arXiv subjects

N. Krishnan

Publications and source records attributed to N. Krishnan.

4 recordsLinked to original sources

arXiv2026

Application of the Skyrme Hartree-Fock-Bogoliubov Theory to WIMP-Nucleus Interactions in 40Ar

WIMP scattering from 40Ar is investigated using a self-consistent Skyrme Hartree-Fock-Bogoliubov (HFB) approach. Nuclear form factors relevant to dark matter direct detection are calculated from the resulting one-body density matrix elements and compared with shell-model predictions. Good agreement is found for the spin-independent response, while significant differences are observed for the spin-orbit response due to variations in single-particle occupancies. The effects of particle-number projection are shown to be small for 40Ar. These results demonstrate the sensitivity of certain dark matter response channels to the underlying nuclear structure model and establish a framework for extending mean-field calculations to nuclei beyond the reach of large-scale shell-model studies.

nucl-th↗

arXiv2024

The picasso gas model: Painting intracluster gas on gravity-only simulations

We introduce picasso, a model designed to predict thermodynamic properties of the intracluster medium based on the properties of halos in gravity-only simulations. The predictions result from the combination of an analytical gas model, mapping gas properties to the gravitational potential, and of a machine learning model to predict the model parameters for individual halos based on their scalar properties, such as mass and concentration. Once trained, the model can be applied to make predictions for arbitrary potential distributions, allowing its use with flexible inputs such as N-body particle distributions or radial profiles. We present the model, and train it using pairs of gravity-only and hydrodynamic simulations. We show that when trained to learn the mapping from gravity-only to non-radiative hydrodynamic simulations, picasso can make remarkably accurate and precise predictions of intracluster gas thermodynamics, with percent-level bias and $\sim 20 \%$ scatter for $r / R_{500c} \in [0.1,1]$. Training the model on hydrodynamic simulations including sub-resolution physics modeling yields robust predictions as well, albeit with the introduction of a radius-dependent bias and an increase in scatter. We further show that the model can be trained to make accurate predictions from very minimal halo information, down to mass and concentration, at the cost of modestly reduced precision. picasso is made publicly available as a Python package at https://github.com/fkeruzore/picasso, which includes trained models that can be used to make predictions easily and efficiently, in a fully auto-differentiable and hardware-accelerated framework

astro-ph.CO↗

arXiv2015

Linear Massive MIMO Precoders in the Presence of Phase Noise -- A Large-Scale Analysis

We study the impact of phase noise on the downlink performance of a multi-user multiple-input multiple-output system, where the base station (BS) employs a large number of transmit antennas $M$. We consider a setup where the BS employs $M_{\mathrm{osc}}$ free-running oscillators, and $M/M_{\mathrm{osc}}$ antennas are connected to each oscillator. For this configuration, we analyze the impact of phase noise on the performance of the regularized zero-forcing (RZF), when $M$ and the number of users $K$ are asymptotically large, while the ratio $M/K=β$ is fixed. We analytically show that the impact of phase noise on the signal-to-interference-plus-noise ratio (SINR) can be quantified as an effective reduction in the quality of the channel state information available at the BS when compared to a system without phase noise. As a consequence, we observe that as $M_{\mathrm{osc}}$ increases, the SINR performance of all considered precoders degrades. On the other hand, the variance of the random phase variations caused by the BS oscillators reduces with increasing $M_{\mathrm{osc}}$. Through Monte-Carlo simulations, we verify our analytical results, and compare the performance of the precoders for different phase noise and channel noise variances. For all considered precoders, we show that when $β$ is small, the performance of the setup where all BS antennas are connected to a single oscillator is superior to that of the setup where each BS antenna has its own oscillator. However, the opposite is true when $β$ is large and the signal-to-noise ratio at the users is low.

cs.IT↗

arXiv2014

On the Impact of Oscillator Phase Noise on the Uplink Performance in a Massive MIMO-OFDM System

In this work we study the effect of oscillator phase noise on the uplink performance of a massive multiple-input multiple output system. Specifically, we consider an orthogonal frequency division multiplexing-based uplink transmission and analyze two scenarios: (a) all the base station (BS) antennas are fed by a common oscillator, and (b) each of the BS antennas is fed by a different oscillator. For the scenarios considered, we derive the instantaneous signal-to-noise ratio on each subcarrier and analyze the ergodic capacity when a linear receiver is used. Furthermore, we propose a phase noise tracking algorithm based on Kalman filtering that mitigates the effect of phase noise on the system performance.

cs.IT↗