Search arXiv⌕ Search

arXiv · 2409.12991

Acquiring the Lefschetz thimbles: efficient evaluation of the diffraction integral for lensing in wave optics

Abstract

Evaluating the Kirchhoff-Fresnel diffraction integral is essential in studying wave effects in astrophysical lensing, but is often intractable because of the highly oscillatory integrated. A recent breakthrough was made by exploiting the Picard-Lefschetz theory: the integral can be performed along the `Lefschetz thimbles' in the complex domain where the integrand is not oscillatory but rapidly converging. The application of this method, however, has been limited by both the unfamiliar concepts involved and the low numerical efficiency of the method used to find the Lefschetz thimbles. In this paper, we give simple examples of the Lefschetz thimbles and define the `flow lines' that facilitate the understanding of the concepts. Based on this, we propose new ways to obtain the Lefschetz thimbles with high numerical efficiency, which provide an effective tool for studying wave effects in astrophysical lensing.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Xun Shi. 2024-09-19. Acquiring the Lefschetz thimbles: efficient evaluation of the diffraction integral for lensing in wave optics. https://doi.org/10.1093/mnras%2Fstae2127

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

KEEP EXPLORING

Related papers

Radial Pulsations in Polaris: A Secondary Science Application of Cherenkov Telescopes via Intensity Interferometry

Ground-based Cherenkov telescopes, which are typically inoperative during moonlit nights for gamma-ray observations, offer a valuable opportunity during this time for secondary scientific applications through Intensity Interferometry (II). Recent developments and observations suggest that implementing II instrumentation on existing and planned Imaging Atmospheric Cherenkov Telescopes (IACTs) can significantly advance optical stellar measurements. Motivated by the resurgence of II efforts over the past two decades, this work presents simulations demonstrating the estimation of stellar parameters for a radially pulsating star, such as Polaris, using either a single telescope or multiple telescopes. For single-telescope simulations, we assume that the photon pixels in the camera are mapped onto four distinct regions of the aperture, generating multiple baselines and enabling enhanced observational plane coverage. These results highlight the potential of Cherenkov telescopes in India for high-resolution optical astronomy during otherwise inoperative periods and offer promising insights into the characterization of bright stellar objects with unprecedented precision.

astro-ph.IM↗

Parallel adaptive reweighting importance sampling for Bayesian astrophysics

Efficient sampling from high-dimensional, multi-modal posteriors is a central challenge in Bayesian inference across the physical sciences. Gravitational-wave (GW) astronomy, where likelihood evaluations are computationally expensive and posteriors are complex, is one application motivating this work. Popular families of methods like Markov-chain Monte Carlo, nested sampling, and importance sampling all rely on proposal distributions to guide exploration. Because prior knowledge of the target is often limited, practitioners can adopt adaptive proposals that iteratively refine themselves using information gained from previously drawn samples. Traditional adaptive strategies, however, struggle in high-dimensional multi-modal settings: complex, non-linear correlations are hard to capture, and hyperparameters typically require tedious, problem-specific tuning. To address these issues, we introduce Parallel Adaptive Reweighting Importance Sampling (PARIS; descriptively, ``seed sampling''). PARIS models its proposal as a Gaussian mixture whose component centers are the existing samples and whose component weights match the current importance weights. New draws from the proposal therefore concentrate around high-weight regions, while candidate points in unexplored areas receive intentionally inflated weights. As the algorithm continuously reweights all samples up to the latest proposal, any initial over-weighting self-corrects once additional neighbor samples are collected. To enable rapid reweighting, we present an efficient update scheme and evaluate PARIS on illustrative toy problems and more realistic gravitational-wave parameter estimation tasks. PARIS achieves accurate posterior reconstruction and evidence estimation with substantially fewer function evaluations than competing approaches, highlighting its promise for widespread use in astrophysical data analysis.

astro-ph.IM↗

Ising noise filter: physics-informed filtering for particle detectors

We present the Ising noise filter, a highly portable, graph-based pre-filtering algorithm for early-stage background suppression in particle accelerators and astrophysical detectors. Standard noise rejection methods relying on track fitting suffer from severe combinatorial explosion. Our method bypasses this by mapping individual detector hits to a network of binary spins and minimizing an energy functional. The interaction kernels are physics-informed, tailored to the underlying physics and geometry of the experiment. We demonstrate the efficacy of this approach in two distinct experimental regimes. Applied to the Baikal-GVD neutrino telescope the filter yields fast, standard-quality noise rejection with 96.8% recall for astrophysical neutrinos. For the SPD detector at the NICA collider the filter attains recall of 97% on a toy Monte Carlo sample. Furthermore, when combined with a Peterson--Hopfield network for track finding, our physics-informed coupling improves the TrackML score from 0.5 to 0.95.

astro-ph.IM↗