From spin noise to systematics: stochastic processes in the first International Pulsar Timing Array data release

Lentati, L.; Shannon, R. M.; Coles, W. A.; Verbiest, J. P. W.; Haasteren, Rutger van; Ellis, Justin; Caballero, R. N.; Manchester, R. N.; Arzoumanian, Zaven; Babak, S.; Bassa, C. G.; Bhat, N. D. R.; Brem, Patrick; Burgay, M.; Burke-Spolaor, S.; Champion, D. J.; Chatterjee, Shami; Cognard, I.; Cordes, J. M.; Dai, Shi; Demorest, Paul; Desvignes, G.; Dolch, Timothy; Ferdman, R. D.; Fonseca, Emmanuel; Gair, J. R.; Gonzalez, M. E.; Graikou, E.; Guillemot, L.; Hessels, J. W. T.; Hobbs, G.; Janssen, G. H.; Jones, Glenn; Karuppusamy, R.; Keith, Michael; Kerr, M.; Krämer, M.; Lam, Michael T.; Lasky, P. D.; Lassus, A.; Lazarus, P.; Lazio, T. Joseph W.; Lee, K. J.; Levin, Lina; Liu, K.; Lynch, R.; Madison, D. R.; McKee, James W.; McLaughlin, M. A.; McWilliams, S. T.; Mingarelli, Chiara M. F.; Nice, D. J.; Osłowski, S.; Pennucci, Timothy T.; Perera, B. B. P.; Perrodin, D.; Petiteau, Antoine; Possenti, A.; Ransom, S. M.; Reardon, D. J.; Rosado, P. A.; Sanidas, S.; Sesana, Alberto; Shaifullah, G.; Siemens, X.; Smits, R.; Stairs, I. H.; Stappers, B. W.; Stinebring, Daniel R.; Stovall, K.; Swiggum, Joseph K.; Taylor, Stephen R.; Theureau, G.; Tiburzi, C.; Toomey, Lawrence; Vallisneri, Michele; Straten, W. van; Vecchio, A.; Wang, J. B.; Wang, Y.; You, X. P.; Zhu, Weiwei; Zhu, X. J.

doi:10.1093/mnras/stw395

Cited by 116 publications

(175 citation statements)

References 57 publications

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“…It also includes a term that provides power at a one-year period; any periodicity found would be in addition to that already included in the model. Lentati et al (2016) do not find significant annual components in the data set, but this is not surprising as this effect is already being taken into account. Lentati et al (2017) find a covariance between temporal variations in scattering and measured DM.…”

Section: Linear Trends and Annual Periodicitiesmentioning

confidence: 76%

“…There is also the possibility that the DMs measured for the observed epochs do not agree between the sites due to the difference in observing frequencies used and spatial location; the two data sets may essentially be sampling a different ISM due to multipath scattering . Lentati et al (2016) apply a Fourier-based method of DM estimation (see also Lentati et al 2014) that allows for robust correction of DM variations during epochs without multifrequency data in the International Pulsar Timing Array (IPTA) data release (Verbiest et al 2016). The DM noise model used by Lentati et al (2016) assumes the power spectrum of the variations is in the form of a frequency-dependent power law.…”

Section: Linear Trends and Annual Periodicitiesmentioning

confidence: 99%

“…Lentati et al (2016) apply a Fourier-based method of DM estimation (see also Lentati et al 2014) that allows for robust correction of DM variations during epochs without multifrequency data in the International Pulsar Timing Array (IPTA) data release (Verbiest et al 2016). The DM noise model used by Lentati et al (2016) assumes the power spectrum of the variations is in the form of a frequency-dependent power law. It also includes a term that provides power at a one-year period; any periodicity found would be in addition to that already included in the model.…”

Section: Linear Trends and Annual Periodicitiesmentioning

confidence: 99%

“…With the increased amount of data in the IPTA data set, Lentati et al (2016) are able to sample the DM event more effectively. They identify the DM event as spanning ∼100 days, with the minimum DM occurring at ∼MJD 54757.…”

Section: Psr J1713+0747mentioning

confidence: 99%

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The NANOGrav Nine-year Data Set: Measurement and Analysis of Variations in Dispersion Measures

Jones

McLaughlin

Lam

et al. 2017

ApJ

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101

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We analyze dispersion measure (DM) variations of 37 millisecond pulsars in the nine-year North American Nanohertz Observatory for Gravitational Waves (NANOGrav) data release and constrain the sources of these variations. DM variations can result from a changing distance between Earth and the pulsar, inhomogeneities in the interstellar medium, and solar effects. Variations are significant for nearly all pulsars, with characteristic timescales comparable to or even shorter than the average spacing between observations. Five pulsars have periodic annual variations, 14 pulsars have monotonically increasing or decreasing trends, and 14 pulsars show both effects. Of the four pulsars with linear trends that have line-of-sight velocity measurements, three are consistent with a changing distance and require an overdensity of free electrons local to the pulsar. Several pulsars show correlations between DM excesses and lines of sight that pass close to the Sun. Mapping of the DM variations as a function of the pulsar trajectory can identify localized interstellar medium features and, in one case, an upper limit to the size of the dispersing region of 4 au. Four pulsars show roughly Kolmogorov structure functions (SFs), and another four show SFs less steep than Kolmogorov. One pulsar has too large an uncertainty to allow comparisons. We discuss explanations for apparent departures from a Kolmogorov-like spectrum, and we show that the presence of other trends and localized features or gradients in the interstellar medium is the most likely cause.

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Section: Linear Trends and Annual Periodicitiesmentioning

confidence: 76%

Section: Linear Trends and Annual Periodicitiesmentioning

confidence: 99%

Section: Linear Trends and Annual Periodicitiesmentioning

confidence: 99%

Section: Psr J1713+0747mentioning

confidence: 99%

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The NANOGrav Nine-year Data Set: Measurement and Analysis of Variations in Dispersion Measures

Jones

McLaughlin

Lam

et al. 2017

ApJ

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101

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“…Coles et al 2015;Keith et al 2013) that require more complex models (e.g. Lentati et al 2016) then the difference between the two models will decrease.…”

Section: Data Setmentioning

confidence: 99%

Wide-band profile domain pulsar timing analysis

Lentati

Kerr

Dai

et al. 2016

Mon. Not. R. Astron. Soc.

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We extend profile domain pulsar timing to incorporate wide-band effects such as frequencydependent profile evolution and broadband shape variation in the pulse profile. We also incorporate models for temporal variations in both pulse width and in the separation in phase of the main pulse and interpulse. We perform the analysis with both nested sampling and Hamiltonian Monte Carlo methods. In the latter case we introduce a new parameterisation of the posterior that is extremely efficient in the low signal-to-noise regime and can be readily applied to a wide range of scientific problems. We apply this methodology to a series of simulations, and to between seven and nine yr of observations for PSRs J1713+0747, J1744−1134, and J1909−3744 with frequency coverage that spans 700-3600 MHz. We use a smooth model for profile evolution across the full frequency range, and compare smooth and piecewise models for the temporal variations in DM. We find the profile domain framework consistently results in improved timing precision compared to the standard analysis paradigm by as much as 40% for timing parameters. Incorporating smoothness in the DM variations into the model further improves timing precision by as much as 30%. For PSR J1713+0747 we also detect pulse shape variation uncorrelated between epochs, which we attribute to variation intrinsic to the pulsar at a level consistent with previously published analyses. Not accounting for this shape variation biases the measured arrival times at the level of ∼30 ns, the same order of magnitude as the expected shift due to gravitational-waves in the pulsar timing band.

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