Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 134 tok/s
Gemini 2.5 Pro 41 tok/s Pro
GPT-5 Medium 25 tok/s Pro
GPT-5 High 26 tok/s Pro
GPT-4o 58 tok/s Pro
Kimi K2 194 tok/s Pro
GPT OSS 120B 427 tok/s Pro
Claude Sonnet 4.5 37 tok/s Pro
2000 character limit reached

Parameter control for eccentric, precessing binary black hole simulations with SpEC (2410.02997v2)

Published 3 Oct 2024 in gr-qc

Abstract: Numerical relativity simulations of merging black holes provide the most accurate description of the binary dynamics and the emitted gravitational wave signal. However, practical considerations such as imperfect initial data and initial parameters mean that achieving target parameters, such as the orbital eccentricity or the black hole spin directions, at the beginning of the usable part of the simulation is challenging. In this paper, we devise a method to produce simulations with specific target parameters, namely the Keplerian orbital parameters-eccentricity, semimajor axis, mean anomaly-and the black hole spin vectors using SpEC. The method is an extension of the current process for achieving vanishing eccentricity and it is based on a parameter control loop that iteratively numerically evolves the system, fits the orbit with analytical post-Newtonian equations, and calculates updated input parameters. Through SpEC numerical simulations, we demonstrate $\lesssim 10{-3}$ and $O(\rm degree)$ convergence for the orbital eccentricity and the spin directions respectively in $\leq7$ iterations. These tests extend to binaries with mass ratios $q \leq 3$, eccentricities $e \leq 0.65$, and spin magnitudes $|\chi | \leq 0.75$. Our method for controlling the orbital and spin parameters of numerical simulations can be used to produce targeted simulations in sparsely covered regions of the parameter space or study the dynamics of relativistic binaries.

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets

This paper has been mentioned in 2 tweets and received 3 likes.

Upgrade to Pro to view all of the tweets about this paper:

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube