arXiv subjects

Alan Junzhe Zhou

Publications and source records attributed to Alan Junzhe Zhou.

6 recordsLinked to original sources

arXiv2025

Forecasting Dark Matter Subhalo Constraints from Stellar Streams using Implicit Likelihood Inference

The evidence for dark matter (DM) remains compelling, although attempts to understand its particle nature remain inconclusive. One promising method to study DM is detecting DM subhalos through their gravitational interactions with stellar streams. In this study, we apply Neural Posterior Estimation (NPE) to constrain subhalo interaction parameters, including mass, scale radius, velocity, and encounter geometry, from stellar stream kinematics. We generate particle spray simulations based on the Lagrange Cloud stripping technique, focusing on the ATLAS-Aliqa Uma stream as a test case. We train multiple NPE models across multiple observational scenarios, quantifying how kinematic completeness affects inference and forecasting constraints from upcoming surveys including LSST, 4MOST, and 10-year Gaia data. Our results demonstrate that NPE can produce accurate and well-calibrated posteriors. In the idealized case with full 6D coordinates, we achieve subhalo mass uncertainties of 15-20% for a $10^7 \, \mathrm{M_\odot}$ subhalo, with 5D coordinates (excluding radial velocities) achieving similar performance. Under realistic observational conditions, mass uncertainties range from 50% (present-day) to 20-40% (future scenarios), with comparable performance between the photometric-only LSST sample and a smaller sample that includes Gaia proper motions and 4MOST radial velocities. Most notably, we find that velocity bimodality emerges when phase space is poorly sampled, whether due to missing kinematic information or limited stellar tracers. Combining large photometric samples with targeted spectroscopic follow-up can effectively resolves this degeneracy. These results demonstrate the power of implicit likelihood inference for optimizing stellar stream observational strategies and forecasting DM subhalo constraints from upcoming surveys.

astro-ph.GA↗

arXiv2025

The isotropy of Hubble expansion in the early and late Universe

We test the isotropy of Hubble expansion by combining several probes for the first time, constructing full-sky maps of expansion rate variation using Type Ia supernovae, fundamental plane galaxies, and CMB temperature fluctuations. We find no hint of anisotropy or correlation between early- and late-Universe expansion across all systematic models. The 99% confidence upper limits on expansion rate anisotropy are 0.39% for low-redshift supernovae, 0.95% for high-redshift CMB, and 0.37% when combined at a 60-degree smoothing scale. A significant anomaly in the fundamental plane residual map may reflect systematics in the current DESI dataset, as evidenced by the absence of cross-correlation with other tracers and its correlation with spatial density variations.

astro-ph.CO↗

arXiv2025

Map-level baryonification: unified treatment of weak lensing two-point and higher-order statistics

Precision cosmology benefits from extracting maximal information from cosmic structures, motivating the use of higher-order statistics (HOS) at small spatial scales. However, predicting how baryonic processes modify matter statistics at these scales has been challenging. The baryonic correction model (BCM) addresses this by modifying dark-matter-only simulations to mimic baryonic effects, providing a flexible, simulation-based framework for predicting both two-point and HOS. We show that a 3-parameter version of the BCM can jointly fit weak lensing maps' two-point statistics, wavelet phase harmonics coefficients, scattering coefficients, and the third and fourth moments to within 2% accuracy across all scales $\ell < 2000$ and tomographic bins for a DES-Y3-like redshift distribution ($z \lesssim 2$), using the FLAMINGO simulations. These results demonstrate the viability of BCM-assisted, simulation-based weak lensing inference of two-point and HOS, paving the way for robust cosmological constraints that fully exploit non-Gaussian information on small spatial scales.

astro-ph.CO↗

arXiv2024

A Hamiltonian, post-Born, three-dimensional, on-the-fly ray tracing algorithm for gravitational lensing

The analyses of the next generation cosmological surveys demand an accurate, efficient, and differentiable method for simulating the universe and its observables across cosmological volumes. We present Hamiltonian ray tracing (HRT) -- the first post-Born (accounting for lens-lens coupling and without relying on the Born approximation), three-dimensional (without assuming the thin-lens approximation), and on-the-fly (applicable to any structure formation simulations) ray tracing algorithm based on the Hamiltonian formalism. HRT performs symplectic integration of the photon geodesics in a weak gravitational field, and can integrate tightly with any gravity solver, enabling co-evolution of matter particles and light rays with minimal additional computations. We implement HRT in the particle-mesh library $\texttt{pmwd}$, leveraging hardware accelerators such as GPUs and automatic differentiation capabilities based on $\texttt{JAX}$. When tested on a point-mass lens, HRT achieves sub-percent accuracy in deflection angles above the resolution limit across both weak and moderately strong lensing regimes. We also test HRT in cosmological simulations on the convergence maps and their power spectra.

astro-ph.CO↗

arXiv2024

Accurate field-level weak lensing inference for precision cosmology

We present $\texttt{Miko}$, a catalog-to-cosmology pipeline for general flat-sky field-level inference, which provides access to cosmological information beyond the two-point statistics. In the context of weak lensing, we identify several new field-level analysis systematics (such as aliasing, Fourier mode-coupling, and density-induced shape noise), quantify their impact on cosmological constraints, and correct the biases to a percent level. Next, we find that model misspecification can lead to both absolute bias and incorrect uncertainty quantification for the inferred cosmological parameters in realistic simulations. The Gaussian map prior infers unbiased cosmological parameters, regardless of the true data distribution, but it yields overconfident uncertainties. The log-normal map prior quantifies the uncertainties accurately, although it requires careful calibration of the shift parameters for unbiased cosmological parameters. We demonstrate systematics control down to the $2\%$ level for both models, making them suitable for ongoing weak lensing surveys.

astro-ph.CO↗

arXiv2023

Field-level multiprobe analysis of the CMB, integrated Sachs-Wolfe effect, and the galaxy density maps

Extracting information from cosmic surveys is often done in a two-step process, construction of maps and then summary statistics such as two-point functions. We use simulations to demonstrate the advantages of a general Bayesian framework that consistently combines different cosmological experiments on the field level, and reconstructs both the maps and cosmological parameters. We apply our method to jointly reconstruct the primordial CMB, the integrated Sachs-Wolfe effect, and six tomographic galaxy density maps on the full sky on large scales along with several cosmological parameters. While the traditional maximum a posterior estimator has both two-point level and field-level bias, the new approach yields unbiased cosmological constraints and improves the signal-to-noise ratio of the maps.

astro-ph.CO↗