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This paper presents a novel perspective on correlation functions in the clustering analysis of the large-scale structure of the universe. We first recognise that pair counting in bins of radial separation is equivalent to evaluating counts-in-cells (CIC), which can be modelled using a filtered density field with a binning-window function. This insight leads to an in situ expression for the two-point correlation function (2PCF). Essentially, the core idea underlying our method is to introduce a window function to define the binning scheme, enabling pair-counting without binning. This approach develops a concept of generalised 2PCF, which extends beyond conventional discrete pair counting by accommodating non-sharp-edged window functions. To extend this framework to N-point correlation functions (NPCF) using current optimal edge-corrected estimators, we developed a binning scheme independent of the specific parameterisation of polyhedral configurations. In particular, we demonstrate a fast algorithm for the three-point correlation function (3PCF), where triplet counting is accomplished by assigning either a spherical tophat or a Gaussian filter to each vertex of triangles. Additionally, we derive analytical expressions for the 3PCF using a multipole expansion in Legendre polynomials, accounting for filtered field (binning) corrections. Numerical tests using several suites of N-body simulation samples show that our approach aligns remarkably well with the theoretical predictions. Our method provides an exact solution for quantifying binning effects in practical measurements and offers a high-speed algorithm, enabling high-order clustering analysis in extremely large datasets from ongoing and upcoming surveys such as Euclid, LSST, and DESI., Comment: 17 pages, 12 figures, submitted to MNRAS

Preheating at the end of inflation is a violent nonlinear process that efficiently transfers the energy of the inflaton to a second field, the preheat field. When the preheat field is light during inflation and its background value modulates the preheating process, the superhorizon isocurvature perturbations of the preheat field may be converted to curvature perturbations that leave an imprint on the cosmic microwave background and the large-scale structure of the universe. We use high-precision lattice simulations to study kinetic preheating after $\alpha$-attractor inflation, a case where the effective mass of the preheat field is naturally suppressed during inflation. By comparing the expansion e-folds between different Hubble patches, we find that the conversion from isocurvature perturbations to curvature perturbations is very inefficient and can hardly be detected by cosmological observations., Comment: MEET-U project II

OwnThink stands as the most extensive Chinese open-domain knowledge graph introduced in recent times. Despite prior attempts in question answering over OwnThink (OQA), existing studies have faced limitations in model representation capabilities, posing challenges in further enhancing overall accuracy in question answering. In this paper, we introduce UniOQA, a unified framework that integrates two complementary parallel workflows. Unlike conventional approaches, UniOQA harnesses large language models (LLMs) for precise question answering and incorporates a direct-answer-prediction process as a cost-effective complement. Initially, to bolster representation capacity, we fine-tune an LLM to translate questions into the Cypher query language (CQL), tackling issues associated with restricted semantic understanding and hallucinations. Subsequently, we introduce the Entity and Relation Replacement algorithm to ensure the executability of the generated CQL. Concurrently, to augment overall accuracy in question answering, we further adapt the Retrieval-Augmented Generation (RAG) process to the knowledge graph. Ultimately, we optimize answer accuracy through a dynamic decision algorithm. Experimental findings illustrate that UniOQA notably advances SpCQL Logical Accuracy to 21.2% and Execution Accuracy to 54.9%, achieving the new state-of-the-art results on this benchmark. Through ablation experiments, we delve into the superior representation capacity of UniOQA and quantify its performance breakthrough., Comment: 10 pages, 5 figures

Joint analysis of the baryon acoustic oscillations (BAO) measurement by the Dark Energy Spectroscopic Instrument (DESI) first data release, Type Ia supernovae (SNe) of the Dark Energy Survey Year 5 (DES5YR) release and cosmic microwave background (CMB) data favors a quintom-like dynamic dark energy model over the standard Lambda cold dark matter ($\Lambda$CDM) model at $3.9\sigma$ level (Adame et al. 2024). We demonstrate that the preference for dynamic dark energy does not rely on the detailed modeling of CMB physics and remains at $3.2\sigma$ level when the full CMB likelihood is replaced by a CMB acoustic-oscillation angle ($\theta_\star$) prior and a baryon abundance ($\Omega_bh^2$) prior. By comparing the data with over $10^4$ $\Lambda$CDM-based simulations, we find that both DES5YR SNe and DESI BAO contribute significantly to the preference for dynamic dark energy. The preference for dynamic dark energy is unlikely (probability $\lesssim 0.02$) due to unknown systematics in DES5YR SNe and statistical fluctuations in DESI BAO, or vice versa., Comment: 5 pages, 3 figures, MEET-U project

