On 4 Jun 2020, at 16:39, Bill Gabella <b.gabella@vanderbilt.edu> wrote:

** [ZE] NRPyPN we use two punctures for initial data in ETK. Looking
for low ecc BBH intial data parameter solvers. Wrote one based on
literature. Option in Two Punctures that you sepcify the 8 input params
and outputs the initial data, P_t and P_r. If close to ecc =0, as close
as Post-Newtonian allows. ES-Good enough params or need to iterate?
ZE-Paper by Ramos-Buades et al. https://arxiv.org/abs/1810.00036 . They
say with one iteration can drop ecc to order 1/10^3 . RH-Good to have
the notebook in the utils folder for Two Punctures. ZE-Tedious, but the
C version is not too hard and integrate with Two Punctures. RH-Very low
eccentricity requires man iterations, a glitch shows that ecc energy
goes up. Scheme gives back P_t and P_r. PN in C does iteration zero,
but not later ones. RH&ZE-Iteration greater than 1 is not in NRPyPN but
RH's student has been doing that. RH-I will reach out to principals and
discuss this. Does two orbits and sees what to update, and then
re-submits itself, and want ecc < 1e-6 . First bit is all eccentricity
reduction. ZE-Found typos in the original paper. dE_GW/dt was very
different than other groups use. RH-Recevied their Mathematica
notebook, so hopefully better than the paper.

Some links to NRPyPN, Low-eccentricity Post-Newtonian BBH initial data
parameters (for Two Punctures):
https://nbviewer.jupyter.org/github/zachetienne/nrpytutorial/blob/master/NRPyPN/NRPyPN.ipynb
https://github.com/zachetienne/nrpytutorial/tree/master/NRPyPN (source
codes)

Hi,

I developed an infrastructure for obtaining low-eccentricity parameters for BBH. It is implemented and freely-available in SimulationTools for Mathematica. See https://bitbucket.org/simulationtools/simulationtools/src/master/EccentricityReduction.m. I haven't used it in a while, but can't think why it wouldn't still work. It lacks documentation - if someone is interested in using it or developing it further, I can see if I can find some time to write up some docs.

You can use it to generate an initial guess from PN (QuasiCircularParametersFromPostNewtonian[{m_, q_, chi1_, chi2_, om_}]) for any aligned-spin case (you can probably use chi_z for precessing cases). This will give you the separation, orbital angular momentum (r * py) and radial linear momentum (px). You then run a simulation with these parameters for a few orbits, and analyse the results. The method uses the time derivative of the radial separation for its eccentricity estimator. You use BinaryEccentricityFromSeparationDerivative, passing it the separation as a function of time (which you can get from ReadBinarySeparation), and a time window in which to measure the eccentricity. You can get a suitable window from EccentricityFitWindow, which calculates it using a simple heuristic from the initial orbital frequency to give two orbits. You can then use ReduceEccentricity with the results of BinaryEccentricityFromSeparationDerivative which gives you updated TwoPunctures parameters.

There are a number of higher level functions designed to fit this into an automated workflow. I was using it with SimFactory 3, which supports the idea of post-simulation scripts and "termination reasons". I had it set up so that when Cactus terminates during an eccentricity reduction run, it would run a script (https://bitbucket.org/simulationtools/simulationtools/src/master/Scripts/AnalyseEccentricity) which would call SimulationTools to estimate the next separation and radial momentum (I chose to keep the angular momentum L fixed, since that corresponds to fixed omega at 0 PN (maybe 1 PN?)). The post-simulation script is https://bitbucket.org/ianhinder/simfactory3/src/master/simfactory/etc/appdb/scripts/post-simulation, and it has a number of heuristics for what to do in different cases.

There are a lot of parts to this system, but it used to run extremely well, and was fully automated, and was used to produce many production simulations, including the very high spin simulations used in https://arxiv.org/abs/1810.10585, with chi_z = 0.9 and mass ratios up to 5.

It does depend on Mathematica, which is a downside, but if you have Mathematica on your cluster, you should be able to use it. Porting the algorithm and heuristics to run fully in Python would be a really nice thing for someone to do, if they have time!

If you want to use it with simfactory 2 (the ET default), you will probably need to hack your run script, or similar, to call the post-simulation script.

* BBH gallery example [AK, PS]
http://einsteintoolkit.org/gallery/bbh/index.html

** These figures are a little unintuitive and confusing in the current
order...(more description), we would like to (1) rearrange...and (2)
write...do you object to these changes?

What is confusing, and what new order would be better?

Thinks improvements to understandability of the graphs. Newest version
and try to make the XY plots, should be quite the same and produce nice
views of the horizons. And work for the GW graphics. AK-Looking at the
BBH gallery and discussion of order of the Gallery page and thinking
about for people starting the first time with these examples. Some
graphics use Wizard, some SimulationTools, some with VisIt, and have not
be able to duplicate some.

What is "Wizard"?

Leave the animation as is...cannot
regenerate yet.

Barry Wardell (barry.wardell@ucd.ie) made the movie; I'm not sure where the scripts for that are; maybe you could contact him?

So re-arrange and add text. ES-Would be good to
collect the scripts that generate these images. A single "Make" would
be nice. RH-Do these changes. ZE&BG-Looks good. BG-Would be nice to
also have the steps/scripts for each of the tools generating the
graphics in case the user does not have access to them all.

RH-Look at http://einsteintoolkit.org/gallery/bns/scripts.tar.gz

Look at https://bitbucket.org/einsteintoolkit/www/src/master/gallery/bbh/Makefile. 🙂 That builds all the plots I contributed. The VisIt ones and the movie were not so simple to script, but it might be possible given an investment of time. The VisIt ones were done by Eloisa Bentivenga (Eloisa.Bentivegna@ibm.com).