Reproducing the Detection of GW150914: the first observation of gravitational waves from a binary black hole merger
Duncan A. Brown1, Karan Vahi2, Michela Taufer3, Von Welch4, Ewa Deelman2
1Syracuse University
2University of Southern California
3University of Tennessee Knoxville
4Indiana University
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 United States License.
In February 2016, LIGO announced the first observation of gravitational waves from merging black holes, known as GW150914. The event was first detected by a low-latency computational search that identifies candidate events, but does not provide a final estimate of the statistical significance. To establish the confidence in the detection large-scale scientific workflows were used to measure the event's significance and establish the detection confidence. These workflows used code written by the LIGO Scientific Collaboration and were executed across a range of cyberinfrastructure resources. The code to perform these analyses are publically available, but there has not yet been an attempt to directly reproduce the results, although several subsequent analyses have replicated the analysis, confirming the detection. To study the reproducability of a major scientific discovery, we attempt to reproduce the result from the compact binary coalescence search presented in the GW150914 discovery paper using publicly available code executed primarily on the Open Science Grid.
The material in this repository accompanies the paper link.
The main script for reproducing the LIGO/Virgo PyCBC GW150914 analysis is generate_workflow.sh. This script performs the following actions:
- Install a version of PyCBC that contains the tools needed to obtain data from the Gravitational Wave Open Science Center (GWOSC).
- Create a wrapper script
pycbc_losc_segment_query.sh
than has the same command line API as the LIGO DQSEGDB tools, but retrieves the metadata from GWOSC. - Create a wrapper script
minifollowup_wrapper.sh
that allows the PyCBC v1.3.2 follow-up workflows to be run using Pegasus WMS 4.9. - Download the bundled executables for the codes that create and run the workflow.
- Download the configuration file that contains the locations of the bundled exectables that are executed in the workflow and modify this file to download them from the cache on https://pegasus.isi.edu rather than the original (defunct) LIGO location.
- Set the
LIGO_DATAFIND_SERVER
environment variable to a server that indexes the GWOSC frame files stored in CVMFS. - Set the
LAL_DATA_PATH
environment variable to use data stored under CVMFS. - Run the workflow generation script with a set of
--config-overrides
that switch the workflow to:
- Use the
minifollowup_wrapper.sh
to plan the follow-up sub-workflow. - Perform the metdata segment query with the wrapper script
pycbc_losc_segment_query.sh
. - Used data stored in the channel
GWOSC-16KHZ_R1_STRAIN
from the frame type toH1_LOSC_16_V1
GWOSC frames. - Configure the segment generation code to use the GWOSC segment type
H1:RESULT:1
andL1:RESULT:1
. - Use the dummy veto definer file
H1L1-DUMMY_O1_CBC_VDEF-1126051217-1220400.xml
since the GWOSC segment wrapper obtainsSCIENCE-CAT1
analysis andCAT2
veto segments from the GWOSC data. - Set the
segments-database-url
to the GWOSC server and use the segment files generated bypycbc_losc_segment_query.sh
rather than re-generating them by setting thesegments-generate-segment-files:if_not_present
flag. - Use
/bin/true
as thesegments_from_cats
executable, as this code is not needed.
- Fix
pegasus.dir.storage.mapper.replica.file
in the sub-workflows for compatibility with Pegasus 4.9 and OSG execution. - Update the workflow to indicate that the GWOSC frame files under CVMFS are available on the OSG.
- Update the main workflow so that Pegasus is run with the optionm
--staging-site osg=local
when generating sub-workflows. - Run
pycbc_submit_dax
to plan and execute the workflow.
The second script make_pycbc_hist.sh creates PyCBC environment that can be used to run the program pycbc_dogsin_hist_sigmas_arrow that makes the result plot. It should be run with no arguments in the directory where the workflow output
directory has been created.
Running the workflow requires a system with HTCondor and Pegasus WMS 4.9 installed. The compute-intensive jobs can be run on the Open Science Grid, if the HTCondor submit host is configured to allow jobs to flock to OSG. Large memory machines are needed for the post processing jobs, as described in the paper.
Some other things to keep in mind
- The repository should be cloned to a shared fileysystem space on your local cluster. The directory where you clone the repository should be accessible on the nodes making up your local HTCondor Pool.
- The inspiral jobs are setup to run on OSG resources. For that the Pegasus workflows will be setup to transfer intermediate data and outputs to/from OSG using the SCP endpoint on your submit host.
- For SCP transfers, the Pegasus workflows will use the SSH key at this location ${HOME}/.ssh/workflow . We recommend you generate a new passwordless key and use it for this workflow.
- For the jobs to run on OSG, they need to be associated with a project. Set the environment variable OSG_PROJECT_NAME to the project (for example USC_Deelman) you are associated with.
Generation of the figures requires a LaTeX installation on the machine where make_pycbc_hist.sh
is run (for example a texlive install). In addition, the Arev Sans fonts need to be installed. To install these on a Linux texlive installation, download http://mirrors.ctan.org/fonts/arev.zip and unzip this file. Copy the tex
and fonts
directories to the appropriate place for your texlive install, e.g.
cp -R arev/tex/latex/arev /usr/share/texlive/texmf-local/texmf-compat/tex/latex
cp -R arev/fonts /usr/share/texlive/texmf-local/texmf-compat/fonts
Run the commands
mktexlsr
updmap-sys --force --enable Map=arev.map
mktexlsr
to install the extra Arev Sans fonts. The install the texlive-mathdesign
fonts by running the command
yum install texlive-mathdesign
or similar for your installation.