Publications
118 results found
Bertone G, Calore F, Caron S, et al., 2016, Global analysis of the pMSSM in light of the Fermi GeV excess: prospects for the LHC Run-II and astroparticle experiments, Journal of Cosmology and Astroparticle Physics, Vol: 2016, ISSN: 1475-7516
Jóhannesson G, Ruiz De Austri R, Vincent AC, et al., 2015, Bayesian approach to galactic cosmic ray propagation
The fully Bayesian approach to the problem of deriving constraints for cosmic ray (CR) model parameters has several advantages. These are: (i) an efficient global scan of the whole parameter space allowing us to explore and take into account parameter correlations and degeneracies, (ii) a best-fit point and statistically well-defined errors on the parameters, (iii) the ability to include and marginalize over 'nuisance' parameters (such as modulation potential and error rescaling parameters) making the analysis more robust. For this study, we use the latest version of the CR propagation code GALPROP together with the BAMBI code, the most efficient Bayesian analysis code available to date that combines MultiNest with Neural networks. The results of the analysis will be reported during the conference.
Strege C, Bertone G, Besjes GJ, et al., 2014, Profile likelihood maps of a 15-dimensional MSSM, Journal of High Energy Physics, Vol: 2014, ISSN: 1126-6708
We present statistically convergent profile likelihood maps obtained via globalfits of a phenomenological Minimal Supersymmetric Standard Model with 15 free parameters(the MSSM-15), based on over 250M points. We derive constraints on the modelparameters from direct detection limits on dark matter, the Planck relic density measurementand data from accelerator searches. We provide a detailed analysis of the richphenomenology of this model, and determine the SUSY mass spectrum and dark matterproperties that are preferred by current experimental constraints. We evaluate the impactof the measurement of the anomalous magnetic moment of the muon (g −2) on our results,and provide an analysis of scenarios in which the lightest neutralino is a subdominant componentof the dark matter. The MSSM-15 parameters are relatively weakly constrained bycurrent data sets, with the exception of the parameters related to dark matter phenomenology(M1, M2, µ), which are restricted to the sub-TeV regime, mainly due to the relic densityconstraint. The mass of the lightest neutralino is found to be < 1.5 TeV at 99% C.L., butcan extend up to 3 TeV when excluding the g − 2 constraint from the analysis. Low-massbino-like neutralinos are strongly favoured, with spin-independent scattering cross-sectionsextending to very small values, ∼ 10−20 pb. ATLAS SUSY null searches strongly impacton this mass range, and thus rule out a region of parameter space that is outside the reachof any current or future direct detection experiment. The best-fit point obtained after inclusionof all data corresponds to a squark mass of 2.3 TeV, a gluino mass of 2.1 TeV and a130 GeV neutralino with a spin-independent cross-section of 2.4×10−10 pb, which is withinthe reach of future multi-ton scale direct detection experiments and of the upcoming LHCrun at increased centre-of-mass energy.
Martin J, Ringeval C, Trotta R, et al., 2014, Compatibility of Planck and BICEP2 results in light of inflation, PHYSICAL REVIEW D, Vol: 90, ISSN: 1550-7998
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Martin J, Ringeval C, Trotta R, et al., 2014, The best inflationary models after Planck, Journal of Cosmology and Astroparticle Physics, Vol: 2014, ISSN: 1475-7516
We compute the Bayesian evidence and complexity of 193 slow-roll single-field models of inflation using the Planck 2013 Cosmic Microwave Background data, with the aim of establishing which models are favoured from a Bayesian perspective. Our calculations employ a new numerical pipeline interfacing an inflationary effective likelihood with the slow-roll library ASPIC and the nested sampling algorithm MultiNest. The models considered represent a complete and systematic scan of the entire landscape of inflationary scenarios proposed so far. Our analysis singles out the most probable models (from an Occam's razor point of view) that are compatible with Planck data, while ruling out with very strong evidence 34% of the models considered. We identify 26% of the models that are favoured by the Bayesian evidence, corresponding to 15 different potential shapes. If the Bayesian complexity is included in the analysis, only 9% of the models are preferred, corresponding to only 9 different potential shapes. These shapes are all of the plateau type.
