thank you for spearheading the latest release of the Einstein Toolkit. This is important work for this community, and we very much appreciate it.

We noticed that Giuseppe Ficarra (in cc) is not listed as contributor nor in the release team.
Giuseppe is a co-author of the new Einstein Toolkit thorn "TwoPunctures_BBHSF."

As per best scientific practices to credit scientific work, I would ask you to please add him as contributor and member of the release team on the Einstein Toolkit website and in the release notes.

Release Announcement

Click here to read the announcement in HTML (with hyperlinks):

https://einsteintoolkit.org/about/releases/ET_2023_05_announcement.html

We are pleased to announce the twenty-sixth release (code name "Karl

Schwarzschild") of the Einstein Toolkit, an open-source,

community-developed software infrastructure for relativistic

astrophysics. The major changes in this release include:

* kuibit 1.4.0, which, in addition to bug fixes, improves support

for working with horizon data, timer data, and higher rank objects

(See release notes for more details).

Two new arrangements and thorns have been added:

* The FUKA reader for numerical relativity initial data

* TwoPunctures_BBHSF for binary black hole with scalar fields

initial data

New capabilities for existing codes:

* Baikal and BaikalVacuum have been updated to have READ/WRITE

declarations for automated ghost zone synchronization. Several

variable groups (such as RHS variables) are also no longer

included in checkpoint/restart operations.

* particle_tracerET can now output the particles' four-velocity

(contravariant and covariant); added support for binary output

format; removed limit on the number of tracer particles

In addition, bug fixes accumulated since the previous release in

November 2022 have been included.

The Einstein Toolkit is a collection of software components and tools

for simulating and analyzing general relativistic astrophysical systems

that builds on numerous software efforts in the numerical relativity

community, including code to compute initial data parameters, the

spacetime evolution codes Baikal, lean_public, and McLachlan, analysis

codes to compute horizon characteristics and gravitational waves, the

Carpet AMR infrastructure, and the relativistic magneto-hydrodynamics

codes GRHydro and IllinoisGRMHD. Data analysis and post-processing is

handled by the kuibit library. The Einstein Toolkit also contains a 1D

self-force code. For parts of the toolkit, the Cactus Framework is used

as the underlying computational infrastructure, providing large-scale

parallelization, general computational components, and a model for

collaborative, portable code development.

The Einstein Toolkit uses a distributed software model, and its

different modules are developed, distributed, and supported either by

the core team of Einstein Toolkit Maintainers or by individual groups.

Where modules are provided by external groups, the Einstein Toolkit

Maintainers provide quality control for modules for inclusion in the

toolkit and help coordinate support. The Einstein Toolkit Maintainers

currently involve staff and faculty from five different institutions and

host weekly meetings that are open for anyone to join.

Guiding principles for the design and implementation of the toolkit

include: open, community-driven software development; well thought-out

and stable interfaces; separation of physics software from computational

science infrastructure; provision of complete working production code;

training and education for a new generation of researchers.

For more information about using or contributing to the Einstein

Toolkit, or to join the Einstein Toolkit Consortium, please visit our

web pages at http://einsteintoolkit.org, or contact the users mailing

list users@einsteintoolkit.org.

The Einstein Toolkit is primarily supported by NSF

2004157/2004044/2004311/2004879/2003893 (Enabling fundamental research

in the era of multi-messenger astrophysics).

The Einstein Toolkit contains about 350 regression test cases. On a

large portion of the tested machines, almost all of these tests pass,

using both MPI and OpenMP parallelization.

Deprecated functionality

There are no features marked as deprecated in this release, to be

removed in the next release.

Contributors

Among the many contributors to the Einstein Toolkit and to this release

in particular, important contributions to new components were made by

the following authors:

* Cheng-Hsin Cheng

* Gabriele Bozzola

* Leonardo Werneck

* Ludwig Jens Papenfort

* Samuel Cupp

* Samuel Tootle

How to upgrade from Kowalevski Release (ET_2022_11)

To upgrade from the previous release, use GetComponents with the new

thornlist to check out the new version.

See the Download page (http://einsteintoolkit.org/download.html) on the

Einstein Toolkit website for download instructions.

The SelfForce-1D code uses a single git repository; thus, using

git pull; git checkout ET_2023_05

will update the code.

To install Kuibit, do the following:

pip install --user -U kuibit==1.4.0

Machine notes

Supported (tested) machines include:

* Debian, Ubuntu, Fedora, Mint, OpenSUSE, and macOS X installations

with dependencies installed as prescribed in the official

installation instructions

* Deep Bayou

* Delta

* Expanse

* Frontera

* Golub

* Queen Bee 2

* Queen Bee 3

* Stampede 2

* Summit

* Sunrise

* SuperMIC

* SuperMUC

* ThornyFlat

Note for individual machines:

* TACC machines: defs.local.ini needs to have `sourcebasedir =

$WORK` and `basedir = $SCRATCH/simulations` configured for this

machine. You need to determine $WORK and $SCRATCH by logging in to

the machine.

* SuperMUC-NG: defs.local.ini needs to have `sourcebasedir = $HOME`

and `basedir = $SCRATCH/simulations` configured for this machine.

You need to determine $HOME and $SCRATCH by logging in to the

machine.

All repositories participating in this release carry a branch ET_2023_05

marking this release. These release branches will be updated if severe

errors are found.

The "Karl Schwarzschild" Release Team on behalf of the Einstein Toolkit

Consortium (2023-05-24)

* Leonardo Werneck

* Samuel Cupp

* Beyhan Karakas

* Bing-Jyun Tsao

* Cheng-Hsin Cheng

* Jake Doherty

* Konrad Topolski

* Peter Diener

* Roland Haas

* Steven R. Brandt

* Terrence Pierre Jacques

* Thiago Assumpcao

* Zachariah Etienne

May 24, 2023

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