arXiv subjects

Zhao Chen

Publications and source records attributed to Zhao Chen.

At least 37 records · Page 2Linked to original sources

arXiv2025

CSST Cosmological Emulator II: Generalized Accurate Halo Mass Function Emulation

Accurate theoretical prediction for halo mass function across a broad cosmological space is crucial for the forthcoming China Space Station Telescope (CSST) observations, which will capture cosmological information from multiple probes, e.g., cluster abundance, and weak lensing. In this work, we quantify the percent-level impact of different mass binning schemes when measuring the differential halo mass function from simulations, and demonstrate that the cumulative form of the halo mass function is independent of the binning scheme. Through the recently finished Kun simulation suite, we propose a generalized framework to construct multiple accurate halo mass function emulators for different halo mass definitions, including $M_{200m}$, $M_{vir}$, and $M_{200c}$. This extends our CSST Emulator to provide fast and accurate halo mass function predictions for halo mass $M\geq 10^{12}\,h^{-1}M_{\odot}$ up to $z=3.0$. For redshifts $z\leq 1.0$, the accuracy is within $2\%$ for $M\leq 10^{13}\,h^{-1}M_{\odot}$, $5\%$ for $M\leq 10^{14}\,h^{-1}M_{\odot}$, and $10\%$ for $M\leq 10^{15}\,h^{-1}M_{\odot}$, which is comparable with the statistical errors of training simulations. This tool is integrated in CSST Emulator and publicly available at https://github.com/czymh/csstemu, providing a fast and accurate theoretical tool to obtain unbiased cosmological constraints of the upcoming CSST survey.

astro-ph.CO↗

arXiv2025

Artifacts in Halo Shapes: Imprints of the Initial Condition

Grid type pre-initial conditions are commonly used to initialize particle positions in cosmological simulations. While these conditions are known to produce noticeable numerical artifacts in void regions, their impact on halo properties has generally been assumed to be negligible. In this work, we employ multiple simulations to demonstrate that grid initialization induces statistically significant artifacts in halo shapes, despite the modest absolute amplitude ($\sim 1\%$) making them unimportant for most cosmological studies. We identify a redshift-dependent artificial alignment pattern: at low redshifts ($z<2$), halo shapes preferentially orient away from the simulation box's Cartesian axes, whereas their constituent particles initially exhibit alignment with these axes. We propose a mathematical hypothesis to explain this flipping behavior.

astro-ph.CO↗

arXiv2025

CSST Cosmological Emulator III: Hybrid Lagrangian Bias Expansion Emulation of Galaxy Clustering

Galaxy clustering is an important probe in the upcoming China Space Station Telescope (CSST) survey to understand the structure growth and reveal the nature of the dark sector. However, it is a long-term challenge to model this biased tracer and connect the observable to the underlying physics. In this work, we present a hybrid Lagrangian bias expansion emulator, combining the Lagrangian bias expansion and the accurate dynamical evolution from $N$-body simulation, to predict the power spectrum of the biased tracer in real space. We employ the Kun simulation suite to construct the emulator, emulating across the space of 8 cosmological parameters including dynamic dark energy $w_0$, $w_a$, and total neutrino mass $\sum m_ν$. The sample variance due to the finite simulation box is further reduced using the Zel'dovich variance control, and it enables the precise measurement of the Lagrangian basis spectra up to the quadratic order. The emulation of basis spectra realizes 1% level accuracy, covering wavelength $ k \leq 1 \,{\rm Mpc}^{-1}h$ and redshift $0\leq z\leq 3$ up to the quadratic order field. To validate the emulator, we perform a joint fit to the halo auto power spectrum and the halo-matter cross power spectrum measured from 46 independent simulations. Depending on the choice of counterterm, the joint fit is unbiased up to $k_{\rm max}\simeq 0.7\,{\rm Mpc}^{-1}h$ within $1\sim 2$ percent accuracy, for all the redshift and halo mass samples. As part of the CSST cosmological emulator series, this emulator is expected to provide accurate theoretical predictions for the galaxy power spectrum in upcoming CSST survey.