Hilbe JM, Riggs J, Wandelt BD, et al., 2014, Life, the universe, and everything, Significance, Vol: 11, Pages: 48-75, ISSN: 1740-9705
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Trotta R, 2013, Cosmological bayesian model selection: Recent advances and open challenges, Pages: 127-140
The cosmology community has been increasingly focusing on Bayesian model selection as a tool to discriminate between competing theories to explain a large amount of data about our Universe. In this paper, I summarize the conceptual underpinnings and the algorithmic implementations of Bayesian model comparison. I then discuss two representative applications of Bayesian model comparison to cosmological problems: determining whether the Universe is infinite and selecting the "best" model of inflation. I conclude by offering some reflections about open challenges and interpretational issues. Help and suggestions from the statistics community would be appreciated in further developing the field. © Springer Science+Business Media New York 2013.
Amendola L, Appleby S, Bacon D, et al., 2013, Cosmology and Fundamental Physics with the Euclid Satellite, Living Reviews in Relativity, Vol: 16, ISSN: 1433-8351
Euclid is a European Space Agency medium-class mission selected for launch in 2019 withinthe Cosmic Vision 2015 – 2025 program. The main goal of Euclid is to understand the originof the accelerated expansion of the universe. Euclid will explore the expansion history of theuniverse and the evolution of cosmic structures by measuring shapes and red-shifts of galaxiesas well as the distribution of clusters of galaxies over a large fraction of the sky.Although the main driver for Euclid is the nature of dark energy, Euclid science covers avast range of topics, from cosmology to galaxy evolution to planetary research. In this reviewwe focus on cosmology and fundamental physics, with a strong emphasis on science beyondthe current standard models. We discuss five broad topics: dark energy and modified gravity,dark matter, initial conditions, basic assumptions and questions of methodology in the dataanalysis.This review has been planned and carried out within Euclid’s Theory Working Group andis meant to provide a guide to the scientific themes that will underlie the activity of the groupduring the preparation of the Euclid mission.
Strege C, Bertone G, Feroz F, et al., 2013, Global fits of the cMSSM and NUHM including the LHC Higgs discovery and new XENON100 constraints, JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS, Vol: 2013, ISSN: 1475-7516
We present global fits of the constrained Minimal Supersymmetric Standard Model (cMSSM) and the Non-Universal Higgs Model (NUHM), including the most recent CMS constraint on the Higgs boson mass, 5.8 fb−1 integrated luminosity null Supersymmetry searches by ATLAS, the new LHCb measurement of BR(bar Bs → μ+μ−) and the 7-year WMAP dark matter relic abundance determination. We include the latest dark matter constraints from the XENON100 experiment, marginalising over astrophysical and particle physics uncertainties. We present Bayesian posterior and profile likelihood maps of the highest resolution available today, obtained from up to 350M points. We find that the new constraint on the Higgs boson mass has a dramatic impact, ruling out large regions of previously favoured cMSSM and NUHM parameter space. In the cMSSM, light sparticles and predominantly gaugino-like dark matter with a mass of a few hundred GeV are favoured. The NUHM exhibits a strong preference for heavier sparticle masses and a Higgsino-like neutralino with a mass of 1 TeV. The future ton-scale XENON1T direct detection experiment will probe large portions of the currently favoured cMSSM and NUHM parameter space. The LHC operating at 14 TeV collision energy will explore the favoured regions in the cMSSM, while most of the regions favoured in the NUHM will remain inaccessible. Our best-fit points achieve a satisfactory quality-of-fit, with p-values ranging from 0.21 to 0.35, so that none of the two models studied can be presently excluded at any meaningful significance level.
Pato M, Strigari LE, Trotta R, et al., 2013, Taming astrophysical bias in direct dark matter searches, Journal of Cosmology and Astroparticle Physics, Vol: 2013, ISSN: 1475-7516
We explore systematic biases in the identification of dark matter in future direct detection experiments and compare the reconstructed dark matter properties when assuming a self-consistent dark matter distribution function and the standard Maxwellian velocity distribution. We find that the systematic bias on the dark matter mass and cross-section determination arising from wrong assumptions for its distribution function is of order ~ 1σ. A much larger systematic bias can arise if wrong assumptions are made on the underlying Milky Way mass model. However, in both cases the bias is substantially mitigated by marginalizing over galactic model parameters. We additionally show that the velocity distribution can be reconstructed in an unbiased manner for typical dark matter parameters. Our results highlight both the robustness of the dark matter mass and cross-section determination using the standard Maxwellian velocity distribution and the importance of accounting for astrophysical uncertainties in a statistically consistent fashion.
