Discordances in Cosmology and the Violation of Slow-Roll Inflationary Dynamics.

We identify examples of single field inflationary trajectories beyond the slow-roll regime that improve the fit to Planck 2018 data compared to a baseline Λ cold dark matter model with power law form of primordial spectrum and at the same time alleviate existing tensions between different datasets in the estimate of cosmological parameters such as H_{0} and S_{8}. A damped oscillation in the first Hubble flow function-or equivalently a feature in the potential-and the corresponding localized oscillations in the primordial power spectrum partially mimic the improvement in the fit of Planck data due to A_{L} or Ω_{K}. Compared to the baseline model, this model can lead simultaneously to a larger value of H_{0} and a smaller value of S_{8}, a trend that can be enhanced when the most recent SH0ES measurement for H_{0} is combined with Planck and BICEP-Keck 2018 data. Large scale structure data and more precise cosmic microwave background polarization measurements will further provide critical tests of this intermediate fast-roll phase.

Modelling the first wave of COVID-19 in India.

Estimating the burden of COVID-19 in India is difficult because the extent to which cases and deaths have been undercounted is hard to assess. Here, we use a 9-component, age-stratified, contact-structured epidemiological compartmental model, which we call the INDSCI-SIM model, to analyse the first wave of COVID-19 spread in India. We use INDSCI-SIM, together with Bayesian methods, to obtain optimal fits to daily reported cases and deaths across the span of the first wave of the Indian pandemic, over the period Jan 30, 2020 to Feb 15, 2021. We account for lock-downs and other non-pharmaceutical interventions (NPIs), an overall increase in testing as a function of time, the under-counting of cases and deaths, and a range of age-specific infection-fatality ratios. We first use our model to describe data from all individual districts of the state of Karnataka, benchmarking our calculations using data from serological surveys. We then extend this approach to aggregated data for Karnataka state. We model the progress of the pandemic across the cities of Delhi, Mumbai, Pune, Bengaluru and Chennai, and then for India as a whole. We estimate that deaths were undercounted by a factor between 2 and 5 across the span of the first wave, converging on 2.2 as a representative multiplier that accounts for the urban-rural gradient. We also estimate an overall under-counting of cases by a factor of between 20 and 25 towards the end of the first wave. Our estimates of the infection fatality ratio (IFR) are in the range 0.05-0.15, broadly consistent with previous estimates but substantially lower than values that have been estimated for other LMIC countries. We find that approximately 35% of India had been infected overall by the end of the first wave, results broadly consistent with those from serosurveys. These results contribute to the understanding of the long-term trajectory of COVID-19 in India.

Joining Bits and Pieces of Reionization History.

Cosmic microwave background (CMB) temperature and polarization anisotropies from Planck have estimated a lower value of the optical depth to reionization (τ) compared to WMAP. A significant period in the reionization history would then fall within 6

Inflation with whip-shaped suppressed scalar power spectra.

Motivated by the idea that inflation occurs at the grand unified theory symmetry breaking scale, in this Letter we construct a new class of large field inflaton potentials where the inflaton starts with a power law potential; after an initial period of relatively fast roll that lasts until after a few e folds inside the horizon it transits to the attractor of the slow roll part of the potential with a lower power. Because of the initial fast roll stages of inflation, we find a suppression in scalar primordial power at large scales and at the same time the choice of the potential can provide us a tensor primordial spectrum with a high amplitude. This suppression in scalar power with a large tensor-to-scalar ratio helps us to reconcile the Planck and BICEP2 data in a single framework. We find that a transition from a cubic to quadratic form of inflaton potential generates an appropriate suppression in the power of the scalar primordial spectrum that provides a significant improvement in fit compared to the power law model when compared with Planck and BICEP2 data together. We calculate the extent of non-Gaussianity, specifically, the bispectrum for the best fit potential, and show that it is consistent with Planck bispectrum constraints.

Primordial non-Gaussianity in the forest: 3D bispectrum of Lyman-α flux spectra along multiple lines of sight.

We investigate the possibility of constraining primordial non-Gaussianity using the 3D bispectrum of the Lyman (Ly)-α forest. The strength of the quadratic non-Gaussian correction to an otherwise Gaussian primordial gravitational field is assumed to be dictated by a single parameter f(NL). We present the first prediction for bounds on f(NL) using Ly-α flux spectra along multiple lines of sight. The 3D Ly-α transmitted flux field is modeled as a biased tracer of the underlying matter distribution sampled along 1D skewers corresponding to quasar sight lines. The precision to which f(NL) can be constrained depends on the survey volume, pixel noise, and aliasing noise (arising from discrete sampling of the density field). We consider various combinations of these factors to predict bounds on f(NL). We find that in an idealized situation of full sky survey and negligible Poisson noise one may constrain f(NL)∼23 in the equilateral limit. Assuming a Ly-α survey covering large parts of the sky (k(min) = 8 × 10(-4) Mpc(-1)) and with a quasar density of n = 5 × 10(-3) Mpc(-2), it is possible to constrain f(NL)∼100 for equilateral configurations. The possibility of measuring f(NL) at a precision comparable to large scale structure studies maybe useful for joint constraining of inflationary scenarios using different data sets.