792 research outputs found
Fast methods for training Gaussian processes
No full textThis submission includes a simplified version of some code we have been developing for fast training of Gaussian processes. We also include a sample data set, which is NOAA tidal data from Woods Hole in the US, downloaded from http://tidesandcurrents.noaa.gov/ .
The code and data included here were used to produce the numerical results in the following paper:
[1] Fast methods for training Gaussian processes, C. J. Moore, A. J. K. Chua, C. P. L. Berry, and J. R. Gair (2016), submitted to RSOS
Fast methods for training Gaussian processes
No full textThis submission includes a simplified version of some code we have been developing for fast training of Gaussian processes. We also include a sample data set, which is NOAA tidal data from Woods Hole in the US, downloaded from http://tidesandcurrents.noaa.gov/ . The code and data included here were used to produce the numerical results in the following paper: [1] Fast methods for training Gaussian processes, C. J. Moore, A. J. K. Chua, C. P. L. Berry, and J. R. Gair (2016), submitted to RSOS.Moore, Christopher J; Chua, Alvin J K; Berry, Christopher P L; Gair, Jonathan R. (2016). Fast methods for training Gaussian processes, [software]. http://dx.doi.org/10.7488/ds/1343
Detecting extreme mass ratio inspirals with LISA using time-frequency methods II: search characterization
No full textThe inspirals of stellar-mass compact objects into supermassive black holes constitute some of the most important sources for LISA. Detection of these sources using fully coherent matched filtering is computationally intractable, so alternative approaches are required. In a previous paper (Wen L and Gair J R 2005 Class. Quantum Grav. 22 S445), we outlined a detection method based on looking for excess power in a time–frequency spectrogram of the LISA data. The performance of the algorithm was assessed using a single 'typical' trial waveform and approximations to the noise statistics. In this paper we present results of Monte Carlo simulations of the search noise statistics and examine its performance in detecting a wider range of trial waveforms. We show that typical extreme mass ratio inspirals can be detected at distances of up to 1–3 Gpc, depending on the source parameters. We also discuss some remaining issues with the technique and possible ways in which the algorithm can be improved
Extracting Information about EMRIs using Time-Frequency Methods
No full textThe inspirals of stellar-mass compact objects into supermassive black holes are some of the most exciting sources of gravitational waves for LISA. Detection of these sources using fully coherent matched filtering is computationally intractable, so alternative approaches are required. In Wen & Gair (2005), we proposed a detection method based on searching for significant deviation of power density from noise in a time-frequency spectrogram of the LISA data. The performance of the algorithm was assessed in Gair & Wen (2005) using Monte-Carlo simulations on several trial waveforms and approximations to the noise statistics. We found that typical extreme mass ratio inspirals (EMRIs) could be detected at distances of up to 1-3 Gpc, depending on the source parameters. In this paper, we first give an overview of our previous work in Wen & Gair (2005) and Gair & Wen (2005), and discuss the performance of the method in a broad sense. We then introduce a decomposition method for LISA data that decodes LISA's directional sensitivity. This decomposition method could be used to improve the detection efficiency, to extract the source waveform, and to help solve the source confusion problem. Our approach to constraining EMRI parameters using the output from the time-frequency method will be outlined
Discriminating between different scenarios for the formation and evolution of massive black holes with LISA
No full textElectromagnetic observations have provided strong evidence for the existence of massive black holes in the center of galaxies, but their origin is still poorly known. Different scenarios for the formation and evolution of massive black holes lead to different predictions for their properties and merger rates. LISA observations of coalescing massive black hole binaries could be used to reverse engineer the problem and shed light on these mechanisms. In this paper, we introduce a pipeline based on hierarchical Bayesian inference to infer the mixing fraction between different theoretical models by comparing them to LISA observations of massive black hole mergers. By testing this pipeline against simulated LISA data, we show that it allows us to accurately infer the properties of the massive black hole population as long as our theoretical models provide a reliable description of the Universe. We also show that measurement errors, including both instrumental noise and weak lensing errors, have little impact on the inference
Leaving for the Anzac ceremony at the Shrine, 1963
No full textSwinburne staff and students leaving for the Anzac ceremony at the Shrine, 25 April 1963. Photograph appeared in the 1963 edition of 'Open Door' (p. 33) Back row: G. Oakley, Mr. Sutherland, R. Gair, P. Brinsden, N. Nobes, S. Pear, L. Williams.
Middle row: Mrs. Penrose, F. Cameron, J. Palmer, C. McDonald, B. Whitby, P. Dawson.
Front row: M. Forbes (holding wreath), A. Graham, R. Newton, D. Brown, P. Green
Event rate estimates for LISA extreme mass ratio capture sources
No full textOne of the most exciting prospects for the LISA gravitational wave observatory is the detection of gravitational radiation from the inspiral of a compact object into a supermassive black hole. The large inspiral parameter space and low amplitude of the signal makes detection of these sources computationally challenging. We outline here a first cut data analysis scheme that assumes realistic computational resources. In the context of this scheme, we estimate the signal-to-noise ratio that a source requires to pass our thresholds and be detected. Combining this with an estimate of the population of sources in the Universe, we estimate the number of inspiral events that LISA could detect. The preliminary results are very encouraging -- with the baseline design, LISA can see inspirals out to a redshift z=1 and should detect over a thousand events during the mission lifetime
Reducing distance errors for standard candles and standard sirens with weak-lensing shear and flexion maps
No full textGravitational lensing induces significant errors in the measured distances to high-redshift standard candles and standard sirens such as type-Ia supernovae, gamma-ray bursts, and merging supermassive black hole binaries. There will therefore be a signif-icant benefit from correcting for the lensing error by using independent and accurate estimates of the lensing magnification. Here we investigate how accurately the magni-fication can be inferred from convergence maps reconstructed from galaxy shear and flexion data. We employ ray-tracing through the Millennium Simulation to simulate lensing observations in large fields, and perform a weak-lensing reconstruction on the simulated fields. We identify optimal ways to filter the reconstructed convergence maps and to convert them to magnification maps, and analyse the resulting relation between the estimated and true magnification for sources at redshifts zS = 1 to 5. We find that a deep shear survey with 100 galaxies/arcmin2 can help to reduce the lensing-induced distance errors for standard candles/sirens at redshifts zS ≈ 1.5 (zS ≈ 5) on average by 20 % (10%), whereas a futuristic survey with shear and flexion estimates from 50
Hawaii vs Utah, September 24, 1983
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