arXiv subjects

Saba Etezad-Razavi

Publications and source records attributed to Saba Etezad-Razavi.

3 recordsLinked to original sources

arXiv2025

A New Approach for Constraining Large-Scale Temperature Fluctuations in the Intergalactic Medium

The reionization of helium is thought to occur at $2.5\lesssim z\lesssim4$, marking the last phase transition and final global heating event of the intergalactic medium (IGM). Since it is driven by rare quasars, helium reionization should give rise to strong temperature fluctuations in the IGM between neutral and recently-ionized regions of order $\sigma (\ln T) \sim \Delta T/T = 20-50\%$. We introduce a novel method to search for reionization-induced temperature fluctuations in the IGM by using the effective optical depths of the Lyman-$\alpha$ forest towards a large number of background quasars. Higher IGM temperatures give rise to lower effective optical depths in the Lyman-$\alpha$ forest, implying that temperature fluctuations will broaden the observed optical depth distribution. We measured the distributions of effective Lyman-$\alpha$ forest optical depths across $71$ X-Shooter spectra from the XQ-100 survey in four redshift bins from $z=3.76$ to $z=4.19$ and compared them to a large-volume cosmological hydrodynamical simulation. A good agreement is found between the observations and the simulation, which does not include temperature fluctuations; therefore, we do not detect a signature of helium reionization. We then post-process the simulations to include an increasing amount of temperature fluctuations until the model becomes inconsistent with the observations. We obtain tight constraints on $\sigma (\ln T) < 0.29 \ (<0.40)$ at $2 \sigma\ (3 \sigma)$ at $z=3.76$ when averaging over scales of $100$ comoving Mpc, and weaker constraints for higher redshifts and smaller scales. Our constraints are the tightest to date, and imply that either the IGM temperature contrast caused by helium reionization is less than $\sim30\%$, or that the process has not yet significantly started at $z=3.76$.

astro-ph.CO↗

arXiv2023

Paradox with Phase-Coupled Interferometers

A pair of interferometers can be coupled by allowing one path from each to overlap such that if the particles meet in this overlap region, they annihilate. It was shown by one of us over thirty years ago that such annihilation-coupled interferometers can exhibit apparently paradoxical behaviour. More recently, Bose et al. and Marletto and Vedral have considered a pair of interferometers that are phase-coupled (where the coupling is through gravitational interaction). In this case one path from each interferometer undergoes a phase-coupling interaction. We show that these phase-coupled interferometers exhibit the same apparent paradox as the annihilation-coupled interferometers, though in a curiously dual manner.

quant-ph↗

arXiv2021

Cosmic velocity, density and halo mass function: Insights from deep learning

We discuss an implementation of a deep learning framework to gain insight into dark matter (DM) structure formation. We investigate the contribution of velocity and density field information to the construction of the halo mass function (HMF) in cosmological N-body simulations. We train a Convolutional Neural Network (CNN) on the initial snapshot of a DM-only simulation to predict the halo mass that individual particles fall into at $z = 0$, in the halo mass range of $10.5 < \log (M/M_{\odot}) < 14$. We show that for the standard $\Lambda$CDM cosmology with amplitude of initial perturbations $A_s = 2 \times 10^{-9}$, the initial velocity and density fields have equivalent information, as expected in the linear regime, and manifest the power of our CNN to diagnose the redundant information. To investigate the non-linear effects, we increase the initial power spectrum. In the linear regime, this is equivalent to decreasing the initial redshift. The CNN model trained on the simulation snapshots with large $A_s$ shows a considerable improvement in the HMF prediction when adding the velocity field information. Using our CNN map without further physical assumptions, we precisely evaluate when these non-linear effects become vital. Eventually, for the simulation with $A_s = 8 \times10^{-8}$, the model trained with only density information shows at least an $80\%$ increase in the mean squared error relative to the model with both velocity and density information. Our work shows the interpretability and ability of CNNs to read higher-order information from simple images, making them an excellent tool for cosmological studies.

astro-ph.CO↗