MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics

Feroz, Farhan; Hobson, M.; Bridges, M.

doi:10.1111/j.1365-2966.2009.14548.x

Cited by 2,843 publications

(2,829 citation statements)

References 42 publications

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“…parameters (in addition to S0-2ʼs sixKeplerian orbital parameters). This 13-dimensional orbital fit was done using the Bayesian multimodal nested sampling algorithm called MULTIN-EST (see Feroz & Hobson 2008;Feroz et al 2009). We also use the results of this orbital fit of S0-2 alone to compare with the results of fitting S0-38 alone and S0-2 and S0-38 simultaneously (Section 3.4).…”

Section: Appendix B S0-2 Data and Orbital Analysismentioning

confidence: 99%

An Improved Distance and Mass Estimate for SGR A* From a Multistar Orbit Analysis

Boehle

Ghez

Schödel

et al. 2016

ApJ

347

413

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We present new, more precise measurements of the mass and distance of our Galaxy's central supermassive black hole, Sgr A * . These results stem from a new analysis that more than doubles the time baseline for astrometry of faint stars orbiting Sgr A * , combining 2decades of speckle imaging and adaptive optics data. Specifically, we improve our analysis of the speckle images by using information about a star's orbit from the deep adaptive optics data (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013) to inform the search for the star in the speckle years (1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005). When this new analysis technique is combined with the first complete re-reduction of Keck Galactic Center speckle images using speckle holography, we are able to track the short-period star S0-38 (K-band magnitude=17, orbital period=19 yr) through the speckle years. We use the kinematic measurements from speckle holography and adaptive optics to estimate the orbits of S0-38 and S0-2 and thereby improve our constraints of the mass (M bh ) and distance (R o ) of Sgr A * : M bh = (4.02±0.16±0.04) ×10 6 M e and 7.86±0.14±0.04 kpc. The uncertainties in M bh and R o as determined by the combined orbital fit of S0-2 and S0-38 are improved by a factor of 2 and 2.5, respectively, compared to an orbital fit of S0-2 alone and a factor of ∼2.5 compared to previous results from stellar orbits. This analysis also limits the extended dark mass within 0.01 pc to less than 0.13×10 6 M e at 99.7% confidence, a factor of 3 lower compared to prior work.

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Section: Appendix B S0-2 Data and Orbital Analysismentioning

confidence: 99%

An Improved Distance and Mass Estimate for SGR A* From a Multistar Orbit Analysis

Boehle

Ghez

Schödel

et al. 2016

ApJ

347

413

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“…We have computed the distribution of the posterior (4.4) in the CMSSM parameter space using a modified version of the public SuperBayeS package [19] 6 adopting MultiNest v2.8 [20,21] as a scanning algorithm. We use as running parameters a number of live points 5 Fortunately, in our case this point is irrelevant thanks to the Jacobian factor, J.…”

Section: Resultsmentioning

confidence: 99%

Histogram comparison tools for the search of new physics at LHC. Application to the CMSSM

Cabrera

Casas

Mitsou

et al. 2012

J. High Energ. Phys.

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Abstract:We propose a rigorous and effective way to compare experimental and theoretical histograms, incorporating the different sources of statistical and systematic uncertainties. This is a useful tool to extract as much information as possible from the comparison between experimental data with theoretical simulations, optimizing the chances of identifying New Physics at the LHC. We illustrate this by showing how a search in the CMSSM parameter space, using Bayesian techniques, can effectively find the correct values of the CMSSM parameters by comparing histograms of events with multijets + missing transverse momentum displayed in the effective-mass variable. The procedure is in fact very efficient to identify the true supersymmetric model, in the case supersymmetry is really there and accessible to the LHC.

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“…(4.2) we use the Multinest program [38][39][40]. We use our own routines to evaluate event rates and the Likelihood function.…”

Section: Jcap07(2014)055mentioning

confidence: 99%

Prospects for direct detection of dark matter in an effective theory approach

Catena¹

2014

J. Cosmol. Astropart. Phys.

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We perform the first comprehensive analysis of the prospects for direct detection of dark matter with future ton-scale detectors in the general 11-dimensional effective theory of isoscalar dark matter-nucleon interactions mediated by a heavy spin-1 or spin-0 particle. The theory includes 8 momentum and velocity dependent dark matter-nucleon interaction operators, besides the familiar spin-independent and spin-dependent operators. From a variegated sample of 27 benchmark points selected in the parameter space of the theory, we simulate independent sets of synthetic data for ton-scale Germanium and Xenon detectors. From the synthetic data, we then extract the marginal posterior probability density functions and the profile likelihoods of the model parameters. The associated Bayesian credible regions and frequentist confidence intervals allow us to assess the prospects for direct detection of dark matter at the 27 benchmark points. First, we analyze the data assuming the knowledge of the correct dark matter nucleon-interaction type, as it is commonly done for the familiar spin-independent and spin-dependent interactions. Then, we analyze the simulations extracting the dark matter-nucleon interaction type from the data directly, in contrast to standard analyses. This second approach requires an extensive exploration of the full 11-dimensional parameter space of the dark matter-nucleon effective theory. Interestingly, we identify 5 scenarios where the dark matter mass and the dark matter-nucleon interaction type can be reconstructed from the data simultaneously. We stress the importance of extracting the dark matter nucleon-interaction type from the data directly, discussing the main challenges found addressing this complex 11-dimensional problem.

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MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics

Cited by 2,843 publications

References 42 publications

An Improved Distance and Mass Estimate for SGR A* From a Multistar Orbit Analysis

An Improved Distance and Mass Estimate for SGR A* From a Multistar Orbit Analysis

Histogram comparison tools for the search of new physics at LHC. Application to the CMSSM

Prospects for direct detection of dark matter in an effective theory approach

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