#parameter estimation
22 resultsAb Initio Real-Time Gravitational-Wave Parameter Estimation
David Yallup, Metha Prathaban, James Alvey +4
The paper introduces a GPU‑optimized nested sampling algorithm that can perform rapid, full‑waveform gravitational‑wave parameter estimation for binary neutron star events, achievi…
Inferring Coupling Strengths in Synchronized Oscillators
Gug Young Kim, Hoseok Sul, Jee Woong Choi +1
The paper proposes an extended Kalman filter method to infer the unknown coupling strength in a globally coupled Kuramoto oscillator network using only the time series of the macro…
Assessing the Impact of Instrumental Requirements on the Scientific Performance of the Einstein Telescope
Ulyana Dupletsa, Francesco Iacovelli, Mikhail Korobko +23
The paper evaluates how different design choices and noise characteristics of the Einstein Telescope affect its ability to detect and analyze various gravitational‑wave sources, us…
Horizon Selection in Physics-Enhanced Neural ODEs: Theoretical Insights and Flux Linkage Application
Giulio Montecchio, Benjamin Hartmann, Sven Reimann +3
The paper investigates how the integration horizon used during training influences physics-enhanced Neural ODEs, proposing longer horizons to reduce bias in physical parameter esti…
Periodic line-of-sight velocity-driven modulations to gravitational waves emitted by compact binaries in Keplerian outer orbits
Avinash Tiwari, Shasvath J. Kapadia, Aditya Vijaykumar +1
The paper derives how a periodic line‑of‑sight velocity of a compact binary’s centre of mass, caused by a circular or eccentric outer orbit, imprints phase and amplitude modulation…
Structural identifiability of partially-observed stochastic processes: from single-particle trajectories to total particle density data
Arianna Ceccarelli, Alexander P. Browning, Ruth E. Baker
The paper presents a method to assess structural identifiability of stochastic process models, showing that parameters can be uniquely recovered from single-particle trajectory dat…
Temporal quartic variation for non-linear stochastic heat equations with piecewise constant coefficients
Yongkang Li, Yaozhong Hu, Litan Yan +1
The paper studies a stochastic heat equation with piecewise constant coefficients driven by multiplicative space‑time white noise, proving existence and uniqueness of its mild solu…
Signatures of Modified Gravity on Linear Scales in a Dynamical Dark Energy Background
Yo Toda, Adrià Gómez-Valent
The paper studies how possible deviations from General Relativity on linear scales influence cosmic structure growth in a universe with dynamical dark energy (CPL model), and uses…
Diffusion bridge with randomized initial and terminal times and its application to fish migration
Hidekazu Yoshioka, Mohammed Louriki
The paper develops a stochastic differential equation model, a diffusion bridge with random start and end times, to describe migratory fish counts in a river while accounting for e…
Non-asymptotic Bounds of Learning-based Linear MPC With Input Constraints and Unbounded Stochastic Noise
Changyi Lei, Seth Siriya, Dragan NeÅ¡iÄ +1
The paper proposes a certainty‑equivalence, switching model predictive control scheme that learns unknown linear dynamics online via regularized least‑squares, handling hard input…
Cross-Field Channel Parameter Estimation and Channel Characterization at THz Bands in Indoor Scenarios
Hengtai Chang, Cheng-Xiang Wang, Cunhua Pan +4
The paper presents indoor terahertz (260‑380 GHz) channel measurements with a virtual uniform linear array and introduces a cross‑field SAGE algorithm that jointly estimates near‑f…
Estimation Problems and the Modulating Function Method: The Algebra of Modulating Functions
Davi G. Accioli, Jerome Jouffroy
The paper analyzes the algebraic structure of modulating functions used for state, parameter, and fault estimation, introduces a simple algorithm to construct new families of modul…
Testing general relativity with amplitudes of subdominant gravitational-wave modes
Ish Gupta, Purnima Narayan, Lionel London +2
The paper introduces an improved test of general relativity that examines the amplitudes of higher-order (subdominant) gravitational-wave modes from binary black hole mergers, vali…
Learning-based Homothetic Tube MPC with Non-Asymptotic Guarantees
Changyi Lei, Seth Siriya, Dragan NeÅ¡iÄ +1
The paper proposes a model predictive control method that builds a confidence set for unknown linear system parameters using regularized least‑squares, and incorporates this set in…
Composite likelihood inference of fractional Gaussian processes with sequentially optimal subset selection
Mathis Fourreau, Matthieu Garcin
The paper proposes a composite likelihood framework for estimating parameters of fractional Gaussian processes, introducing a sequential subset selection scheme that maximizes Goda…
Tensor-Based Reduced-Order Modeling for Optimization-Based Inverse Problems
Sahidul Islam, Andreas Mang, Maxim Olshanskii
The paper introduces a tensor-train based reduced-order modeling framework that directly approximates the parameter-to-observation map for optimization-based inverse problems, enab…
James-Stein estimation for quantum sensing schemes
Luke Alexander Rhodes, Sean William Moore, Jacob A. Dunningham
The paper investigates using the James‑Stein estimator for multi‑parameter quantum sensing when only limited data are available, showing it can improve estimation without requiring…
Deep Simulation-Based Inference for Inhomogeneous Bivariate Log-Gaussian Cox Processes
Qihan Zou, Yan Wang, Tingjin Chu +1
The paper presents a two‑step simulation‑based estimation approach that first fits Poisson first‑order parameters and then uses neural networks to infer latent field parameters of…
MCMC Methods for Parameter Inference in Structurally Nonidentifiable Models
Xuyuan Wang, Donglin Han, Michael Y. Li
The paper proposes two MCMC algorithms that incorporate structural identifiability analysis to improve Bayesian parameter inference for ODE models with non-identifiable parameters.
Constraining initial orbital eccentricity of inspiral-dominated gravitational-wave events with an analytic approximant
Hemantakumar Phurailatpam, Gopakumar Achamveedu, Maria Haney +2
The paper introduces the TaylorF2Ecck frequency‑domain approximant for non‑spinning compact binaries on eccentric orbits, implements it in LALSuite, and applies it to GW170817 and…
O5 dark-siren forecasts for modified GW propagation: background robustness of the posterior
Zhaorui Zhang, Hong-Bo Jin
The paper studies how assumptions about the matter density (Ωₘ) affect the inference of the modified gravitational‑wave propagation parameter (ε) from binary‑black‑hole dark‑siren…
NeuroMem-FHP: A Likelihood-Free Deep Learning Framework for Parameter Estimation of Fractional Hawkes Process
Neha Gupta, Aditya Maheshwari
The paper introduces NeuroMem-FHP, a deep‑learning framework that uses LSTM and Transformer models to estimate the parameters of fractional Hawkes processes directly from event tim…