March MC, Trotta R, Berkes P, et al., 2013, Improved cosmological constraints from a bayesian hierarchical model of supernova type ia data, Springer Series in Astrostatistics, Pages: 203-235
We present a Bayesian hierarchical model for inferring the cosmological parameters from the supernovae type Ia fitted with the SALT-II lightcurve fitter. We demonstrate with simulated data sets that our method delivers tighter statistical constraints on the cosmological parameters over 90% of the time, that it reduces statistical bias typically by a factor ~2–3 and that it has better coverage properties than the usual χ2 approach. As a further benefit, a full posterior probability distribution for the dispersion of the intrinsic magnitude of SNe is obtained. We apply this method to recent SNIa data, and by combining them with CMB and BAO data we obtain Ωm = 0:28 ± 0:02, ΩΛ = 0:73 ± 0:01 (assuming ω = −1) and Ωm = 0:28 ± 0:01, ω = −0:90 ± 0:05 (assuming flatness; statistical uncertainties only). We constrain the intrinsic dispersion of the B-band magnitude of the SNIa population, obtaining (Formula Presented) = 0:13 ± 0:01 [mag].
Trotta R, Feroz F, Hobson M, et al., 2013, Recent Advances in Bayesian Inference in Cosmology and Astroparticle Physics Thanks to the MultiNest Algorithm, Springer Series in Astrostatistics, Pages: 107-119
We present a new algorithm, called MultiNest, which is a highly efficient alternative to traditional Markov Chain Monte Carlo (MCMC) sampling of posterior distributions. MultiNest is more efficient than MCMC, can deal with highly multi-modal likelihoods and returns the Bayesian evidence (or model likelihood, the prime quantity for Bayesian model comparison) together with posterior samples. It can thus be used as an all-around Bayesian inference engine. When appropriately tuned, it also provides an exploration of the profile likelihood that is competitive with what can be obtained with dedicated algorithms. We demonstrate the power and flexibility of MultiNest for Bayesian inference for multi-dimensional, multimodal-likelihoods, for Bayesian model selection and for profile likelihood evaluation for multi-modal, multi-scale likelihoods. Applications in cosmology and astroparticle physics are presented, including gravitational waves astronomy, inflationary Bayesian model comparison and supersymmetric parameter spaces exploration.
March MC, Trotta R, Berkes P, et al., 2013, Improved cosmological constraints from a Bayesian hierarchical model of supernova type Ia data, Astrostatistical Challenges for the New Astronomy, Pages: 203-235, ISBN: 9781461435075
We present a Bayesian hierarchical model for inferring the cosmological parameters from the supernovae type Ia fitted with the SALT-II lightcurve fitter. We demonstrate with simulated data sets that our method delivers tighter statistical constraints on the cosmological parameters over 90% of the time, that it reduces statistical bias typically by a factor ~2-3 and that it has better coverage properties than the usual X2 approach. As a further benefit, a full posterior probability distribution for the dispersion of the intrinsic magnitude of SNe is obtained. We apply this method to recent SNIa data, and by combining them with CMB and BAO data we obtain Ωm D 0:28 ± 0:02, ΩΛ D 0:73 ± 0:01 (assuming w = -1) and Ωm D 0:28 ± 0:01, w = -0:90 ± 0:05 (assuming flatness; statistical uncertainties only). We constrain the intrinsic dispersion of the B-band magnitude of the SNIa population, obtaining σμint = 0:13 ± 0:01 [mag]. ©
Trotta R, Feroz F, Hobson M, et al., 2013, Recent advances in Bayesian inference in cosmology and astroparticle physics thanks to the multinest algorithm, Astrostatistical Challenges for the New Astronomy, Pages: 107-119, ISBN: 9781461435075
We present a new algorithm, called MultiNest, which is a highly efficient alternative to traditional Markov Chain Monte Carlo (MCMC) sampling of posterior distributions. MultiNest is more efficient than MCMC, can deal with highly multi-modal likelihoods and returns the Bayesian evidence (or model likelihood, the prime quantity for Bayesian model comparison) together with posterior samples. It can thus be used as an all-around Bayesian inference engine. When appropriately tuned, it also provides an exploration of the profile likelihood that is competitive with what can be obtained with dedicated algorithms. We demonstrate the power and flexibility of MultiNest for Bayesian inference for Bayesian model selection and l, multi-scale likelihoods. Applicapresented, including gravitational comparison and supersymmetric Abstract for profile likelihood evaluation for multi-moda for multi-dimensional, multimodal-likelihoods, tions in cosmology and astroparticle physics are waves astronomy, inflationary Bayesian model parameter spaces exploration.
