Physical and Environmental Monitor injection

Project description

Physical and Environmental Monitor (PEM) injection is a way to identify environmental noise sources by intentionally injecting them, such as vibrations, large sound or electromagnetic waves.

Management tool

github: https://github.com/gw-analysis/detector-characterization/projects/1?

Communication tool

Slack

Schedule

bKAGRA phase1

Related URLs and Documents

• KAGRA Commissioning workshop 2017

  http://gwwiki.icrr.u-tokyo.ac.jp/JGWwiki/KAGRA/Commissioning/CommissioningWorkshop2017/PEMInjection

• Introduction to PEM injection

  https://dcc.ligo.org/cgi-bin/private/DocDB/ShowDocument?docid=G1702321&version=

• Sensor list
  • wiki page

    KAGRA/Subgroups/DET/EnvironmentalMonitor

  • JGW doc

    https://gwdoc.icrr.u-tokyo.ac.jp/cgi-bin/private/DocDB/ShowDocument?docid=7529

• Reference of RF noise measurement at NAOJ in 2005 (in Japanese)

  https://gwdoc.icrr.u-tokyo.ac.jp/cgi-bin/private/DocDB/ShowDocument?docid=7525

• INTRODUCTION TO PEM, Robert Schofield, 2006

  https://people.ligo-wa.caltech.edu/~robert.schofield/RobertsPEMguide/SciMonCampPEM.html

• Determining the Effect of Acoustic Coupling on Advanced LIGO

  https://dcc.ligo.org/public/0120/T1500301/001/Final%20Paper.pdf

• Characterization of transient noise in Advanced LIGO relevant to gravitational wave signal GW150914

  https://dcc.ligo.org/public/0122/P1500238/024/P1500238_GW150914_noise_characterization.pdf

• Quantifying the Effects of Acoustic Coupling on Advanced LIGO

  https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/0k225c518

• The characterization of Virgo data and its impact on gravitational-wave searches

  https://arxiv.org/abs/1203.5613

• Scattering Noise Studies at LLO via Injection Techniques

  https://dcc.ligo.org/public/0119/T1500285/001/FP.pdf

• The characterization of Virgo data and its impact on gravitational-wave searches

  https://arxiv.org/abs/1203.5613

• Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science

  https://arxiv.org/abs/1611.04596