World Model Research Articles

Dynamics of cosmological phase crossover during Bose–Einstein condensation of dark matter in Tsallis cosmology

AbstractDuring the cosmic evolution process, as the temperature of a cosmological boson gas falls below a certain threshold, a Bose–Einstein condensation process can occur at various points throughout the cosmic history of the Universe. In this model, dark matter, conceptualized as a non-relativistic, Newtonian gravitational condensate is governed by the Gross–Pitaevskii–Poisson system. In our present study, we investigate the Bose–Einstein condensation process of bosonic DM by treating it as an approximate first-order phase transition within a modified cosmological framework, known as Tsallis cosmology. We examine the evolution of relevant physical quantities characterizing the evolution dynamics of the Universe, including energy density, temperature, redshift, scale factor, Hubble parameter, and dimensionless deceleration parameter before, during, and following the Bose–Einstein condensation phase transition takes place. Additionally, we especially investigate the specific era of the evolution of the Universe characterized by a mixture of normal and condensate phases of dark matter. We analyze the behavior of temporal evolution of an important time-dependent parameter, called the condensate dark matter fraction throughout the condensation process and find the time duration of condensation of dark matter in the Tsallis cosmological model. We see that the presence of Bose–Einstein condensate dark matter in the framework of Tsallis-modified cosmology significantly alters the cosmological evolution of the Universe as compared to the standard model of cosmology. We also find for a typical value of Tsallis non-extensive parameter $$\beta =0.35$$ β = 0.35 , the model could explain an accelerated Universe without invoking any additional energy component and solve the age problem of our Universe.

Momentum – the Archilles’ Heel of Causality-based Physics

Galileo famously discovered the inverse square law of gravitation on earth and also formulated the concept of momentum as “mv” (mass x velocity) for the dynamics of moving bodies. But the momentum “mv” was at an odd with his inverse square law of “Fee Fall” as pointed out later by G.W. Leibniz, who gave the correct formulation of momentum as “mv^2”. G.W.F. Hegel later showed that the momentum expressed as “mv” is incompatible with what he called, “the laws of absolutely free motion” discovered by Kepler’s three laws of the planetary system. However, in spite of the controversy raised by J. Kepler, Leibniz and Hegel; the continued use of “mv” as the measure of momentum of moving bodies in physics and dynamics since R. Descartes and I. Newton; until now, even after the recognition of the quantum phenomena at the turn of the 20th century; is leading to more and more mysteries and myths; impeding further developments in physics and cosmology. It can now be shown that developments in Quantum electrodynamics and dialectics abolishes any notion of Newtonian mystery of “First Impulse” for matter and motion (momentum) from God. Leibniz’s formulation of momentum as “mv^2” is more appropriate and its use can resolve many of the problems in modern theoretical physics and cosmology; including in extra-terrestrial dynamics of the heavenly bodies and gravitation, electrodynamics and in quantum electrodynamics. The recognition of Leibniz’s vis viva, Hegel’s “absolute dynamics” and momentum as “mv^2” (“mv^3” only for neutral quantum particles like light photons); would represent the end of all mysticism and myths of cosmology, created since Isaac Newton’s “negation” of the Copernican revolution; later reinforced and accentuated by Albert Einstein’s theories of relativity.

EchoPT: A Pretrained Transformer Architecture that Predicts 2D In-Air Sonar Images for Mobile Robotics

The predictive brain hypothesis suggests that perception can be interpreted as the process of minimizing the error between predicted perception tokens generated via an internal world model and actual sensory input tokens. When implementing working examples of this hypothesis in the context of in-air sonar, significant difficulties arise due to the sparse nature of the reflection model that governs ultrasonic sensing. Despite these challenges, creating consistent world models using sonar data is crucial for implementing predictive processing of ultrasound data in robotics. In an effort to enable robust robot behavior using ultrasound as the sole exteroceptive sensor modality, this paper introduces EchoPT (Echo-Predicting Pretrained Transformer), a pretrained transformer architecture designed to predict 2D sonar images from previous sensory data and robot ego-motion information. We detail the transformer architecture that drives EchoPT and compare the performance of our model to several state-of-the-art techniques. In addition to presenting and evaluating our EchoPT model, we demonstrate the effectiveness of this predictive perception approach in two robotic tasks.

