Use of the MultiNest algorithm for gravitational wave data analysis

We describe an application of the MultiNest algorithm to gravitational wave data analysis. MultiNest is a multimodal nested sampling algorithm designed to efficiently evaluate the Bayesian evidence and return posterior probability densities for likelihood surfaces containing multiple secondary modes. The algorithm employs a set of live points which are updated by partitioning the set into multiple overlapping ellipsoids and sampling uniformly from within them. This set of live points climbs up the likelihood surface through nested iso-likelihood contours and the evidence and posterior distributions can be recovered from the point set evolution. The algorithm is model-independent in the sense that the specific problem being tackled enters only through the likelihood computation, and does not change how the live point set is updated. In this paper, we consider the use of the algorithm for gravitational wave data analysis by searching a simulated LISA data set containing two non-spinning supermassive black hole binary signals. The algorithm is able to rapidly identify all the modes of the solution and recover the true parameters of the sources to high precision.Comment: 18 pages, 4 figures, submitted to Class. Quantum Grav; v2 includes various changes in light of referee's comment

Evolutionary algorithm-based analysis of gravitational microlensing lightcurves

A new algorithm developed to perform autonomous fitting of gravitational microlensing lightcurves is presented. The new algorithm is conceptually simple, versatile and robust, and parallelises trivially; it combines features of extant evolutionary algorithms with some novel ones, and fares well on the problem of fitting binary-lens microlensing lightcurves, as well as on a number of other difficult optimisation problems. Success rates in excess of 90% are achieved when fitting synthetic though noisy binary-lens lightcurves, allowing no more than 20 minutes per fit on a desktop computer; this success rate is shown to compare very favourably with that of both a conventional (iterated simplex) algorithm, and a more state-of-the-art, artificial neural network-based approach. As such, this work provides proof of concept for the use of an evolutionary algorithm as the basis for real-time, autonomous modelling of microlensing events. Further work is required to investigate how the algorithm will fare when faced with more complex and realistic microlensing modelling problems; it is, however, argued here that the use of parallel computing platforms, such as inexpensive graphics processing units, should allow fitting times to be constrained to under an hour, even when dealing with complicated microlensing models. In any event, it is hoped that this work might stimulate some interest in evolutionary algorithms, and that the algorithm described here might prove useful for solving microlensing and/or more general model-fitting problems.Comment: 14 pages, 3 figures; accepted for publication in MNRA

Cosmic Swarms: A search for Supermassive Black Holes in the LISA data stream with a Hybrid Evolutionary Algorithm

We describe a hybrid evolutionary algorithm that can simultaneously search for multiple supermassive black hole binary (SMBHB) inspirals in LISA data. The algorithm mixes evolutionary computation, Metropolis-Hastings methods and Nested Sampling. The inspiral of SMBHBs presents an interesting problem for gravitational wave data analysis since, due to the LISA response function, the sources have a bi-modal sky solution. We show here that it is possible not only to detect multiple SMBHBs in the data stream, but also to investigate simultaneously all the various modes of the global solution. In all cases, the algorithm returns parameter determinations within $5\sigma$ (as estimated from the Fisher Matrix) of the true answer, for both the actual and antipodal sky solutions.Comment: submitted to Classical & Quantum Gravity. 19 pages, 4 figure

Classical and MgII-selected Damped Lyman-alpha Absorbers: impact on Omega_HI at z<1.7

The Damped Lyman-alpha systems (DLAs), seen in absorption in the spectrum of quasars, are believed to contain a large fraction of the neutral gas in the Universe. Paradoxically, these systems are more difficult to observe at z_abs<1.7, since they are rare and their HI feature then falls in UV spectra. Rao & Turnshek (2000) pioneered a method based on MgII-selected DLAs, that is absorbers discovered thanks to our knowledge of their MgII feature in optical spectra. We use new observations undertaken at the TNG as well as a careful literature & archival search to build samples of low redshift absorbers classified according to the technique used for their discovery. We successfully recover N(HI) and equivalent widths of FeII 2600, MgII 2796, MgII 2803 and MgII 2852 for a sample of 36 absorbers, 21 of which are MgII-selected. We find that the MgII-selected sample contains a marginally larger fraction of absorbers with log N(HI)>21.0 than seen otherwise at low redshift. If confirmed, this property will in turn affect estimates of Omega_HI which is dominated by the highest HI column densities. We find that log N(HI) does not correlate significantly with metal equivalent widths. Similarly, we find no evidence that gravitational lensing, the fraction of associated systems or redshift evolution affect the absorber samples in a different way. We conclude that the hint of discrepancies in N(HI) distributions most likely arises from small number statistics. Therefore, further observations are required to better clarify the impact of this discrepancy on estimates of Omega_HI at low redshift.Comment: 13 pages, 8 figures. Accepted for publication in MNRA

When Darwin Met Einstein: Gravitational Lens Inversion with Genetic Algorithms

Gravitational lensing can magnify a distant source, revealing structural detail which is normally unresolvable. Recovering this detail through an inversion of the influence of gravitational lensing, however, requires optimisation of not only lens parameters, but also of the surface brightness distribution of the source. This paper outlines a new approach to this inversion, utilising genetic algorithms to reconstruct the source profile. In this initial study, the effects of image degradation due to instrumental and atmospheric effects are neglected and it is assumed that the lens model is accurately known, but the genetic algorithm approach can be incorporated into more general optimisation techniques, allowing the optimisation of both the parameters for a lensing model and the surface brightness of the source.Comment: 9 pages, to appear in PAS

A Search for Dark Matter Annihilation with the Whipple 10m Telescope

We present observations of the dwarf galaxies Draco and Ursa Minor, the local group galaxies M32 and M33, and the globular cluster M15 conducted with the Whipple 10m gamma-ray telescope to search for the gamma-ray signature of self-annihilating weakly interacting massive particles (WIMPs) which may constitute astrophysical dark matter (DM). We review the motivations for selecting these sources based on their unique astrophysical environments and report the results of the data analysis which produced upper limits on excess rate of gamma rays for each source. We consider models for the DM distribution in each source based on the available observational constraints and discuss possible scenarios for the enhancement of the gamma-ray luminosity. Limits on the thermally averaged product of the total self-annihilation cross section and velocity of the WIMP, , are derived using conservative estimates for the magnitude of the astrophysical contribution to the gamma-ray flux. Although these limits do not constrain predictions from the currently favored theoretical models of supersymmetry (SUSY), future observations with VERITAS will probe a larger region of the WIMP parameter phase space, and WIMP particle mass (m_\chi).Comment: 33 pages, 12 figures, accepted for publication in the Astrophysical Journa