astro-ph.CO↗

arXiv2025

Generative Data Mining with Longtail-Guided Diffusion

It is difficult to anticipate the myriad challenges that a predictive model will encounter once deployed. Common practice entails a reactive, cyclical approach: model deployment, data mining, and retraining. We instead develop a proactive longtail discovery process by imagining additional data during training. In particular, we develop general model-based longtail signals, including a differentiable, single forward pass formulation of epistemic uncertainty that does not impact model parameters or predictive performance but can flag rare or hard inputs. We leverage these signals as guidance to generate additional training data from a latent diffusion model in a process we call Longtail Guidance (LTG). Crucially, we can perform LTG without retraining the diffusion model or the predictive model, and we do not need to expose the predictive model to intermediate diffusion states. Data generated by LTG exhibit semantically meaningful variation, yield significant generalization improvements on numerous image classification benchmarks, and can be analyzed by a VLM to proactively discover, textually explain, and address conceptual gaps in a deployed predictive model.

cs.LG↗

arXiv2025

CSST Cosmological Emulator I: Matter Power Spectrum Emulation with one percent accuracy to k = 10 h/Mpc

In the near future, the China Space Station Telescope (CSST) will obtain unprecedented imaging and spectroscopic data. The statistical errors in the cosmological parameter constraints will be reduced significantly. The corresponding theoretical tools must meet the percent-level accuracy required to extract as much cosmological information as possible from the observations. We present the CSST Emulator to provide nonlinear power spectrum predictions in the eight cosmological parameter space $Ω_\mathrm{cb},Ω_\mathrm{b},H_{0},n_{s},A_{s},w_{0}, w_{a}$, and $m_ν$. It is constructed based on the Kun simulation suite, consisting of 129 high-resolution simulations with box size $L=1\,h^{-1} {\rm Gpc}$ and evolving $3072^3$ particles. The determinations of parameter ranges, sampling method, and emulation strategy in the whole construction have been optimized exquisitely. This enables our prediction for $k\leq 10\,h {\rm Mpc}^{-1}$ and $z\leq 2.0$ to reach $1\%$ accuracy validated through internal and external simulations. We also compare our results with recent BACCO, EuclidEmulator2, and Mira-Titan IV emulators, which demonstrate the CSST Emulator's excellent performance across a wide cosmological parameter range in the nonlinear regime. CSST Emulator is publicly released at https://github.com/czymh/csstemu, and provides a fundamental theoretical tool for accurate cosmological inference with future CSST observations.

astro-ph.CO↗

arXiv2025

The Jiutian simulations for the CSST extra-galactic surveys

We provide an overview of the Jiutian simulations, a hybrid simulation suite for the China Space Survey Telescope (CSST) extragalactic surveys. It consists of four complementary modules: the primary runs with high resolutions with the fiducial concordance cosmology, the emulator runs exploring the parameter uncertainties around the fiducial cosmology, the reconstruction runs intended for recovering the observed Universe position by position, and the extension runs employing extended cosmologies beyond the standard model. For the primary runs, two independent pipelines are adopted to construct subhaloes and merger trees. On top of them, four sets of mock galaxy light-cone catalogs are produced from semi-analytical models and subhalo abundance matching, providing a variety of observational properties including galaxy SED, emission lines, lensing distortions, and mock images. The 129 emulator runs are used to train the CSST emulator, achieving one percent accuracy in predicting the matter power spectrum over $k\leq 10h{\rm Mpc}^{-1}$ and $z\leq 2$. The reconstruction runs employ a number of subgrid baryonic models to predict the evolution and galaxy population resembling certain regions in the real Universe with constrained initial conditions, enabling controlled investigation of galaxy formation on top of structure formation. The extension runs cover models with warm dark matter, $f(R)$ gravity, interacting dark energy, and nonzero neutrino masses, revealing differences in the cosmic structure under alternative cosmological models. We introduce the specifications for each run, the data products derived from them, the corresponding pipeline developments, and present some main tests. Using the primary runs, we also show that the subhalo peak mass functions of different levels are approximately universal. These simulations form a comprehensive and open library for CSST surveys and beyond.

astro-ph.CO↗

arXiv2025

Robust Estimation of Double Autoregressive Models via Normal Mixture QMLE

This paper investigates the estimation of the double autoregressive (DAR) model in the presence of skewed and heavy-tailed innovations. We propose a novel Normal Mixture Quasi-Maximum Likelihood Estimation (NM-QMLE) method to address the limitations of conventional quasi-maximum likelihood estimation (QMLE) under non-Gaussian conditions. By incorporating a normal mixture distribution into the quasi-likelihood framework, NM-QMLE effectively captures both heavy-tailed behavior and skewness. A critical contribution of this paper is addressing the often-overlooked challenge of selecting the appropriate number of mixture components, $K$, a key parameter that significantly impacts model performance. We systematically evaluate the effectiveness of different model selection criteria. Under regularity conditions, we establish the consistency and asymptotic normality of the NM-QMLE estimator for DAR($p$) models. Numerical simulations demonstrate that NM-QMLE outperforms commonly adopted QMLE methods in terms of estimation accuracy, particularly when the innovation distribution deviates from normality. Our results also show that while criteria like BIC and ICL improve parameter estimation of $K$, fixing a small order of components provides comparable accuracy. To further validate its practical applicability, we apply NM-QMLE to empirical data from the S\&P 500 index and assess its performance through Value at Risk (VaR) estimation. The empirical findings highlight the effectiveness of NM-QMLE in modeling real-world financial data and improving risk assessment. By providing a robust and flexible estimation approach, NM-QMLE enhances the analysis of time series models with complex innovation structures, making it a valuable tool in econometrics and financial modeling.