JWST observations constrain the time evolution of fine structure constants and dark energy-electromagnetic coupling

Abstract It was hypothesized in the literature that some physical parameters may be time-evolving and the astrophysical data can serve as a probe. Recently, James Webb Space Telescope (JWST) have released its early observations. In this work, we select the JWST spectroscopic observations of the high redshift ($z>7.1$) galaxies with strong [\ion{O}{III}] ($\lambda=4959$\AA \,and $5007$\AA \,in the rest frame) emission lines to constraint the evolution of the fine structure constant ($\alpha$). With the spectra from two galaxies at redshifts of $7.19$ and $8.47$, the deviation of $\alpha$ to its fiducial value is found to be as small as $0.44^{+8.4+1.7}_{-8.3-1.7} \times 10^{-4}$ and $-10.0^{+18+1.5}_{-18-1.5} \times 10^{-4}$, respectively (the first error is statistical and the latter is systematic). The combination of our results with the previous data reveals that $\frac{1}{\alpha} \frac{d \alpha}{dt} = 0.30^{+4.5}_{-4.5} \times 10^{-17}~{\rm yr^{-1}}$. Clearly, there is no evidence for a cosmic evolution of $\alpha$. The prospect of further constraining the time evolution of $\alpha$ is also discussed. The scalar field of dark energy is hypothesized to drive the acceleration of the universe's expansion through an interaction with the electromagnetic field. By integrating the observational data of the fine-structure constant variation, $\frac{\Delta\alpha}{\alpha}(z)$, we have established a stringent upper limit on the coupling strength between dark energy and electromagnetism. Our analysis yields $\zeta \leq 3.92 \times 10^{-7}$ at the 95\% confidence level, representing the most stringent bound to date.

PROBLEMS AND PROSPECTS OF THE DEVELOPMENT OF BANKING REGULATION AND SUPERVISION IN UKRAINE

Effective banking regulation and supervision are key factors for ensuring the stability of the national banking system of Ukraine. The purpose of this article is to analyze the peculiarities of banking regulation and supervision in Ukraine in order to identify problems and possible ways of development, including in the context of world experience. The article examines the theoretical aspects of the essence of the concepts of banking regulation and banking supervision. The main indicators of the banking system of Ukraine for 2020-2024 and the mechanism for identifying risks in the activities of banking institutions are also considered. The global trends in banking regulation at the current stage are its centralization, the concept of risk-oriented supervision, the introduction of new financial technologies, the development of the principles of proportional banking regulation. The main positive and negative methods of supervision in the banking sector are singled out. It has been proven that the conditions of a full-scale military conflict significantly undermined the development of the banking system of Ukraine. The destruction of infrastructure, loss of customer confidence, risks to the bank's loan portfolio and capitalization have become major problems. In addition, the decrease in liquidity and the growth of inflation risks create serious challenges for the banking system. The degree of suitability of world models in terms of the priority tasks of the state regarding the regulation of the banking sector is analyzed. Directions for improving banking supervision through improvement of banking activity licensing procedures are proposed; introduction of permanent monitoring of the activities of domestic commercial banks, in particular their financial condition; implementation of constant monitoring of compliance with banking regulations and current legislation; introduction of permanent inspections of banking activities; implementation by commercial banks of ongoing internal control and external audit control; developing and sending supervisory instructions for banking institutions regarding the implementation of Basel III standards.

MIGHTEE: The continuum survey data release 1

Abstract The MeerKAT International GHz Tiered Extragalactic Exploration Survey (MIGHTEE) is one of the large survey projects using the MeerKAT telescope, covering four fields that have a wealth of ancillary data available. We present Data Release 1 of the MIGHTEE continuum survey, releasing total intensity images and catalogues over ∼20 deg2, across three fields at ∼1.2-1.3 GHz. This includes 4.2 deg2 over the Cosmic Evolution Survey (COSMOS) field, 14.4 deg2 over the XMM Large-Scale Structure (XMM-LSS) field and deeper imaging over 1.5 deg2 of the Extended Chandra Deep Field South (CDFS). We release images at both a lower resolution (7–9 arcsec) and higher resolution (∼5 arcsec). These images have central rms sensitivities of ∼1.3 −2.7 μJy beam−1 (∼1.2 −3.6 μJy beam−1) in the lower (higher) resolution images respectively. We also release catalogues comprised of ∼144 000 (∼114 000) sources using the lower (higher) resolution images. We compare the astrometry and flux-density calibration with the Early Science data in the COSMOS and XMM-LSS fields and previous radio observations in the CDFS field, finding broad agreement. Furthermore, we extend the source counts at the ∼10 μJy level to these larger areas (∼20 deg2) and, using the areal coverage of MIGHTEE we measure the sample variance for differing areas of sky. We find a typical sample variance of 10-20percnt for 0.3 and 0.5 sq. deg. sub-regions at S1.4 ≤ 200 μJy, which increases at brighter flux densities, given the lower source density and expected higher galaxy bias for these sources.

