• DOI:10.3847/0004-637X/820/1/7
• Corpus ID: 119267579

Systematic errors in low latency gravitational wave parameter estimation impact electromagnetic follow-up observations

@article{Littenberg2016SystematicEI,
  title={Systematic errors in low latency gravitational wave parameter estimation impact electromagnetic follow-up observations},
  author={Tyson B. Littenberg and Benjamin Farr and Scott Coughlin and Vicky Kalogera},
  journal={arXiv: High Energy Astrophysical Phenomena},
  year={2016}
}

• T. Littenberg, B. Farr, +1 author V. Kalogera
• Published 11 January 2016
• Geology
• arXiv: High Energy Astrophysical Phenomena

Among the most eagerly anticipated opportunities made possible by Advanced LIGO/Virgo are multimessenger observations of compact mergers. Optical counterparts may be short-lived so rapid characterization of gravitational wave (GW) events is paramount for discovering electromagnetic signatures. One way to meet the demand for rapid GW parameter estimation is to trade off accuracy for speed, using waveform models with simplified treatment of the compact objects' spin. We report on the systematic errors in GW parameter estimation suffered when using different spin approximations to recover generic signals. Component mass measurements can be biased by $>5\sigma$ using simple-precession waveforms and in excess of $20\sigma$ when non-spinning templates are employed. This suggests that electromagnetic observing campaigns should not take a strict approach to selecting which LIGO/Virgo candidates warrant follow-up observations based on low-latency mass estimates. For sky localization, we find searched areas are up to a factor of ${\sim}$2 larger for non-spinning analyses, and are systematically larger for any of the simplified waveforms considered in our analysis. Distance biases for the non-precessing waveforms can be in excess of 100\% and are largest when the spin angular momenta are in the orbital plane of the binary. We confirm that spin-aligned waveforms should be used for low-latency parameter estimation at the minimum. Including simple precession, though more computationally costly, mitigates biases except for signals with extreme precession effects. Our results shine a spotlight on the critical need for development of computationally inexpensive precessing waveforms and/or massively parallel algorithms for parameter estimation.