stat.ME↗

arXiv2025

MRAG: A Modular Retrieval Framework for Time-Sensitive Question Answering

Understanding temporal relations and answering time-sensitive questions is crucial yet a challenging task for question-answering systems powered by large language models (LLMs). Existing approaches either update the parametric knowledge of LLMs with new facts, which is resource-intensive and often impractical, or integrate LLMs with external knowledge retrieval (i.e., retrieval-augmented generation). However, off-the-shelf retrievers often struggle to identify relevant documents that require intensive temporal reasoning. To systematically study time-sensitive question answering, we introduce the TempRAGEval benchmark, which repurposes existing datasets by incorporating temporal perturbations and gold evidence labels. As anticipated, all existing retrieval methods struggle with these temporal reasoning-intensive questions. We further propose Modular Retrieval (MRAG), a trainless framework that includes three modules: (1) Question Processing that decomposes question into a main content and a temporal constraint; (2) Retrieval and Summarization that retrieves evidence and uses LLMs to summarize according to the main content; (3) Semantic-Temporal Hybrid Ranking that scores each evidence summarization based on both semantic and temporal relevance. On TempRAGEval, MRAG significantly outperforms baseline retrievers in retrieval performance, leading to further improvements in final answer accuracy.

cs.CL↗

arXiv2025

Interpretable Deep Regression Models with Interval-Censored Failure Time Data

Deep neural networks (DNNs) have become powerful tools for modeling complex data structures through sequentially integrating simple functions in each hidden layer. In survival analysis, recent advances of DNNs primarily focus on enhancing model capabilities, especially in exploring nonlinear covariate effects under right censoring. However, deep learning methods for interval-censored data, where the unobservable failure time is only known to lie in an interval, remain underexplored and limited to specific data type or model. This work proposes a general regression framework for interval-censored data with a broad class of partially linear transformation models, where key covariate effects are modeled parametrically while nonlinear effects of nuisance multi-modal covariates are approximated via DNNs, balancing interpretability and flexibility. We employ sieve maximum likelihood estimation by leveraging monotone splines to approximate the cumulative baseline hazard function. To ensure reliable and tractable estimation, we develop an EM algorithm incorporating stochastic gradient descent. We establish the asymptotic properties of parameter estimators and show that the DNN estimator achieves minimax-optimal convergence. Extensive simulations demonstrate superior estimation and prediction accuracy over state-of-the-art methods. Applying our method to the Alzheimer's Disease Neuroimaging Initiative dataset yields novel insights and improved predictive performance compared to traditional approaches.

stat.ML↗

arXiv2025

Chemistry-aware battery degradation prediction under simulated real-world cyclic protocols

Battery degradation is governed by complex and randomized cyclic conditions, yet existing modeling and prediction frameworks usually rely on rigid, unchanging protocols that fail to capture real-world dynamics. The stochastic electrical signals make such prediction extremely challenging, while, on the other hand, they provide abundant additional information, such as voltage fluctuations, which may probe the degradation mechanisms. Here, we present chemistry-aware battery degradation prediction under dynamic conditions with machine learning, which integrates hidden Markov processes for realistic power simulations, an automated batch-testing system that generates a large electrochemical dataset under randomized conditions, an interfacial chemistry database derived from high-throughput X-ray photoelectron spectroscopy for mechanistic probing, and a machine learning model for prediction. By automatically constructing a polynomial-scale feature space from irregular electrochemical curves, our model accurately predicts both battery life and critical knee points. This feature space also predicts the composition of the solid electrolyte interphase, revealing six distinct failure mechanisms-demonstrating a viable approach to use electrical signals to infer interfacial chemistry. This work establishes a scalable and adaptive framework for integrating chemical engineering and data science to advance noninvasive diagnostics and optimize processes for more durable and sustainable energy storage technologies.

