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

License

This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 United States License.

Abstract

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.

Data Products

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:

1. Install a version of PyCBC that contains the tools needed to obtain data from the Gravitational Wave Open Science Center (GWOSC).
2. 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.
3. 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.
4. Download the bundled executables for the codes that create and run the workflow.
5. 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.
6. Set the LIGO_DATAFIND_SERVER environment variable to a server that indexes the GWOSC frame files stored in CVMFS.
7. Set the LAL_DATA_PATH environment variable to use data stored under CVMFS.
8. 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 to H1_LOSC_16_V1 GWOSC frames.
• Configure the segment generation code to use the GWOSC segment type H1:RESULT:1 and L1:RESULT:1.
• Use the dummy veto definer file H1L1-DUMMY_O1_CBC_VDEF-1126051217-1220400.xml since the GWOSC segment wrapper obtains SCIENCE-CAT1 analysis and CAT2 veto segments from the GWOSC data.
• Set the segments-database-url to the GWOSC server and use the segment files generated by pycbc_losc_segment_query.sh rather than re-generating them by setting the segments-generate-segment-files:if_not_present flag.
• Use /bin/true as the segments_from_cats executable, as this code is not needed.

9. Fix pegasus.dir.storage.mapper.replica.file in the sub-workflows for compatibility with Pegasus 4.9 and OSG execution.
10. Update the workflow to indicate that the GWOSC frame files under CVMFS are available on the OSG.
11. Update the main workflow so that Pegasus is run with the optionm --staging-site osg=local when generating sub-workflows.
12. 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.

Replication notes

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.

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.