Optimal cross-correlation technique to search for strongly lensed gravitational waves

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Kopty, Anirban, Mitra, Sanjit, More, Anupreeta
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	General Relativity and Quantum Cosmology High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics Data Analysis, Statistics and Probability
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Identifying noise transients in gravitational-wave data arising from nonlinear couplings
di: Hall, Bernard, et al.
Pubblicazione: (2024)

Doppler correlation-driven vetoes for the Frequency Hough analysis in continuous gravitational-wave searches
di: Di Giovanni, Matteo, et al.
Pubblicazione: (2024)

One-stop strategy to search for long-duration gravitational-wave signals
di: Tenorio, Rodrigo, et al.
Pubblicazione: (2024)

Sampling the full hierarchical population posterior distribution in gravitational-wave astronomy
di: Mancarella, Michele, et al.
Pubblicazione: (2025)

Optimizing searches for gravitational wave bursts using coherent WaveBurst 2G
di: Martini, Alessandro, et al.
Pubblicazione: (2025)

Scalable data-analysis framework for long-duration gravitational waves from compact binaries using short Fourier transforms
di: Tenorio, Rodrigo, et al.
Pubblicazione: (2025)

py5vec: a modular Python package for the 5-vector method to search for continuous gravitational waves
di: D'Onofrio, Luca, et al.
Pubblicazione: (2026)

Learning to detect continuous gravitational waves: an open data-analysis competition
di: Tenorio, Rodrigo, et al.
Pubblicazione: (2025)

Gravitational-wave astronomy requires population-informed parameter estimation
di: Mould, Matthew, et al.
Pubblicazione: (2026)

Wavelets for power spectral density estimation of gravitational wave data
di: Zhu, Jin-Bao, et al.
Pubblicazione: (2025)

Extraction of gravitational wave signals from LISA data in the presence of artifacts
di: Castelli, Eleonora, et al.
Pubblicazione: (2024)

Glitches far from transient gravitational-wave events do not bias inference
di: Hourihane, Sophie, et al.
Pubblicazione: (2025)

Robust and scalable simulation-based inference for gravitational wave signals with gaps
di: Mao, Ruiting, et al.
Pubblicazione: (2025)

False positives for gravitational lensing: the gravitational-wave perspective
di: Keitel, David
Pubblicazione: (2024)

Deep source separation of overlapping gravitational-wave signals and non-stationary noise artifacts
di: Houba, Niklas
Pubblicazione: (2025)

LensingFlow: An Automated Workflow for Gravitational Wave Lensing Analyses
di: Wright, Mick, et al.
Pubblicazione: (2025)

Stochastic problems in pulsar timing
di: Bernardo, Reginald Christian
Pubblicazione: (2026)

Angular power spectra of anisotropic stochastic gravitational wave background: developing statistical methods and analyzing data from ground-based detectors
di: Agarwal, Deepali, et al.
Pubblicazione: (2023)

A $χ^2$ statistic for the identification of strongly lensed gravitational waves from compact binary coalescences
di: Gholap, Sudhir, et al.
Pubblicazione: (2025)

Suppressing data anomalies of gravitational reference sensors with time delay interferometry combinations
di: Wu, Pengzhan, et al.
Pubblicazione: (2022)

Phase consistency test to identify type II strongly lensed gravitational wave signals using a single event
di: Taylor, Kelsie, et al.
Pubblicazione: (2024)

Residual test to search for microlensing signatures in strongly lensed gravitational wave signals
di: Seo, Eungwang, et al.
Pubblicazione: (2025)

Calibrating gravitational-wave search algorithms with conformal prediction
di: Ashton, Gregory, et al.
Pubblicazione: (2024)

Simultaneously search for multi-target Galactic binary gravitational waves
di: Gao, Pin, et al.
Pubblicazione: (2024)

DeepExtractor: Time-domain reconstruction of signals and glitches in gravitational wave data with deep learning
di: Dooney, Tom, et al.
Pubblicazione: (2025)

Background information: a study on the sensitivity of astrophysical gravitational-wave background searches
di: Renzini, Arianna I., et al.
Pubblicazione: (2024)

An interference-based method for the detection of strongly lensed gravitational waves
di: Shan, Xikai, et al.
Pubblicazione: (2023)

Quantum Computing Tools for Fast Detection of Gravitational Waves in the Context of LISA Space Mission
di: Isfan, Maria-Catalina, et al.
Pubblicazione: (2025)

FluxMC: Rapid and High-Fidelity Inference for Space-Based Gravitational-Wave Observations
di: Liang, Bo, et al.
Pubblicazione: (2026)

Gauge Theoretic Signal Processing I: The Commutative Formalism for Single-Detector Adaptive Whitening
di: Kennington, James, et al.
Pubblicazione: (2026)

Analyzing black-hole ringdowns II: data conditioning
di: Siegel, Harrison, et al.
Pubblicazione: (2024)

Reconstruction of Black Hole Ringdown Signals with Data Gaps using a Deep-Learning Framework
di: Lai, Jing-Qi, et al.
Pubblicazione: (2025)

A novel stacked hybrid autoencoder for imputing LISA data gaps
di: Mao, Ruiting, et al.
Pubblicazione: (2024)

Advancing Space-Based Gravitational Wave Astronomy: Rapid Parameter Estimation via Normalizing Flows
di: Du, Minghui, et al.
Pubblicazione: (2023)

Wavelet Scattering Transform for Gravitational Waves Analysis. An Application to Glitch Characterization
di: Licciardi, Alessandro, et al.
Pubblicazione: (2024)

SlotFlow: Amortized Trans-Dimensional Inference with Slot-Based Normalizing Flows
di: Houba, Niklas, et al.
Pubblicazione: (2025)

PADÉ FILTERING, Principles and Use: an Introductory Report
di: Fournier, Jean-Daniel, et al.
Pubblicazione: (2024)

Rapid Parameter Estimation for Merging Massive Black Hole Binaries Using Continuous Normalizing Flows
di: Liang, Bo, et al.
Pubblicazione: (2024)

Next-generation global gravitational-wave detector network: Impact of detector orientation on compact binary coalescence and stochastic gravitational-wave background searches
di: Ebersold, Michael, et al.
Pubblicazione: (2024)

Gravitational Wave Signal Extraction Against Non-Stationary Instrumental Noises with Deep Neural Network
di: Xu, Yuxiang, et al.
Pubblicazione: (2024)