## Publications

## 2024

The measurements of the cosmic microwave background (CMB) have played a significant role in understanding the nature of dark energy. In this article, we investigate the dynamics of the dark energy equation of state, utilizing high-precision CMB data from multiple experiments. We begin by examining the Chevallier-Polarski-Linder (CPL) parameterization, a commonly used and recognized framework for describing the dark energy equation of state. We then explore the general Exponential parameterization, which incorporates CPL as its first-order approximation, and extensions of this parameterization that incorporate nonlinear terms. We constrain these scenarios using CMB data from various missions, including the Planck 2018 legacy release, the Wilkinson Microwave Anisotropy Probe (WMAP), the Atacama Cosmology Telescope (ACT), and the South Pole Telescope (SPT), as well as combinations with lowIn this study, we investigate gravitational lensing within the framework of more realistic dark matter halo models, transcending the limitations of spherical-collapse approximations. Through analytical computations utilizing diverse mass functions, we address critical factors typically overlooked in the standard Press-Schechter formalism, including ellipsoidal-collapse conditions, angular momentum dynamics, dynamical friction, and the cosmological constant. Our analysis incorporates two widely recognized halo density profiles, the Navarro-Frenk-White and Einasto profiles, considering both spherical and ellipsoidal-collapse scenarios. We present relevant calculations of pivotal gravitational lensing observables, such as Einstein radii, lensing optical depths, and time delays, spanning a wide range of redshifts and masses across two distinct lensing models: the point mass and singular isothermal sphere (SIS) lens models. Our findings illuminate that adopting more realistic dark matter halo models leads to heightened lensing effects compared to their spherical-collapse counterparts. Furthermore, our analyses of lensing optical depths and time delays reveal distinct characteristics between point mass and SIS lens models. These outcomes highlight the need for more realistic halo descriptions instead of simple approximations when modeling gravitational lensing, as this approach can potentially better reveal the complex structures of dark matter.

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Dynamical dark energy confronted with multiple CMB missions**

Mahdi Najafi, Supriya Pan, Eleonora Di Valentino, Javad T. Firouzjaee

The measurements of the cosmic microwave background (CMB) have played a significant role in understanding the nature of dark energy. In this article, we investigate the dynamics of the dark energy equation of state, utilizing high-precision CMB data from multiple experiments. We begin by examining the Chevallier-Polarski-Linder (CPL) parameterization, a commonly used and recognized framework for describing the dark energy equation of state. We then explore the general Exponential parameterization, which incorporates CPL as its first-order approximation, and extensions of this parameterization that incorporate nonlinear terms. We constrain these scenarios using CMB data from various missions, including the Planck 2018 legacy release, the Wilkinson Microwave Anisotropy Probe (WMAP), the Atacama Cosmology Telescope (ACT), and the South Pole Telescope (SPT), as well as combinations with low

The ongoing debate regarding the most accurate accretion model for supermassive black holes at the center of quasars has remained a contentious issue in astrophysics. One significant challenge is the variation in calculated accretion efficiency, with values exceeding the standard range of . This discrepancy is especially pronounced in high redshift supermassive black holes, necessitating the development of a comprehensive model that can address the accretion efficiency for supermassive black holes in both the low and high redshift ranges. In this study, we have focused on low redshift () PG quasars (79 quasars) and high redshift () quasars with standard disks from the flux- and volume-limited QUOTAS+QuasarNET dataset (76 quasars) to establish a model for accretion efficiency. An interesting trend is revealed where in redshift larger than 3, accretion efficiency increases as redshift decreases, while in redshift lower than 0.5, accretion efficiency decreases with reducing redshift. This suggests a peak in accretion efficiency between the low and high redshift quasars. This peak is recognized for the flux- and volume-limited QUOTAS+QuasarNET+DL11 dataset, which is , and it seems to be related to the peak of the star formation rate. (). This result can potentially lead to a more accurate correlation between the star formation rate in quasars and their relationship with the mass of the central supermassive black holes with a more comprehensive disk model in future studies.

**Structure formation in various dynamical dark energy scenarios**

Masoume Reyhani, Mahdi Najafi, Javad T. Firouzjaee, Elenora Di Valentino

DOI: 10.1016/j.dark.2024.101477This research investigates the impact of the nature of Dark Energy (DE) on structure formation, focusing on the matter power spectrum and the Integrated Sachs–Wolfe effect (ISW). By analyzing the matter power spectrum at redshifts z= 0 and z= 5, as well as the ISW effect on the scale of ℓ= 10− 100, the study provides valuable insights into the influence of DE equations of state (EoS) on structure formation. The findings reveal that dynamical DE models exhibit a stronger matter power spectrum compared to constant DE models, with the JBP model demonstrating the highest amplitude and the CPL model the weakest. Additionally, the study delves into the ISW effect, highlighting the time evolution of the ISW source term F (a) and its derivative d F (a)/d a, and demonstrating that models with constant DE EoS exhibit a stronger amplitude of F (a) overall, while dynamical models such as CPL exhibit the highest amplitude

Oil companies are among the largest companies in the world whose economic indicators in the global stock market have a great impact on the world economy (Stevens, 2018) and market due to their relation to gold (Aijaz et al., 2016), crude oil (Henriques & Sadorsky, 2008), and the dollar (Huang et al., 1996). This study investigates the impact of correlated features on the interpretability of long short‐term memory (LSTM) (Peters, 2001) models for predicting oil company stocks. To achieve this, we designed a standard long short‐term memory (LSTM) network and trained it using various correlated data sets. Our approach is aimed at improving the accuracy of stock price prediction by considering the multiple factors affecting the market, such as crude oil prices, gold prices, and the US dollar. The results demonstrate that adding a feature correlated with oil stocks does not improve the interpretability of LSTM models