unprecedented precision in testing gravity at cosmological scales. However, realising this potential requires accurately modelling the non-linear cosmic web. We address this challenge by exploring conditional generative modelling to create 3D dark matter density fields via score-based (diffusion) and flow-based methods. Our results demonstrate the power of diffusion models to accurately reproduce the matter power spectra and bispectra, even for unseen configurations. They also offer a significant speed-up with slightly reduced accuracy, when flow-based reconstructing the probability distribution function, but they struggle with higher-order statistics. To improve conditional generation, we introduce a novel multi-output model to develop feature representations of the cosmological parameters. Our findings offer a powerful tool for exploring deviations from standard gravity, combining high precision with reduced computational cost, thus paving the way for more comprehensive and efficient cosmological analyses

astro-ph.CO↗