Photometric Redshift Estimation of Quasars by a Cross-modal Contrast Learning Method

Estimating photometric redshifts (photo-z) of quasars is crucial for measuring cosmic distances and monitoring cosmic evolution. While numerous point estimation methods have successfully determined photo-z, they often struggle with the inherently ill-posed nature of the problem and frequently overlook significant morphological features in the probability density functions (pdfs) of photo-z, such as calibration and sharpness. To address these challenges, we introduce a cross-modal contrastive learning probabilistic model that employs adversarial training, contrastive loss functions, and a mixture density network to estimate the pdf of photo-z. This method facilitates the conversion between multiband photometric data attributes, such as magnitude and color, and photometric image features, while extracting features invariant across modalities. We utilize the continuous ranked probability score (CRPS) and the probability integral transform (PIT) as metrics to assess the quality of the pdf. Our approach demonstrates robust performance across various survey bands, image qualities, and redshift distributions. Specifically, in a comprehensive data set from the Sloan Digital Sky Survey and the Wide-field Infrared Survey Explorer (WISE) survey, our probabilistic model achieved a CRPS of 0.1187. Additionally, in a combined data set from SkyMapper and WISE, it reached a CRPS of 0.0035. Our probabilistic model also produced well-calibrated PIT histograms for both data sets, indicating nearly uniform distributions. We further tested our approach in classification tasks within the SkyMapper data set. Despite the absence of u, v, and g bands, it effectively distinguished between quasars, galaxies, and stars with an accuracy of 98.96%. This versatile method can be extended to other scenarios, such as analyzing extended sources like galaxies, across different surveys and varying redshift distributions.

Exploring the limits of hierarchical world models in reinforcement learning.

Hierarchical model-based reinforcement learning (HMBRL) aims to combine the sample efficiency of model-based reinforcement learning with the abstraction capability of hierarchical reinforcement learning. While HMBRL has great potential, the structural and conceptual complexities of current approaches make it challenging to extract general principles, hindering understanding and adaptation to new use cases, and thereby impeding the overall progress of the field. In this work we describe a novel HMBRL framework and evaluate it thoroughly. We construct hierarchical world models that simulate the environment at various levels of temporal abstraction. These models are used to train a stack of agents that communicate top-down by proposing goals to their subordinate agents. A significant focus of this study is the exploration of a static and environment agnostic temporal abstraction, which allows concurrent training of models and agents throughout the hierarchy. Unlike most goal-conditioned H(MB)RL approaches, it also leads to comparatively low dimensional abstract actions. Although our HMBRL approach did not outperform traditional methods in terms of final episode returns, it successfully facilitated decision-making across two levels of abstraction. A central challenge in enhancing our method's performance, as uncovered through comprehensive experimentation, is model exploitation on the abstract level of our world model stack. We provide an in depth examination of this issue, discussing its implications and suggesting directions for future research to overcome this challenge. By sharing these findings, we aim to contribute to the broader discourse on refining HMBRL methodologies.

The geometry of correlated variability leads to highly suboptimal discriminative sensory coding.

The brain represents the world through the activity of neural populations; however, whether the computational goal of sensory coding is to support discrimination of sensory stimuli or to generate an internal model of the sensory world is unclear. Correlated variability across a neural population (noise correlations) is commonly observed experimentally, and many studies demonstrate that correlated variability improves discriminative sensory coding compared to a null model with no correlations. However, such results do not address whether correlated variability is for discriminative sensory coding. If the computational goal of sensory coding is discriminative, then correlated variability should be optimized to support that goal. We assessed optimality of noise correlations for discriminative sensory coding in diverse datasets by developing two novel null models, each with a biological interpretation. Across datasets, we found correlated variability in neural populations leads to highly suboptimal discriminative sensory coding according to both null models. Furthermore, biological constraints prevent many subsets of the neural populations from achieving optimality, and subselecting based on biological criteria leaves discriminative coding performance suboptimal. Finally, we show that optimal subpopulation are exponentially small as the population size grows. Together, these results demonstrate that the geometry of correlated variability leads to highly suboptimal discriminative sensory coding.