Gandy A, Trotta R, 2013, Special Issue on Astrostatistics, STATISTICAL ANALYSIS AND DATA MINING, Vol: 6, Pages: 1-+, ISSN: 1932-1864
Strege C, Trotta R, Bertone G, et al., 2012, Fundamental statistical limitations of future dark matter direct detection experiments, Physical Review D, Vol: 86, ISSN: 1550-7998
We discuss irreducible statistical limitations of future ton-scale dark matter direct detection experiments. We focus in particular on the coverage of confidence intervals, which quantifies the reliability of the statistical method used to reconstruct the dark matter parameters and the bias of the reconstructed parameters. We study 36 benchmark dark matter models within the reach of upcoming ton-scale experiments. We find that approximate confidence intervals from a profile-likelihood analysis exactly cover or overcover the true values of the weakly interacting massive particle (WIMP) parameters, and hence are conservative. We evaluate the probability that unavoidable statistical fluctuations in the data might lead to a biased reconstruction of the dark matter parameters, or large uncertainties on the reconstructed parameter values. We show that this probability can be surprisingly large, even for benchmark models leading to a large event rate of order a hundred counts. We find that combining data sets from two different targets leads to improved coverage properties, as well as a substantial reduction of statistical bias and uncertainty on the dark matter parameters
Strege C, Bertone G, Cerdeno DG, et al., 2012, Updated global fits of the cMSSM including the latest LHC SUSY and Higgs searches and XENON100 data, Journal of Cosmology and Astroparticle Physics, Vol: 2012, ISSN: 1475-7516
We present new global fits of the constrained Minimal Supersymmetric Standard Model (cMSSM), including LHC 1/fb integrated luminosity SUSY exclusion limits, recent LHC 5/fb constraints on the mass of the Higgs boson and XENON100 direct detection data. Our analysis fully takes into account astrophysical and hadronic uncertainties that enter the analysis when translating direct detection limits into constraints on the cMSSM parameter space. We provide results for both a Bayesian and a Frequentist statistical analysis. We find that LHC 2011 constraints in combination with XENON100 data can rule out a significant portion of the cMSSM parameter space. Our results further emphasise the complementarity of collider experiments and direct detection searches in constraining extensions of Standard Model physics. The LHC 2011 exclusion limit strongly impacts on low-mass regions of cMSSM parameter space, such as the stau co-annihilation region, while direct detection data can rule out regions of high SUSY masses, such as the Focus-Point region, which is unreachable for the LHC in the near future. We show that, in addition to XENON100 data, the experimental constraint on the anomalous magnetic moment of the muon plays a dominant role in disfavouring large scalar and gaugino masses. We find that, should the LHC 2011 excess hinting towards a Higgs boson at 126 GeV be confirmed, currently favoured regions of the cMSSM parameter space will be robustly ruled out from both a Bayesian and a profile likelihood statistical perspective.
Bertone G, Cerdeno DG, Fornasa M, et al., 2012, Complementarity of indirect and accelerator dark matter searches, Physical Review D, Vol: 85, ISSN: 1550-7998
Even if supersymmetric particles are found at the Large Hadron Collider (LHC), it will be difficult to prove that they constitute the bulk of the dark matter (DM) in the Universe using LHC data alone. We study the complementarity of LHC and DM indirect searches, working out explicitly the reconstruction of the DM properties for a specific benchmark model in the coannihilation region of a 24-parameters supersymmetric model. Combining mock high-luminosity LHC data with presentday null searches for gamma rays from dwarf galaxies with the Fermi Large Area Telescope, we show that current Fermi Large Area Telescope limits already have the capability of ruling out a spurious wino-like solution which would survive using LHC data only, thus leading to the correct identification of the cosmological solution. We also demonstrate that upcoming Planck constraints on the reionization history will have a similar constraining power and discuss the impact of a possible detection of gamma rays from DM annihilation in the Draco dwarf galaxy with a Cherenkov-Telescope-Array-like experiment. Our results indicate that indirect searches can be strongly complementary to the LHC in identifying the DM particles, even when astrophysical uncertainties are taken into account.
