arXiv subjects

Zhengyangguang Gong

Publications and source records attributed to Zhengyangguang Gong.

8 recordsLinked to original sources

arXiv2026

Probing Parity Violation with Weak Lensing Trispectrum

We establish the weak gravitational lensing convergence trispectrum as an independent probe of cosmological parity violation in the late-time Large-Scale Structure (LSS). To map three-dimensional primordial symmetries into two-dimensional observables, we derive a generalized, compact projection formalism for the reduced angular trispectrum applicable to two classes of factorizable primordial curvature trispectra considered in this work. Applying this framework, we compute the parity-odd signal and forecast the expected Signal-to-Noise Ratio (SNR) using two phenomenological parity-violating trispectrum templates. These two templates encode the initial conditions for the late-time weak lensing observables. One template peaks at the squeezed limit, and the other template peaks at the collapsed limit. Our analysis evaluates idealized Dirac-delta source redshift distributions alongside actual tomographic profiles from the Dark Energy Survey Year 3 (DES Y3) and forecasted profiles for the Legacy Survey of Space and Time Year 10 (LSST Y10). We map the geometric sensitivity of these templates, providing physical intuition into how the resulting SNR is strongly modulated by the specific source galaxy redshift distributions and the underlying geometry of the configurations. Furthermore, we rigorously test the validity of the Limber approximation for higher-order angular statistics, demonstrating the necessity of exact line-of-sight numerical integration to capture the parity-violating signal accurately. By demonstrating the theoretical detectability of parity-breaking signatures through weak lensing, this work serves as a proof of principle, showing how upcoming weak lensing surveys can be leveraged to probe the fundamental symmetries of the early universe.

astro-ph.CO↗

arXiv2026

C3NN-SBI: Learning Hierarchies of $N$-Point Statistics from Cosmological Fields with Physics-Informed Neural Networks

Cosmological analyses are moving past the well understood 2-point statistics to extract more information from cosmological fields. A natural step in extending inference pipelines to other summary statistics is to include higher order N-point correlation functions (NPCFs), which are computationally expensive and difficult to model. At the same time it is unclear how many NPCFs one would have to include to reasonably exhaust the cosmological information in the observable fields. An efficient alternative is given by learned and optimized summary statistics, largely driven by overparametrization through neural networks. This, however, largely abandons our physical intuition on the NPCF formalism and information extraction becomes opaque to the practitioner. We design a simulation-based inference pipeline, that not only benefits from the efficiency of machine learned summaries through optimization, but also holds on to the NPCF program. We employ the heavily constrained Cosmological Correlator Convolutional Neural Network (C3NN) which extracts summary statistics that can be directly linked to a given order NPCF. We present an application of our framework to simulated lensing convergence maps and study the information content of our learned summary at various orders in NPCFs for this idealized example. We view our approach as an exciting new avenue for physics-informed simulation-based inference.

astro-ph.CO↗

arXiv2025

Clustering of the extreme: A theoretical description of weak lensing critical points power spectra in the mildly nonlinear regime

In cosmic web analysis, complementary to traditional cosmological probes, the extrema (e.g. peaks and voids) two-point correlation functions (2PCFs) are of particular interest for the study of both astrophysical phenomena and cosmological structure formation. However most previous studies constructed those statistics via N-body simulations without a robust theoretical derivation from first principles. A strong motivation exists for analytically describing the 2PCFs of these local extrema, taking into account the nonlinear gravitational evolution in the late Universe. In this paper, we derive analytical formulae for the power spectra and 2PCFs of 2D critical points, including peaks (maxima), voids (minima) and saddle points, in mildly non-Gaussian weak gravitational lensing fields. We apply a perturbative bias expansion to model the clustering of 2D critical points. We successfully derive the power spectrum of weak lensing critical points up to the next-to-next-to-leading order (NNLO) in gravitational perturbation theory, where trispectrum configurations of the weak lensing field have to be included. We numerically evaluate those power spectra up to the next-to-leading order (NLO), which correspond to the inclusion of bispectrum configurations, and transform them to the corresponding 2PCFs. An exact Monte Carlo (MC) integration is performed assuming a Gaussian distributed density field to validate our theoretical predictions. Overall, we find similar properties in 2D compared to the clustering of 3D critical points previously measured from N-body simulations. Contrary to standard lensing power spectra analysis, we find distinct BAO features in the lensing peak 2PCFs due to the gradient and curvature constraints, and we quantify that non-Gaussianity makes for ~10% of the signal at quasi-linear scales which could be important for current stage-IV surveys.

