More Like this

1. Implementation of a generalized precession parameter in the RIFT parameter estimation algorithm

   https://doi.org/10.1088/1361-6382/ac6cc0  

   Henshaw, Chad; O’Shaughnessy, Richard; Cadonati, Laura (May 2022, Classical and Quantum Gravity)

   Abstract Since the initial discovery of gravitational waves in 2015, significant developments have been made towards waveform interpretation and estimation of compact binary source parameters. We present herein an implementation of the generalized precession parameter ⟨ χ p ⟩ [Gerosa et al 2021], which averages over all angular variations on the precession timescale, within the RIFT parameter estimation framework. Relative to the precession parameter χ p , which characterizes the single largest dynamical spin in a binary, ⟨ χ p ⟩ has a unique domain 1 < ⟨ χ p ⟩ < 2, which is exclusive to binaries with two precessing spins. After reviewing the physical differences between these two parameters, we describe how ⟨ χ p ⟩ was implemented in RIFT and apply it to all 36 events from the second half of the Advanced LIGO and Advanced Virgo third observing run (O3b). In O3b, ten events show significant amounts of precession ⟨ χ p ⟩ > 0.5. Of particular interest is GW191109_010717; we show it has a ∼ 28 % probability that the originating system necessarily contains two misaligned spins. 

   more » « less
2. Sequential Maximal Updated Density Parameter Estimation for Dynamical Systems With Parameter Drift

   https://doi.org/10.1002/nme.7618  

   del‐Castillo‐Negrete, Carlos; Spence, Rylan; Butler, Troy; Dawson, Clint (December 2024, International Journal for Numerical Methods in Engineering)

   ABSTRACT We present a novel method for generating sequential parameter estimates and quantifying epistemic uncertainty in dynamical systems within a data‐consistent (DC) framework. The DC framework differs from traditional Bayesian approaches due to the incorporation of the push‐forward of an initial density, which performs selective regularization in parameter directions not informed by the data in the resulting updated density. This extends a previous study that included the linear Gaussian theory within the DC framework and introduced the maximal updated density (MUD) estimate as an alternative to both least squares and maximum a posterior (MAP) estimates. In this work, we introduce algorithms for operational settings of MUD estimation in real‐ or near‐real time where spatio‐temporal datasets arrive in packets to provide updated estimates of parameters and identify potential parameter drift. Computational diagnostics within the DC framework prove critical for evaluating (1) the quality of the DC update and MUD estimate and (2) the detection of parameter value drift. The algorithms are applied to estimate (1) wind drag parameters in a high‐fidelity storm surge model, (2) thermal diffusivity field for a heat conductivity problem, and (3) changing infection and incubation rates of an epidemiological model. 

   more » « less
3. Oscillation inequalities in ergodic theory and analysis: one-parameter and multi-parameter perspectives

   https://doi.org/10.4171/RMI/1383  

   Mirek, Mariusz; Szarek, Tomasz Z.; Wright, James (January 2022, Revista Matemática Iberoamericana)
4. ML-based Physical Design Parameter Optimization for 3D ICs: From Parameter Selection to Optimization

   Hsiao, H; Vanna-iampikul, P;   Lu, Y; Lim, S (June 2024, ACM Design Automation Conference)
5. Secure parameter estimation: Fundamental tradeoffs

   https://doi.org/10.1109/GlobalSIP.2017.8308689  

   Sihag, Saurabh; Tajer, Ali (November 2017, Global Conference on Signal and Information Processing)