eess.SP↗

arXiv2025

High-throughput calculations of two-dimensional auxetic $M_4X_8$ with magnetism, electrocatalysis, and alkali metal battery applications

Two-dimensional (2D) materials with multifunctional properties, such as negative Poisson's ratio (NPR), magnetism, catalysis, and energy storage capabilities, are of significant interest for advanced applications in flexible electronics, spintronics, catalysis, and lithium-ion batteries. However, the discovery of such materials, particularly in low-dimensional forms, remains a challenge. In this study, we perform high-throughput density-functional theory (DFT) calculations to explore a new class of 2D V-shaped monolayers with remarkable physicochemical properties. Among 18 stable $M_4X_8$ (M = transition metal; X = halogen) compounds, we identify 9 auxetic monolayers, with \ce{Pd4I8} standing out for its exceptionally high NPR of -0.798. Notably, 4 of these materials exhibit half semiconductor properties, while 5 others are bipolar magnetic semiconductors, offering a unique combination of electronic and magnetic behavior. Additionally, these materials demonstrate promising catalytic activity for hydrogen and oxygen evolution reactions (HER/OER) and show potential as anodes for rechargeable metal-ion batteries, particularly in alkali-ion systems. This work not only expands the family of 2D NPR materials but also introduces new candidates with multifunctional capabilities for a wide range of applications in nanoelectronics, catalysis, and energy storage.

cond-mat.mes-hall↗

arXiv2024

High-throughput Screening of Ferrimagnetic Semiconductors With Ultrahigh N$\acute{e}$el Temperature

Ferrimagnetic semiconductors, integrated with net magnetization, antiferromagnetic coupling and semi-conductivity, have constructed an ideal platform for spintronics. For practical applications, achieving high N$\acute{e}$el temperatures ($T_{\mathrm{N}}$) is very desirable, but remains a significant challenge. Here, via high-throughput density-functional-theory calculations, we identify 19 intrinsic ferrimagnetic semiconductor candidates from nearly 44,000 structures in the Materials Project database, including 10 ferrimagnetic bipolar magnetic semiconductors (BMS) and 9 ferrimagnetic half semiconductors (HSC). Notably, the BMS \ce{NaFe5O8} possesses a high $T_{\mathrm{N}}$ of 768 K. By element substitutions, we obtain an HSC \ce{NaFe5S8} with a $T_{\mathrm{N}}$ of 957 K and a BMS \ce{LiFe5O8} with a $T_{\mathrm{N}}$ reaching 1059 K. Our results pave a promising avenue toward the development of ferrimagnetic spintronics at ambient temperature.

cond-mat.mtrl-sci↗

arXiv2024

Box Replication Effects in Weak Lensing Light-cone Construction

Weak gravitational lensing simulations serve as indispensable tools for obtaining precise cosmological constraints. In particular, it is crucial to address the systematic uncertainties in theoretical predictions, given the rapid increase in galaxy numbers and the reduction in observational noise. Both on-the-fly and post-processing methods for constructing lensing light-cones encounter limitations due to the finite simulated volume, necessitating the replication of the simulation box to encompass the volume to high redshifts. To address this issue, our primary focus lies on investigating and quantifying the impact of box replication on the convergence power spectrum and higher-order moments of lensing fields. Subsequently, a univariate model is utilized to estimate the amplitude parameter $A$ by fitting four statistics measured from partial-sky light-cones along specific angles, to the averaged result from random directions. The investigation demonstrates that the systematic bias stemming from the box replication phenomenon falls within the bounds of statistical errors for the majority of cases. However, caution should be exercised when considering high-order statistics on a small sky coverage ($\lesssim 25~\mathrm{deg^2}$). For this case, we have developed a code that facilitates the identification of optimal viewing angles for the light-cone construction. This code has been made publicly accessible at https://github.com/czymh/losf.

astro-ph.CO↗

arXiv2024

HDR Imaging for Dynamic Scenes with Events

High dynamic range imaging (HDRI) for real-world dynamic scenes is challenging because moving objects may lead to hybrid degradation of low dynamic range and motion blur. Existing event-based approaches only focus on a separate task, while cascading HDRI and motion deblurring would lead to sub-optimal solutions, and unavailable ground-truth sharp HDR images aggravate the predicament. To address these challenges, we propose an Event-based HDRI framework within a Self-supervised learning paradigm, i.e., Self-EHDRI, which generalizes HDRI performance in real-world dynamic scenarios. Specifically, a self-supervised learning strategy is carried out by learning cross-domain conversions from blurry LDR images to sharp LDR images, which enables sharp HDR images to be accessible in the intermediate process even though ground-truth sharp HDR images are missing. Then, we formulate the event-based HDRI and motion deblurring model and conduct a unified network to recover the intermediate sharp HDR results, where both the high dynamic range and high temporal resolution of events are leveraged simultaneously for compensation. We construct large-scale synthetic and real-world datasets to evaluate the effectiveness of our method. Comprehensive experiments demonstrate that the proposed Self-EHDRI outperforms state-of-the-art approaches by a large margin. The codes, datasets, and results are available at https://lxp-whu.github.io/Self-EHDRI.

