scholarly journals Is there a spectral turnover in the spin noise of millisecond pulsars?

2020 ◽  
Vol 497 (3) ◽  
pp. 3264-3272 ◽  
Author(s):  
Boris Goncharov ◽  
Xing-Jiang Zhu ◽  
Eric Thrane

ABSTRACT Pulsar timing arrays provide a unique means to detect nanohertz gravitational waves through long-term measurements of pulse arrival times from an ensemble of millisecond pulsars. After years of observations, some timing array pulsars have been shown to be dominated by low-frequency red noise, including spin noise that might be associated with pulsar rotational irregularities. The power spectral density of pulsar timing red noise is usually modelled with a power law or a power law with a turnover frequency below which the noise power spectrum plateaus. If there is a turnover in the spin noise of millisecond pulsars, residing within the observation band of current and/or future pulsar timing measurements, it may be easier than projected to resolve the gravitational-wave background from supermassive binary black holes. Additionally, the spectral turnover can provide valuable insights on neutron star physics. In the recent study by Melatos and Link, the authors provided a derivation of the model for power spectral density of spin noise from superfluid turbulence in the core of a neutron star, from first principles. The model features a spectral turnover, which depends on the dynamical response time of the superfluid and the steady-state angular velocity lag between the crust and the core of the star. In this work, we search for a spectral turnover in spin noise using the first data release of the International Pulsar Timing Array. Through Bayesian model selection, we find no evidence of a spectral turnover. Our analysis also shows that data from PSRs J1939+2134, J1024–0719, and J1713+0747 prefers the power-law model to the superfluid turbulence model.

2021 ◽  
Vol 247 ◽  
pp. 09009
Author(s):  
Atsushi Sakon ◽  
Kunihiro Nakajima ◽  
Kazuki Takahashi ◽  
Shin-ya Hohara ◽  
Tadafumi Sano ◽  
...  

In graphite-reflected thermal reactors, even a detector placed far from fuel region may detect a certain degree of the correlation amplitude. This is because mean free path of neutrons in graphite is longer than that in water or polyethylene. The objective of this study is experimentally to confirm a high flexibility of neutron detector placement in graphite reflector for reactor noise analysis. The present reactor noise analysis was carried out in a graphite-moderated and -reflected thermal core in Kyoto University Critical Assembly (KUCA). BF3 proportional neutron counters (1” dia.) were placed in graphite reflector region, where the counters were separated by about 35cm and 30cm -thick graphite from the core, respectively. At a critical state and subcritical states, time-sequence signal data from these counters were acquired and analyzed by a fast Fourier transform (FFT) analyzer, to obtain power spectral density in frequency domain. The auto-power spectral density obtained from the counters far from the core contained a significant degree of correlated component. A least-squares fit of a familiar formula to the auto-power spectral density data was made to determine the prompt-neutron decay constant. The decay constant was 63.3±14.5 [1/s] in critical state. The decay constant determined from the cross-power spectral density and coherence function data between the two counters also had a consistent value. It is confirmed that reactor noise analysis is possible using a detector placed at about 35cm far from the core, as we expected.


2009 ◽  
Vol 2 (1) ◽  
pp. 40-47
Author(s):  
Montasser Tahat ◽  
Hussien Al-Wedyan ◽  
Kudret Demirli ◽  
Saad Mutasher

Author(s):  
Benjamin Yen ◽  
Yusuke Hioka

Abstract A method to locate sound sources using an audio recording system mounted on an unmanned aerial vehicle (UAV) is proposed. The method introduces extension algorithms to apply on top of a baseline approach, which performs localisation by estimating the peak signal-to-noise ratio (SNR) response in the time-frequency and angular spectra with the time difference of arrival information. The proposed extensions include a noise reduction and a post-processing algorithm to address the challenges in a UAV setting. The noise reduction algorithm reduces influences of UAV rotor noise on localisation performance, by scaling the SNR response using power spectral density of the UAV rotor noise, estimated using a denoising autoencoder. For the source tracking problem, an angular spectral range restricted peak search and link post-processing algorithm is also proposed to filter out incorrect location estimates along the localisation path. Experimental results show the proposed extensions yielded improvements in locating the target sound source correctly, with a 0.0064–0.175 decrease in mean haversine distance error across various UAV operating scenarios. The proposed method also shows a reduction in unexpected location estimations, with a 0.0037–0.185 decrease in the 0.75 quartile haversine distance error.


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