Image: GW memory for BBH merger calculated using several approximate methods. Inset: full merger waveform with (solid) and without (dashed) GW memory included. (Credit: Favata 2009)

• Gravitational wave (GW) memory is a non-oscillatory GW effect that results in a permanent change to the spacetime as it passes. GW memory is produced by all GW producing events with the amount of memory related to the energy radiated as GWs.
• When a great deal of GWs are produced rapidly, as in binary black hole mergers, the memory effect looks like a simple step up (or down, see figure).
• The GW memory causes the distance between the Earth and pulsars to change. This can be detected by a pulsar timing array like NANOGrav.
• Because GW memory is a generic effect, it can be used to look for exotic or unknown sources of GWs, where we don't know what the GWs will look like in advance.
• Searches for gravitational wave signals is a prime focus of NANOGrav's Gravitational Wave Detection working group.

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• NANOGrav's latest data release contains radio-pulse times of arrival (TOAs) and timing-models for 45 millisecond pulsars. Published in The Astrophysical Journal Supplement Series, April 9th 2018.
• The observations span 11.4 years, from July 30, 2005, to December 31, 2015. The pulsar with the longest baseline is J1744-1134, with 11.37 years of observations. The pulsar with the largest data volume is J1713+0747, with 27,571 TOAs.
• Observations were carried out using the 100-m Robert C. Byrd Green Bank Telescope (GBT) of the Green Bank Observatory, and the 305-m William E. Gordon Telescope (Arecibo) of Arecibo Observatory.
• In the sky map shown here, pulsar positions are marked by circles, with areas proportional to the number of TOAs in the dataset; the color scale indicates the timing baseline. The 34 pulsars with baselines greater than 3 years have solid red edges. We use only these 34 in our searches for a GW background.

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Image: sky map of NANOGrav pulsars.

Image: 95% upper limit on GW memory amplitude as a function of burst epoch, the time of the GW memory producing event. Comparison of 11-year data set under different solar system ephemerides.

• We did not detect any GW memory when analyzing the NANOGrav 11-year dataset. We used our non-detection to place limits on the strain amplitude of GW memory that could have occurred during our observation period.
• Unlike previous NANOGrav GW analyses for different sources, the results of a GW memory search are not particularly sensitive to the choice of solar system ephemeris.
• If an event produced GW memory with a strain amplitude greater than the curves shown in the figure, we would have detected it.

• From the limits on GW memory strain amplitude, we constructed a limit on the rate that GW memory producing events occur. We compared those limits to previously published limits and predictions made by others. Islo et al. (2019) determined the rate super-massive black hole binary (SMBHB) mergers that produce detectable GW memory.
• While this work was originally motivated by the prospects of detecting SMBHB merger, GW memory is a generic feature of all GW producing events. Even though we compare our rate limits to predictions about SMBHB mergers, our rate limits are agnostic to the source of GW memory.
• Because all GW producing events also produce GW memory, searches like ours could uncover new or exotic sources of GWs. Looking into the future, searches for GW memory should remain an integral part of the pulsar timing array data analysis regime. Even though they are unlikely to detect anything the prospects for the unknown are too great to pass up.

Image: 95% upper limit on the rate of memory causing events as a function of strain amplitude. left: Comparison of the 11-year data set rate upper limit to the memory rate from SMBHB mergers predicted by Islo et al. (2019). right: Comparison of the 11-year data set upper limit to the limits previously published in our previous memory analysis. Note that the blue curves are the same in both panels.

• Members of the NANOGrav Collaboration: K. Aggarwal, Z. Arzoumanian, P. T. Baker, A. Brazier, P. R. Brook, S. Burke-Spolaor, S. Chatterjee, J. M. Cordes, N. J. Cornish, F. Crawford, H. T. Cromartie, K. Crowter, M. DeCesar, P. B. Demorest, T. Dolch, J. A. Ellis, R. D. Ferdman, E. C. Ferrara, E. Fonseca, N. Garver-Daniels, P. Gentile, D. Good, J. S. Hazboun, A. M. Holgado, E. A. Huerta, K. Islo, R. Jennings, G. Jones, M. L. Jones, D. L. Kaplan, L. Z. Kelley, J. S. Key, M. T. Lam, T. J. W. Lazio, L. Levin, D. R. Lorimer, J. Luo, R. S. Lynch, D. R. Madison, M. A. McLaughlin, S. T. McWilliams, C. M. F. Mingarelli, C. Ng, D. J. Nice, T. T. Pennucci, N. S. Pol, S. M. Ransom, P. S. Ray, X. Siemens, J. Simon, R. Spiewak, I. H. Stairs, D. R. Stinebring, K. Stovall, J. K. Swiggum, S. R. Taylor, M. Vallisneri, R. Van Haasteren, S. J. Vigeland, C. A. Witt, W. W. Zhu
• Contact: Dr. Paul T. Baker (corresponding author), Dr. Scott Ransom (NANOGrav chair).

The NANOGrav Collaboration at the 2018 Spring meeting in Green Bank, WV