The Algorithmic Agent Perspective and Computational Neuropsychiatry: From Etiology to Advanced Therapy in Major Depressive Disorder

Major Depressive Disorder (MDD) is a complex, heterogeneous condition affecting millions worldwide. Computational neuropsychiatry offers potential breakthroughs through the mechanistic modeling of this disorder. Using the Kolmogorov theory (KT) of consciousness, we developed a foundational model where algorithmic agents interact with the world to maximize an Objective Function evaluating affective valence. Depression, defined in this context by a state of persistently low valence, may arise from various factors—including inaccurate world models (cognitive biases), a dysfunctional Objective Function (anhedonia, anxiety), deficient planning (executive deficits), or unfavorable environments. Integrating algorithmic, dynamical systems, and neurobiological concepts, we map the agent model to brain circuits and functional networks, framing potential etiological routes and linking with depression biotypes. Finally, we explore how brain stimulation, psychotherapy, and plasticity-enhancing compounds such as psychedelics can synergistically repair neural circuits and optimize therapies using personalized computational models.

Discovery of Six Low-z, High-luminosity, and High-mass Quasars

This study presents the discovery of six low-redshift quasars, identified through spectral observations conducted with the RTT-150 telescope. These quasars, with redshifts ranging from 0.3 to 0.6, were selected incorporating their placement in the color–color diagrams and detection by ROSAT 2RXS, focusing on those resembling the spectral energy distribution of a quasar. Our analysis includes detailed modeling of their continuum and emission line properties, which allowed us to estimate their bolometric luminosities, central black hole masses, and Eddington ratios. The findings reveal that these quasars exhibit exceptionally high luminosities and high masses for their redshift values, making them rare examples in the low-redshift quasar population. The results contribute to our understanding of quasar characteristics and their role in probing the structure and evolution of the nearby universe. This research underscores the importance of continued exploration of low-redshift quasars to enhance our knowledge of cosmic evolution and the dynamics of supermassive black holes in the early universe.

CEERS Key Paper. IX. Identifying Galaxy Mergers in CEERS NIRCam Images Using Random Forests and Convolutional Neural Networks

A crucial yet challenging task in galaxy evolution studies is the identification of distant merging galaxies, a task that suffers from a variety of issues ranging from telescope sensitivities and limitations to the inherently chaotic morphologies of young galaxies. In this paper, we use random forests and convolutional neural networks to identify high-redshift JWST Cosmic Evolution Early Release Science Survey (CEERS) galaxy mergers. We train these algorithms on simulated 3 < z < 5 CEERS galaxies created from the IllustrisTNG subhalo morphologies and the Santa Cruz SAM light cone. We apply our models to observed CEERS galaxies at 3 < z < 5. We find that our models correctly classify ∼60%–70% of simulated merging and nonmerging galaxies; better performance on the merger class comes at the expense of misclassifying more nonmergers. We could achieve more accurate classifications, as well as test for a dependency on physical parameters such as gas fraction, mass ratio, and relative orbits, by curating larger training sets. When applied to real CEERS galaxies using visual classifications as ground truth, the random forests correctly classified 40%–60% of mergers and nonmergers at 3 < z < 4 but tended to classify most objects as nonmergers at 4 < z < 5 (misclassifying ∼70% of visually classified mergers). On the other hand, the CNNs tended to classify most objects as mergers across all redshifts (misclassifying 80%–90% of visually classified nonmergers). We investigate what features the models find most useful, as well as the characteristics of false positives and false negatives, and also calculate merger rates derived from the identifications made by the models.

The Cosmic Evolution of the Supermassive Black Hole Population: A Hybrid Observed Accretion and Simulated Mergers Approach

Supermassive black holes (SMBHs) can grow through both accretion and mergers. It is still unclear how SMBHs evolve under these two channels from high redshifts to the SMBH population we observe in the local Universe. Observations can directly constrain the accretion channel but cannot effectively constrain mergers yet, while cosmological simulations provide galaxy merger information but can hardly return accretion properties consistent with observations. In this work, we combine the observed accretion channel and the simulated merger channel, taking advantage of observations and cosmological simulations, to depict a realistic evolution pattern of the SMBH population. With this methodology, we can derive the scaling relation between the black hole mass (M BH) and host-galaxy stellar mass (M ⋆), and the local black hole mass function (BHMF). Our scaling relation is lower than those based on dynamically measured M BH, supporting the claim that dynamically measured SMBH samples may be biased. We show that the scaling relation has little redshift evolution. The BHMF steadily increases from z = 4 to z = 1 and remains largely unchanged from z = 1 to z = 0. The overall SMBH growth is generally dominated by the accretion channel, with possible exceptions at high mass (M BH ≳ 108 M ⊙ or M ⋆ ≳ 1011 M ⊙) and low redshift (z ≲ 1). We also predict that around 25% of the total SMBH mass budget in the local Universe may be locked within long-lived, wandering SMBHs, and the wandering mass fraction and wandering SMBH counts increase with M ⋆.

