From Spin Noise to Systematics: Stochastic Processes in the First International Pulsar Timing Array Data Release
Abstract
We analyse the stochastic properties of the 49 pulsars that comprise the first International Pulsar Timing Array (IPTA) data release. We use Bayesian methodology, performing model selection to determine the optimal description of the stochastic signals present in each pulsar. In addition to spin-noise and dispersion-measure (DM) variations, these models can include timing noise unique to a single observing system, or frequency band. We show the improved radio-frequency coverage and presence of overlapping data from different observing systems in the IPTA data set enables us to separate both system and band-dependent effects with much greater efficacy than in the individual PTA data sets. For example, we show that PSR J16431224 has, in addition to DM variations, significant band-dependent noise that is coherent between PTAs which we interpret as coming from time-variable scattering or refraction in the ionised interstellar medium. Failing to model these different contributions appropriately can dramatically alter the astrophysical interpretation of the stochastic signals observed in the residuals. In some cases, the spectral exponent of the spin noise signal can vary from 1.6 to 4 depending upon the model, which has direct implications for the long-term sensitivity of the pulsar to a stochastic gravitational-wave (GW) background. By using a more appropriate model, however, we can greatly improve a pulsar's sensitivity to GWs. For example, including system and band-dependent signals in the PSR J04374715 data set improves the upper limit on a fiducial GW background by compared to a model that includes DM variations and spin-noise only.
Cite
@article{arxiv.1602.05570,
title = {From Spin Noise to Systematics: Stochastic Processes in the First International Pulsar Timing Array Data Release},
author = {L. Lentati and R. M. Shannon and W. A. Coles and J. P. W. Verbiest and R. van Haasteren and J. A. Ellis and R. N. Caballero and R. N. Manchester and Z. Arzoumanian and S. Babak and C. G. Bassa and N. D. R. Bhat and P. Brem and M. Burgay and S. Burke-Spolaor and D. Champion and S. Chatterjee and I. Cognard and J. M. Cordes and S. Dai and P. Demorest and G. Desvignes and T. Dolch and R. D. Ferdman and E. Fonseca and J. R. Gair and M. E. Gonzalez and E. Graikou and L. Guillemot and J. W. T. Hessels and G. Hobbs and G. H. Janssen and G. Jones and R. Karuppusamy and M. Keith and M. Kerr and M. Kramer and M. T. Lam and P. D. Lasky and A. Lassus and P. Lazarus and T. J. W. Lazio and K. J. Lee and L. Levin and K. Liu and R. S. Lynch and D. R. Madison and J. McKee and M. McLaughlin and S. T. McWilliams and C. M. F. Mingarelli and D. J. Nice and S. Osłowski and T. T. Pennucci and B. B. P. Perera and D. Perrodin and A. Petiteau and A. Possenti and S. M. Ransom and D. Reardon and P. A. Rosado and S. A. Sanidas and A. Sesana and G. Shaifullah and X. Siemens and R. Smits and I. Stairs and B. Stappers and D. R. Stinebring and K. Stovall and J. Swiggum and S. R. Taylor and G. Theureau and C. Tiburzi and L. Toomey and M. Vallisneri and W. van Straten and A. Vecchio and J. -B. Wang and Y. Wang and X. P. You and W. W. Zhu and X. -J. Zhu},
journal= {arXiv preprint arXiv:1602.05570},
year = {2016}
}
Comments
29 pages. 16 figures. Accepted for publication in MNRAS