cs.CV↗

arXiv2023

Deformations and abelian extensions on anti-pre-Lie algebras

In this paper, we introduce the representation of anti-pre-Lie algebras and give the second cohomology group of anti-pre-Lie algebras. As applications, first, we study linear deformations of anti-pre-Lie algebras. The notion of a Nijenhuis operator on an anti-pre-Lie algebra is introduced which can generate a trivial linear deformation of an anti-pre-Lie algebra. Then, we study formal deformations of anti-pre-Lie algebras. We show that the infinitesimal of a formal deformation is a 2-cocycle with the coefficients in the regular representation and depends only on its cohomology class. Moreover, if the second cohomology group $H^2(A;A)$ is trivial, then the anti-pre-Lie algebra is rigid. Finally, we introduce the notion of abelian extensions. We show that abelian extensions are classified by the second cohomology group $H^2(A;V)$.

math.RA↗

arXiv2023

SHIFT3D: Synthesizing Hard Inputs For Tricking 3D Detectors

We present SHIFT3D, a differentiable pipeline for generating 3D shapes that are structurally plausible yet challenging to 3D object detectors. In safety-critical applications like autonomous driving, discovering such novel challenging objects can offer insight into unknown vulnerabilities of 3D detectors. By representing objects with a signed distanced function (SDF), we show that gradient error signals allow us to smoothly deform the shape or pose of a 3D object in order to confuse a downstream 3D detector. Importantly, the objects generated by SHIFT3D physically differ from the baseline object yet retain a semantically recognizable shape. Our approach provides interpretable failure modes for modern 3D object detectors, and can aid in preemptive discovery of potential safety risks within 3D perception systems before these risks become critical failures.

cs.CV↗

arXiv2022

Self-calibration of photometric redshift scatter from DECaLS DR8 power spectrum and validation with simulated catalogues

The uncertainty in the photometric redshift estimation is one of the major systematics in weak lensing cosmology. The self-calibration method is able to reduce this systematics without assuming strong priors. We improve the recently proposed self-calibration algorithm to enhance the stability and robustness with the noisy measurement. The improved algorithm is tested on the power spectra measured from the simulated catalogues constructed according to DECaLS DR8 photometric catalogue. For the fiducial analysis with 5 equal-width redshift bins over $0<z<1$ and 6 bands over scales $100 \leq \ell<1000$, we find that the improved algorithm successfully reconstructs the scatter rates and the auto power spectrum in true redshift bins at the level of $\sim0.015$ and $\sim4.4$ per cent, respectively. The bias of the mean redshift is reduced by more than 50 per cent compared to the photo-$z$ without self-calibration, especially for the cases with catastrophic photo-$z$ errors. The reconstructed results of DECaLS DR8 galaxy sample are in line with the expectations from the simulation validation. The self-calibration code is publicly available at https://github.com/PengHui-hub/FP-NMF.

astro-ph.CO↗

arXiv2022

Structural Inference of Networked Dynamical Systems with Universal Differential Equations

Networked dynamical systems are common throughout science in engineering; e.g., biological networks, reaction networks, power systems, and the like. For many such systems, nonlinearity drives populations of identical (or near-identical) units to exhibit a wide range of nontrivial behaviors, such as the emergence of coherent structures (e.g., waves and patterns) or otherwise notable dynamics (e.g., synchrony and chaos). In this work, we seek to infer (i) the intrinsic physics of a base unit of a population, (ii) the underlying graphical structure shared between units, and (iii) the coupling physics of a given networked dynamical system given observations of nodal states. These tasks are formulated around the notion of the Universal Differential Equation, whereby unknown dynamical systems can be approximated with neural networks, mathematical terms known a priori (albeit with unknown parameterizations), or combinations of the two. We demonstrate the value of these inference tasks by investigating not only future state predictions but also the inference of system behavior on varied network topologies. The effectiveness and utility of these methods is shown with their application to canonical networked nonlinear coupled oscillators.

math.DS↗