2023A&A...678A..49E


Query : 2023A&A...678A..49E

2023A&A...678A..49E - Astronomy and Astrophysics, volume 678A49 (2023/10-1)

The second data release from the European Pulsar Timing Array II. Customised pulsar noise models for spatially correlated gravitational waves.

EPTA COLLABORATION AND INPTA COLLABORATION, ANTONIADIS J., ARUMUGAM P., ARUMUGAM S., BABAK S., BAGCHI M., BAK NIELSEN A.-S., BASSA C.G., BATHULA A., BERTHEREAU A., BONETTI M., BORTOLAS E., BROOK P.R., BURGAY M., CABALLERO R.N., CHALUMEAU A., CHAMPION D.J., CHANLARIDIS S., CHEN S., COGNARD I., DANDAPAT S., DEB D., DESAI S., DESVIGNES G., DHANDA-BATRA N., DWIVEDI C., FALXA M., FERDMAN R.D., FRANCHINI A., GAIR J.R., GONCHAROV B., GOPAKUMAR A., GRAIKOU E., GRIESSMEIER J.-M., GUILLEMOT L., GUO Y.J., GUPTA Y., HISANO S., HU H., IRACI F., IZQUIERDO-VILLALBA D., JANG J., JAWOR J., JANSSEN G.H., JESSNER A., JOSHI B.C., KAREEM F., KARUPPUSAMY R., KEANE E.F., KEITH M.J., KHARBANDA D., KIKUNAGA T., KOLHE N., KRAMER M., KRISHNAKUMAR M.A., LACKEOS K., LEE K.J., LIU K., LIU Y., LYNE A.G., McKEE J.W., MAAN Y., MAIN R.A., MICKALIGER M.B., NITU I.C., NOBLESON K., PALADI A.K., PARTHASARATHY A., PERERA B.B.P., PERRODIN D., PETITEAU A., PORAYKO N.K., POSSENTI A., PRABU T., QUELQUEJAY-LECLERE H., RANA P., SAMAJDAR A., SANIDAS S.A., SESANA A., SHAIFULLAH G., SINGHA J., SPERI L., SPIEWAK R., SRIVASTAVA A., STAPPERS B.W., SURNIS M., SUSARLA S.C., SUSOBHANAN A., TAKAHASHI K., TARAFDAR P., THEUREAU G., TIBURZI C., VAN DER WATEREN E., VECCHIO A., VENKATRAMAN KRISHNAN V., VERBIEST J.P.W., WANG J., WANG L. and WU Z.

Abstract (from CDS):

Aims. The nanohertz gravitational wave background (GWB) is expected to be an aggregate signal of an ensemble of gravitational waves emitted predominantly by a large population of coalescing supermassive black hole binaries in the centres of merging galaxies. Pulsar timing arrays (PTAs), which are ensembles of extremely stable pulsars at approximately kiloparsec distances precisely monitored for decades, are the most precise experiments capable of detecting this background. However, the subtle imprints that the GWB induces on pulsar timing data are obscured by many sources of noise that occur on various timescales. These must be carefully modelled and mitigated to increase the sensitivity to the background signal.
Methods. In this paper, we present a novel technique to estimate the optimal number of frequency coefficients for modelling achromatic and chromatic noise, while selecting the preferred set of noise models to use for each pulsar. We also incorporated a new model to fit for scattering variations in the Bayesian pulsar timing package temponest. These customised noise models enable a more robust characterisation of single-pulsar noise. We developed a software package based on tempo2 to create realistic simulations of European Pulsar Timing Array (EPTA) datasets that allowed us to test the efficacy of our noise modelling algorithms.
Results. Using these techniques, we present an in-depth analysis of the noise properties of 25 millisecond pulsars (MSPs) that form the second data release (DR2) of the EPTA and investigate the effect of incorporating low-frequency data from the Indian Pulsar Timing Array collaboration for a common sample of ten MSPs. We used two packages, enterprise and temponest, to estimate our noise models and compare them with those reported using EPTA DR1. We find that, while in some pulsars we can successfully disentangle chromatic from achromatic noise owing to the wider frequency coverage in DR2, in others the noise models evolve in a much more complicated way. We also find evidence of long-term scattering variations in PSR J1600-3053. Through our simulations, we identify intrinsic biases in our current noise analysis techniques and discuss their effect on GWB searches. The analysis and results discussed in this article directly help to improve the sensitivity to the GWB signal and they are already being used as part of global PTA efforts.

Abstract Copyright: © The Authors 2023

Journal keyword(s): pulsars: general - gravitational waves - methods: statistical

Simbad objects: 25

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Number of rows : 25
N Identifier Otype ICRS (J2000)
RA
ICRS (J2000)
DEC
Mag U Mag B Mag V Mag R Mag I Sp type #ref
1850 - 2026
#notes
1 PSR J0030+0451 Psr 00 30 27.42348 +04 51 39.7144           ~ 596 1
2 PSR B0613-02.0 Psr 06 13 43.977070 -02 00 47.34294           ~ 319 1
3 PSR J0751+1807 Psr 07 51 09.1539 +18 07 38.335           ~ 316 1
4 PSR J0900-3144 Psr 09 00 43.95220 -31 44 30.8722           ~ 68 0
5 PSR J1012+5307 Psr 10 12 33.4345449600 +53 07 02.548542576     19.5     DA 419 1
6 PSR J1022+10 Psr 10 22 57.990 +10 01 52.92           ~ 279 2
7 PSR J1025-07 Psr 10 24 38.6991893112 -07 19 19.045523388     19.81 18.78 18.00 ~ 286 1
8 PSR J1455-3300 Psr 14 55 47.976991 -33 30 46.40280           ~ 165 1
9 PSR J1600-3053 Psr 16 00 51.902514 -30 53 49.45343           ~ 218 0
10 PSR J1640+2224 Psr 16 40 16.744850 +22 24 08.84119           ~ 210 0
11 PSR J1713+0747 Psr 17 13 49.5367813 +07 47 37.450240           ~ 530 1
12 PSR J1730-2304 Psr 17 30 21.68471 -23 04 31.2590           ~ 194 1
13 PSR J1738+0333 Psr 17 38 53.966386 +03 33 10.87200           ~ 191 1
14 PSR J1744-1134 Psr 17 44 29.4220844 -11 34 54.801320           ~ 323 1
15 PSR J1751-2857 Psr 17 51 32.686848 -28 57 46.57130           ~ 65 1
16 PSR J1801-1417 Psr 18 01 51.064902 -14 17 34.54980           ~ 45 0
17 PSR J1804-27 Psr 18 04 21.13307 -27 17 31.3370           ~ 87 1
18 PSR J1843-1113 Psr 18 43 41.260405 -11 13 31.10310           ~ 86 0
19 PSR B1855+09 Psr 18 57 36.390620 +09 43 17.20712           DA 569 1
20 PSR J1909-3744 Psr 19 09 47.42820 -37 44 14.7740           ~ 343 2
21 PSR J1910+1256 Psr 19 10 09.701454 +12 56 25.48680           ~ 108 1
22 PSR J1911+1347 Psr 19 11 55.204694 +13 47 34.38383           ~ 72 0
23 PSR J1918-0642 Psr 19 18 48.027685 -06 42 34.95735           ~ 146 0
24 PSR J2124-3358 Psr 21 24 43.834930 -33 58 45.49195           ~ 337 1
25 PSR J2322+2057 Psr 23 22 22.320041 +20 57 02.50080           ~ 129 1

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