astro-ph.CO↗

arXiv2024

C3NN: Cosmological Correlator Convolutional Neural Network -- an interpretable machine learning tool for cosmological analyses

Modern cosmological research in large scale structure has witnessed an increasing number of applications of machine learning methods. Among them, Convolutional Neural Networks (CNNs) have received substantial attention due to their outstanding performance in image classification, cosmological parameter inference and various other tasks. However, many models which make use of CNNs are criticized as "black boxes" due to the difficulties in relating their outputs intuitively and quantitatively to the cosmological fields under investigation. To overcome this challenge, we present the Cosmological Correlator Convolutional Neural Network (C3NN) -- a fusion of CNN architecture with the framework of cosmological N-point correlation functions (NPCFs). We demonstrate that the output of this model can be expressed explicitly in terms of the analytically tractable NPCFs. Together with other auxiliary algorithms, we are able to open the "black box" by quantitatively ranking different orders of the interpretable convolution outputs based on their contribution to classification tasks. As a proof of concept, we demonstrate this by applying our framework to a series of binary classification tasks using Gaussian and Log-normal random fields and relating its outputs to the analytical NPCFs describing the two fields. Furthermore, we exhibit the model's ability to distinguish different dark energy scenarios ($w_0=-0.95$ and $-1.05$) using N-body simulated weak lensing convergence maps and discuss the physical implications coming from their interpretability. With these tests, we show that C3NN combines advanced aspects of machine learning architectures with the framework of cosmological NPCFs, thereby making it an exciting tool with the potential to extract physical insights in a robust and explainable way from observational data.

astro-ph.CO↗

arXiv2024

Making the leap I: Modelling the reconstructed lensing convergence PDF from cosmic shear with survey masks and systematics

The last few years have seen the development of a promising theoretical framework for statistics of the cosmic large-scale structure -- the theory of large deviations (LDT) for modelling weak-lensing one-point statistics in the mildly non-linear regime. The goal of this series of papers is to make the leap and lay out the steps to perform an actual data analysis with this theoretical tool. Building upon the LDT framework, in this work (Paper I) we demonstrate how to accurately model the Probability Distribution Function (PDF) of a reconstructed Kaiser-Squires convergence field under a realistic mask, that of the third data release of the Dark Energy Survey (DES). We also present how weak lensing systematics and higher-order lensing corrections due to intrinsic alignments, shear biases, photo-$z$ errors and baryonic feedback can be incorporated in the modelling of the reconstructed convergence PDF. In an upcoming work (Paper II) we will then demonstrate the robustness of our modelling through simulated likelihood analyses, the final step required before applying our method to actual data.

astro-ph.CO↗

arXiv2023

Cosmology from the integrated shear 3-point correlation function: simulated likelihood analyses with machine-learning emulators