No Redshift Evolution in the Fe ii/Mg ii Flux Ratios of Quasars across Cosmic Time

The Fe ii/Mg ii emission line flux ratio in quasar spectra serves as a proxy for the relative Fe to α-element abundances in the broad-line regions of quasars. Due to the expected different enrichment timescales of the two elements, they can be used as a cosmic clock in the early Universe. We present a study of the Fe ii/Mg ii ratios in a sample of luminous quasars exploiting high-quality near-IR spectra taken primarily by the XQR-30 program with VLT XSHOOTER. These quasars have a median bolometric luminosity of log(L bol[erg s−1]) ∼ 47.3 and cover a redshift range of z = 6.0–6.6. The median value of the measured Fe ii/Mg ii ratios is ∼7.9 with a normalized median absolute deviation of ∼2.2. In order to trace the cosmic evolution of Fe ii/Mg ii in an unbiased manner, we select two comparison samples of quasars with similar luminosities and high-quality spectra from the literature, one at intermediate redshifts (z = 3.5–4.8) and the other at low redshifts (z = 1.0–2.0). We perform the same spectral analysis for all these quasars, including the usage of the same iron template, the same spectral fitting method, and the same wavelength fitting windows. We find no significant redshift evolution in the Fe ii/Mg ii ratio over the wide redshift range from z = 1 to 6.6. The result is consistent with previous studies and supports the scenario of a rapid iron enrichment in the vicinity of accreting supermassive black holes at high redshift.

The Ubiquity of Time in Latent-cause Inference.

Humans have an outstanding ability to generalize from past experiences, which requires parsing continuously experienced events into discrete, coherent units, and relating them to similar past experiences. Time is a key element in this process; however, how temporal information is used in generalization remains unclear. Latent-cause inference provides a Bayesian framework for clustering experiences, by building a world model in which related experiences are generated by a shared cause. Here, we examine how temporal information is used in latent-cause inference, using a novel task in which participants see "microbe" stimuli and explicitly report the latent cause ("strain") they infer for each microbe. We show that humans incorporate time in their inference of latent causes, such that recently inferred latent causes are more likely to be inferred again. In particular, a "persistent" model, in which the latent cause inferred for one observation has a fixed probability of continuing to cause the next observation, explains the data significantly better than two other time-sensitive models, although extensive individual differences exist. We show that our task and this model have good psychometric properties, highlighting their potential use for quantifying individual differences in computational psychiatry or in neuroimaging studies.

From Generative AI to Objective-Driven Systems: A Paradigm Shift in Artificial Intelligence

Marwan Omar 1Illinois Institute of Technology Email: momar3@iit.edu Abstract The rapid advancement of Artificial Intelligence (AI), particularly in the domain of generative models, has led to impressive achievements in content creation and natural language processing. However, these models are inherently limited by their reliance on pattern recognition and lack of true understanding. In contrast, Objective-Driven AI offers a promising alternative by focusing on goal-oriented behavior, causal reasoning, and the development of world models. This paper explores the limitations of generative AI, highlighting its inability to grasp context, causality, and ethical considerations. It then presents the concept of Objective-Driven AI, emphasizing its potential to operate effectively in complex, real-world environments where understanding and reasoning are critical. The paper concludes with a discussion of future research directions, including advanced world modeling techniques, ethical AI, and robustness against adversarial attacks, which are essential for the further development of Objective-Driven AI systems. Keywords Objective-Driven AI, Generative AI, Causal Reasoning, World Modeling, Ethical AI, Artificial Intelligence, Adversarial Attacks, Machine Learning, Autonomous Systems

Introduction to latent variable energy-based models: a path toward autonomous machine intelligence

Abstract Current automated systems have crucial limitations that need to be addressed before artificial intelligence can reach human-like levels and bring new technological revolutions. Among others, our societies still lack level-5 self-driving cars, domestic robots, and virtual assistants that learn reliable world models, reason, and plan complex action sequences. In these notes, we summarize the main ideas behind the architecture of autonomous intelligence of the future proposed by Yann LeCun. In particular, we introduce energy-based and latent variable models and combine their advantages in the building block of LeCun’s proposal, that is, in the hierarchical joint-embedding predictive architecture.

