Swinburne University of Technology

About: Swinburne University of Technology is a education organization based out in Melbourne, Victoria, Australia. It is known for research contribution in the topics: Galaxy & Population. The organization has 7223 authors who have published 25530 publications receiving 667955 citations. The organization is also known as: Swinburne Technical College & Swinburne College of Technology.

Adaptive random testing

Abstract: In this paper, we introduce an enhanced form of random testing called Adaptive Random Testing. Adaptive random testing seeks to distribute test cases more evenly within the input space. It is based on the intuition that for non-point types of failure patterns, an even spread of test cases is more likely to detect failures using fewer test cases than ordinary random testing. Experiments are performed using published programs. Results show that adaptive random testing does outperform ordinary random testing significantly (by up to as much as 50%) for the set of programs under study. These results are very encouraging, providing evidences that our intuition is likely to be useful in improving the effectiveness of random testing.

Statistical analysis of galaxy surveys – I. Robust error estimation for two-point clustering statistics

Abstract: We present a test of different error estimators for two-point clustering statistics, appropriate for present and future large galaxy redshift surveys Using an ensemble of very large dark matter ACDM N-body simulations, we compare internal error estimators (jackknife and bootstrap) to external ones (Monte Carlo realizations) For three-dimensional clustering statistics, we find that none of the internal error methods investigated is able to reproduce either accurately or robustly the errors of external estimators on 1 to 25 h ―1 Mpc scales The standard bootstrap overestimates the variance of ξ (s) by ∼40 per cent on all scales probed, but recovers, in a robust fashion, the principal eigenvectors of the underlying covariance matrix The jackknife returns the correct variance on large scales, but significantly overestimates it on smaller scales This scale dependence in the jackknife affects the recovered eigenvectors, which tend to disagree on small scales with the external estimates Our results have important implications for fitting models to galaxy clustering measurements For example, in a two-parameter fit to the projected correlation function, we find that the standard bootstrap systematically overestimates the 95 per cent confidence interval, while the jackknife method remains biased, but to a lesser extent Ignoring the systematic bias, the scatter between realizations, for Gaussian statistics, implies that a 2σ confidence interval, as inferred from an internal estimator, corresponds in practice to anything from 1σ to 3σ By oversampling the subvolumes, we find that it is possible, at least for the cases we consider, to obtain robust bootstrap variances and confidence intervals that agree with external error estimates Our results are applicable to two-point statistics, like ξ(s) and w p (r p ), measured in large redshift surveys, and show that the interpretation of clustering measurements with internally estimated errors should be treated with caution

Properties and Astrophysical Implications of the 150 M Binary Black Hole Merger GW190521

Abstract: The gravitational-wave signal GW190521 is consistent with a binary black hole (BBH) merger source at redshift 0.8 with unusually high component masses, 85-14+21 M o˙ and 66-18+17 M o˙, compared to previously reported events, and shows mild evidence for spin-induced orbital precession. The primary falls in the mass gap predicted by (pulsational) pair-instability supernova theory, in the approximate range 65-120 M o˙. The probability that at least one of the black holes in GW190521 is in that range is 99.0%. The final mass of the merger (142-16+28 M o˙) classifies it as an intermediate-mass black hole. Under the assumption of a quasi-circular BBH coalescence, we detail the physical properties of GW190521's source binary and its post-merger remnant, including component masses and spin vectors. Three different waveform models, as well as direct comparison to numerical solutions of general relativity, yield consistent estimates of these properties. Tests of strong-field general relativity targeting the merger-ringdown stages of the coalescence indicate consistency of the observed signal with theoretical predictions. We estimate the merger rate of similar systems to be 0.13-0.11+0.30 Gpc-3 yr-1. We discuss the astrophysical implications of GW190521 for stellar collapse and for the possible formation of black holes in the pair-instability mass gap through various channels: via (multiple) stellar coalescences, or via hierarchical mergers of lower-mass black holes in star clusters or in active galactic nuclei. We find it to be unlikely that GW190521 is a strongly lensed signal of a lower-mass black hole binary merger. We also discuss more exotic possible sources for GW190521, including a highly eccentric black hole binary, or a primordial black hole binary.