The integrated shear 3-point correlation function $ζ_{\pm}$ measures the correlation between the local shear 2-point function $ξ_{\pm}$ and the 1-point shear aperture mass in patches of the sky. Unlike other higher-order statistics, $ζ_{\pm}$ can be efficiently measured from cosmic shear data, and it admits accurate theory predictions on a wide range of scales as a function of cosmological and baryonic feedback parameters. Here, we develop and test a likelihood analysis pipeline for cosmological constraints using $ζ_{\pm}$. We incorporate treatment of systematic effects from photometric redshift uncertainties, shear calibration bias and galaxy intrinsic alignments. We also develop an accurate neural-network emulator for fast theory predictions in MCMC parameter inference analyses. We test our pipeline using realistic cosmic shear maps based on $N$-body simulations with a DES Y3-like footprint, mask and source tomographic bins, finding unbiased parameter constraints. Relative to $ξ_{\pm}$-only, adding $ζ_{\pm}$ can lead to $\approx 10-25\%$ improvements on the constraints of parameters like $A_s$ (or $σ_8$) and $w_0$. We find no evidence in $ξ_{\pm} + ζ_{\pm}$ constraints of a significant mitigation of the impact of systematics. We also investigate the impact of the size of the apertures where $ζ_{\pm}$ is measured, and of the strategy to estimate the covariance matrix ($N$-body vs. lognormal). Our analysis solidifies the strong potential of the $ζ_{\pm}$ statistic and puts forward a pipeline that can be readily used to improve cosmological constraints using real cosmic shear data.

astro-ph.CO↗

arXiv2023

Beyond 3$\times$2-point cosmology: the integrated shear and galaxy 3-point correlation functions

We present the integrated 3-point correlation functions (3PCF) involving both the cosmic shear and the galaxy density fields. These are a set of higher-order statistics that describe the modulation of local 2-point correlation functions (2PCF) by large-scale features in the fields, and which are easy to measure from galaxy imaging surveys. Based on previous works on the shear-only integrated 3PCF, we develop the theoretical framework for modelling 5 new statistics involving the galaxy field and its cross-correlations with cosmic shear. Using realistic galaxy and cosmic shear mocks from simulations, we determine the regime of validity of our models based on leading-order standard perturbation theory with an MCMC analysis that recovers unbiased constraints of the amplitude of fluctuations parameter $A_s$ and the linear and quadratic galaxy bias parameters $b_1$ and $b_2$. Using Fisher matrix forecasts for a DES-Y3-like survey, relative to baseline analyses with conventional 3$\times$2PCFs, we find that the addition of the shear-only integrated 3PCF can improve cosmological parameter constraints by $20-40\%$. The subsequent addition of the new statistics introduced in this paper can lead to further improvements of $10-20\%$, even when utilizing only conservatively large scales where the tree-level models are valid. Our results motivate future work on the galaxy and shear integrated 3PCFs, which offer a practical way to extend standard analyses based on 3$\times$2PCFs to systematically probe the non-Gaussian information content of cosmic density fields.

astro-ph.CO↗

arXiv2022

"The Goose" Pulsar Wind Nebula of PSR J1016-5857: The Birth of a Plerion

We report the results of X-ray (CXO) and radio (ATCA) observations of the pulsar wind nebula (PWN) powered by the young pulsar PSR J1016--5857, which we dub "the Goose" PWN. In both bands the images reveal a tail-like PWN morphology which can be attributed to pulsar's motion. By comparing archival and new CXO observations, we measure the pulsar's proper motion $μ=28.8\pm7.3$ mas/yr, yielding a projected pulsar velocity $v \approx 440\pm110$ km/s (at d=3.2 kpc); its direction is consistent with the PWN shape. Radio emission from the PWN is polarized, with the magnetic field oriented along the pulsar tail. The radio tail connects to a larger radio structure (not seen in X-rays) which we interpret as a relic PWN (also known as a plerion). The spectral analysis of the CXO data shows that the PWN spectrum softens from $Γ=1.7$ to $Γ\approx2.3-2.5$ with increasing distance from the pulsar. The softening can be attributed to rapid synchrotron burn-off, which would explain the lack of X-ray emission from the older relic PWN. In addition to non-thermal PWN emission, we detected thermal emission from a hot plasma which we attribute to the host SNR. The radio PWN morphology and the proper motion of the pulsar suggest that the reverse shock passed through the pulsar's vicinity and pushed the PWN to one side.

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