Spectroscopic analysis of the strongly lensed SN Encore: Constraints on cosmic evolution of Type Ia supernovae

Abstract Strong gravitational lensing magnifies the light from a background source, allowing us to study these sources in detail. Here, we study the spectra of a z = 1.95 lensed Type Ia supernova SN Encore for its brightest Image A, taken 39 days apart. We infer the spectral age with template matching using the supernova identification (SNID) software and find the spectra to be at 29.0 ±5.0 and 37.4 ±2.8 rest-frame days post maximum respectively, consistent with separation in the observer frame after accounting for time-dilation. Since SNe Ia measure dark energy properties by providing relative distances between low- and high-z SNe, it is important to test for evolution of spectroscopic properties. Comparing the spectra to composite low-z SN Ia spectra, we find strong evidence for similarity between the local sample and SN Encore. The line velocities of common SN Ia spectral lines, Si II 6355 and Ca II NIR triplet are consistent with the distribution for the low-z sample as well as other lensed SNe Ia, e.g. iPTF16geu (z = 0.409)and SN H0pe (z = 1.78). The consistency between the low-z sample and lensed SNe at high-z suggests no obvious cosmic evolution demonstrating their using as high-z distance indicators, though this needs to be confirmed/refuted via a larger sample. We also find that the spectra of SN Encore match the predictions for explosion models very well. With future large samples of lensed SNe Ia e.g. with the Vera C. Rubin Observatory, spectra at such late phases will be important to distinguish between different explosion scenarios.

Gas-phase metallicity gradients in galaxies at z ∼ 6–8

The study of gas-phase metallicity and its spatial distribution at high redshift is crucial to understand the processes that shaped the growth and evolution of galaxies in the early Universe. Here we study the spatially resolved metallicity in three systems at z ∼ 6 − 8, namely A2744-YD4, BDF-3299, and COSMOS24108, with JWST NIRSpec IFU low-resolution (R ∼ 100) spectroscopic observations. These are among the highest-z sources in which metallicity gradients have been probed so far. Each of these systems hosts several spatial components in the process of merging within a few kiloparsecs, identified from the rest-frame UV and optical stellar continuum and ionised gas emission line maps. The sources have heterogeneous properties, with stellar masses log(M*/M⊙) ∼7.6–9.3, star formation rates (SFRs) ∼1–15 M⊙ yr−1, and gas-phase metallicities 12+log(O/H) ∼7.7–8.3, which exhibit a large scatter within each system. Their properties are generally consistent with those of the highest-redshift samples to date (z ∼ 3 − 10), though the sources in A2744-YD4 and COSMOS24108 are at the high end of the mass-metallicity relation (MZR) defined by the z ∼ 3 − 10 sources. Moreover, the targets in this work follow the predicted slope of the MZR at z ∼ 6 − 8 from most cosmological simulations. The gas-phase metallicity gradients are consistent with being flat in the main sources of each system. Flat metallicity gradients are thought to arise from gas mixing processes on galaxy scales, such as mergers or galactic outflows and supernova winds driven by intense stellar feedback, which wash out any gradient formed in the galaxy. The existence of flat gradients at z ∼ 6 − 8 sets also important constraints on future cosmological simulations and chemical evolution models, whose predictions on the cosmic evolution of metallicity gradients often differ significantly, especially at high redshift, but are mostly limited to z ≲ 3 so far.

Symmetric teleparallel gravity with variable deceleration parameter

In this paper, our investigation focuses on the cosmic history in modified [Formula: see text] gravity, a different theory that describes the gravitational force using a non-metricity scalar. The equations of the theory are solved by assuming a parametric form for the deceleration parameter. The parameters of the model are estimated by using the Cosmic Chronometer dataset. A number of parameters, like the deceleration parameter, whose signature changes with redshift and shows typical behavior in late-time cosmology, are subject of our analysis. We also show the evolution of other parameters, such as the equation of state parameter and energy density. The parametrization approach used here in [Formula: see text] modified gravity provides a better description of the late-time cosmic evolution without dark energy.