Search arXiv⌕ Search

arXiv · 2611.07824

GradFlex: a Gradient-Based Method for Weak Lensing Flexion Measurement

Abstract

Flexion, the second-order gravitational lensing distortion, probes small-scale mass distribution and complement the cosmic shear measurement. However, its detection is highly challenging due to the faint signal and the contamination arising from the intrinsic galaxy morphology. In this Letter, we present GradFlex, a new method that extracts flexion from the gradient field of galaxy surface brightness distributions without assuming a specific point-spread function model or galaxy morphology. Using surface-brightness conservation, we relate the pre- and post-lensing gradient fields and derive flexion estimators from ensemble-averaged statistics of the corresponding spins. Tests on numerical simulations across diverse galaxy morphologies and multiple noise realizations demonstrate its reliability under realistic observing conditions. Combined with shear measurements, it could tighten constraints on cluster density profiles and enable more detailed studies of substructure than shear alone.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jiarui Sun, Jun Zhang, Toshifumi Futamase. 2026-11-05. GradFlex: a Gradient-Based Method for Weak Lensing Flexion Measurement. https://arxiv.org/abs/2611.07824

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Constraints on Compact Dark Matter Population from Micro-lensing Effect of Gravitational Wave for the third-generation gravitational Wave Detector

Since the first direct detection of gravitational waves (GWs) from a binary black-hole merger by the LIGO-Virgo Collaboration, GWs have emerged as a powerful probe of astrophysics and cosmology. If extragalactic compact dark matter (DM) candidates, such as primordial black holes, constitute a non-negligible fraction of the DM component over a broad mass range, they can imprint distinctive microlensing signatures on GW signals. In this work, we develop a framework to infer the population properties of extragalactic compact DM by simulating microlensing GWs in the frequency band of the third-generation ground-based GW detector, the Einstein Telescope, and analyzing them via hierarchical Bayesian inference. For a population described by a power-law mass function, we show that the detection of only several microlensing GW events among $10^4$ binary black-hole coalescences can constrain the compact DMs abundance to the level of $\sim 10^{-3}$. This forecast demonstrates that searching for and identifying microlensing signatures in future GW observations can provide a complementary approach to constraining compact DM scenarios.

astro-ph.CO↗

Clustering redshift distribution calibration of weak lensing surveys using the DESI-DR1 spectroscopic dataset

We estimate the source redshift distribution of current weak lensing surveys by applying the clustering-based redshift calibration technique, using the galaxy redshift sample provided by the Dark Energy Spectroscopic Instrument Data Release 1 (DESI-DR1). We cross-correlate the Bright Galaxy Survey (BGS), Luminous Red Galaxies (LRGs) and Emission Line Galaxies (ELGs) from DESI, within the redshift range $0.1 < z < 1.6$, with overlapping tomographic source samples from the Dark Energy Survey (DES), Kilo-Degree Survey (KiDS), and Hyper Suprime-Cam (HSC) survey. Using realistic mock catalogues, we test the stability of the clustering redshift signal to fitting scale, reference-sample choice, and the evolution of source galaxy bias. We calibrate a source galaxy bias correction from simulations with conservative (factor-of-two inflated) priors to account for modelling uncertainties, and we explicitly model and marginalise over magnification contributions, which become non-negligible at $z \gtrsim 1$ due to the depth of the DESI ELG sample. We then compare the resulting bias-weighted redshift distributions to \rossi{previously calibrated redshift distributions from the literature, based on self-organising map (SOM) techniques}, finding agreement within uncertainties for all surveys and tomographic bins. Our results demonstrate that clustering redshifts enabled by DESI's unprecedented spectroscopic sample provides a robust, complementary, and independent constraint capable of reducing one of the dominant systematic uncertainties in weak lensing cosmology.

astro-ph.CO↗

Spectral Hierarchy of the Cosmic Web

We introduce a spectral hierarchy of cosmic-web classifications obtained by applying simple scale-weighting kernels to the density field before performing a standard eigenvalue-based web classification. This unifies and extends several widely used web definitions within a single framework: the familiar potential/tidal web (large-scale, nonlocal), a curvature-based web (more local, peak- and ridge-sensitive), and additional higher-derivative levels that progressively emphasize smaller-scale structure. Because the classification is built from second derivatives of the filtered field, successive hierarchy levels align naturally with operator families that appear in renormalised bias and effective descriptions of large-scale structure, providing an explicit bridge between cosmic-web environments and long- and short-range nonlocal bias ingredients. We quantify the information content of the hierarchy with a compact statistic: we map each cell to one of four ordered web types (void, sheet, filament, knot), construct a corresponding ``web contrast'' field, and measure its cross-correlation with halos from the AbacusSummit simulation suite on a coarse mesh with $ΔL\simeq 5.5\,h^{-1}\mathrm{Mpc}$. We find that the hierarchy retains significant tracer-relevant information from very large scales down to the mesh Nyquist limit, with the more local (curvature/higher-derivative) levels dominating toward nonlinear scales. This makes the spectral hierarchy a practical, interpretable conditioning basis for fast mock-galaxy production and field-level modelling, and a flexible tool for studying environment-dependent clustering and assembly bias.

astro-ph.CO↗