The clustering of galaxies and their connections to their initial conditions is a major means by which we learn about cosmology. However, the stochasticity between galaxies and their underlying matter field is a major limitation for precise measurements of galaxy clustering. Efforts have been made with an optimal weighting scheme to reduce this stochasticity using the mass-dependent clustering of dark matter haloes. Here, we show that this is not optimal. We demonstrate that the cosmic-web environments (voids, sheets, filaments \& knots) of haloes, when combined linearly with the linear bias, provide extra information for reducing stochasticity in terms of two-point statistics. Using the environmental information alone can increase the signal-to-noise of clustering by a factor of 3 better than the white-noise level at the scales of the baryon acoustic oscillations. The information about the environment and halo mass are complementary. Their combination increases the signal-to-noise by another factor of 2-3. The information about the cosmic web correlates with other properties of haloes, including halo concentrations and tidal forces -- all are related to the assembly bias of haloes., Comment: 6 pages, 3 figures; accepted for publication in MNRAS, update to match published version

The standard $\Lambda$CDM model is recently reported to deviate from the high-redshift Hubble diagram of type Ia supernovae (SNe) and quasars (QSOs) at $\sim4\sigma$ confidence level. In this work, we combine the PAge approximation (a nearly model-independent parameterization) and a high-quality QSO sample to search for the origins of the deviation. By visualizing the $\Lambda$CDM model and the marginalized $3\sigma$ constraints of SNe+QSOs into PAge space, we confirm that the SNe+QSOs constraints in both flat and non-flat PAge cases are in remarkable tension with the standard $\Lambda$CDM cosmology. Next, we investigate the tension from the perspective of redshift-evolution effects. We find that the QSO correlation coefficient $\gamma$ calibrated by SNe+low-z QSOs and SNe+high-z QSOs shows $\sim2.7\sigma$ and $\sim4\sigma$ tensions in flat and non-flat universes, respectively. The tensions for intrinsic dispersion $\delta$ between different data sets are found to be $>4\sigma$ in both flat and non-flat cases. These results indicate that the QSO luminosity correlation suffers from significant redshift evolution and non-universal intrinsic dispersion. Using a redshift-dependence correlation to build QSO Hubble diagram could lead to biases. Thus, the $\sim4\sigma$ deviation from the standard $\Lambda$CDM probably originates from the redshift-evolution effects and non-universal dispersion of the QSO luminosity correlation rather than new physics., Comment: 6 pages, 3 figures

One of the most prominent challenges to the standard Lambda cold dark matter ($\Lambda$CDM) cosmology is the tension between the structure growth parameter $S_8$ constrained by the cosmic microwave background (CMB) data and the smaller one suggested by the cosmic shear data. Recent studies show that, for $\Lambda$CDM cosmology, redshift-space distortion (RSD) data also prefers a smaller $S_8$ that is $\sim 2$-$3\sigma$ lower than the CMB value, but the result is sensitive to the cosmological model. In the present work we update the RSD constraint on $S_8$ with the most up-to-date RSD data set where the correlation between data points are properly taken into account. To reduce the model dependence, we add in our Monte Carlo Markov Chain calculation the most up-to-date data sets of Type Ia supernovae (SN) and baryon acoustic oscillations (BAO), whose correlation with RSD is also taken into account, to constrain the background geometry. For $\Lambda$CDM cosmology we find $S_8= 0.812 \pm 0.026$, which is $\sim 2\sigma$ larger than previous studies, and hence is consistent with the CMB constraint. By replacing $\Lambda$CDM with the Parameterization based on cosmic Age (PAge), an almost model-independent description of the late universe, we find that the RSD + SN + BAO constraint on $S_8$ is insensitive to the cosmological model., Comment: 6 pages, 3 figures; to appear on SCPMA

Recently, several statistically significant tensions between different cosmological datasets have raised doubts about the standard Lambda cold dark matter ($\Lambda$CDM) model. A recent letter~\citet{Huang:2020mub} suggests to use "Parameterization based on cosmic Age" (PAge) to approximate a broad class of beyond-$\Lambda$CDM models, with a typical accuracy $\sim 1\%$ in angular diameter distances at $z\lesssim 10$. In this work, we extend PAge to a More Accurate Parameterization based on cosmic Age (MAPAge) by adding a new degree of freedom $\eta_2$. The parameter $\eta_2$ describes the difference between physically motivated models and their phenomenological PAge approximations. The accuracy of MAPAge, typically of order $10^{-3}$ in angular diameter distances at $z\lesssim 10$, is significantly better than PAge. We compare PAge and MAPAge with current observational data and forecast data. The conjecture in~\citet{Huang:2020mub}, that PAge approximation is sufficiently good for current observations, is quantitatively confirmed in this work. We also show that the extension from PAge to MAPAge is important for future observations, which typically requires sub-percent accuracy in theoretical predictions., Comment: 7 pages, 3 figures