Arina C, Hamann J, Trotta R, et al., 2012, Evidence for dark matter modulation in CoGeNT?, Journal of Cosmology and Astroparticle Physics, Vol: 2012, ISSN: 1475-7516
We investigate the question of whether the recent modulation signal claimed by CoGeNT is best explained by the dark matter (DM) hypothesis from a Bayesian model comparison perspective. We consider five phenomenological explanations for the data: no modulation signal, modulation due to DM, modulation due to DM compatible with the total CoGeNT rate, and a signal coming from other physics with a free phase but annual period, or with a free phase and a free period. In each scenario, we assign to the free parameters physically motivated priors. We find that while the DM models are weakly preferred to the no modulation model, but when compared to models where the modulation is due to other physics, the DM hypothesis is favoured with odds ranging from 185:1 to 560:1. This result is robust even when astrophysical uncertainties are taken into account and the impact of priors assessed. Interestingly, the odds for the DM model in which the modulation signal is compatible with the total rate against a DM model in which this prior is not implemented is only 5:8, in spite of the former's prediction of a modulation amplitude in the energy range 0.9 → 3.0 keVee that is significantly smaller than the value observed by CoGeNT. Classical hypothesis testing also rules out the null hypothesis of no modulation at the 1.6σ to 2.3σ level, depending on the details of the alternative. Lastly, we investigate whether anisotropic velocity distributions can help to mitigate the tension between the CoGeNT total and modulated rates, and find encouraging results.
Bertone G, Cerdeno DG, Fornasa M, et al., 2012, Global fits of the cMSSM including the first LHC and XENON100 data, Journal of Cosmology and Astroparticle Physics, Vol: 2012, ISSN: 1475-7516
We present updated global fits of the constrained Minimal Supersymmetric Standard Model (cMSSM), including the most recent constraints from the ATLAS and CMS detectors at the LHC, as well as the most recent results of the XENON100 experiment. Our robust analysis takes into account both astrophysical and hadronic uncertainties that enter in the calculation of the rate of WIMP-induced recoils in direct detection experiment. We study the consequences for neutralino Dark Matter, and show that current direct detection data already allow to robustly rule out the so-called Focus Point region, therefore demonstrating the importance of particle astrophysics experiments in constraining extensions of the Standard Model of Particle Physics. We also observe an increased compatibility between results obtained from a Bayesian and a Frequentist statistical perspective. We find that upcoming ton-scale direct detection experiments will probe essentially the entire currently favoured region (at the 99% level), almost independently of the statistical approach used. Prospects for indirect detection of the cMSSM are further reduced.
Bertone G, Cumberbatch D, Ruiz de Austri R, et al., 2012, Dark Matter searches: the nightmare scenario, Journal of Cosmology and Astroparticle Physics, Vol: 2012, ISSN: 1475-7516
The unfortunate case where the Large Hadron Collider (LHC) fails to discover physics Beyond the Standard Model (BSM) is sometimes referred to as the ``Nightmare scenario'' of particle physics. We study the consequences of this hypothetical scenario for Dark Matter (DM), in the framework of the constrained Minimal Supersymmetric Standard Model (cMSSM). We evaluate the surviving regions of the cMSSM parameter space after null searches at the LHC, using several different LHC configurations, and study the consequences for DM searches with ton-scale direct detectors and the IceCube neutrino telescope. We demonstrate that ton-scale direct detection experiments will be able to conclusively probe the cMSSM parameter space that would survive null searches at the LHC with 100 fb−1 of integrated luminosity at 14 TeV. We also demonstrate that IceCube (80 strings plus DeepCore) will be able to probe as much as sime 17% of the currently favoured parameter space after 5 years of observation.
Trotta R, 2012, Recent advances in cosmological Bayesian model comparison, Springer Series in Astrostatistics, Pages: 3-15
I review the framework of Bayesian model comparison as applied to cosmological model building. I then discuss some recent developments in the evaluation of the Bayesian evidence, the central quantity for Bayesian model comparison, and present applications to inflationary model building and to constraining the curvature and minimum size of the Universe. I conclude by discussing what I think are some of the open challenges in the field.
Trotta R, 2012, Cosmological bayesian model selection: Recent advances and open challenges, Pages: 127-140, ISSN: 0930-0325
The cosmology community has been increasingly focusing on Bayesian model selection as a tool to discriminate between competing theories to explain a large amount of data about our Universe. In this paper, I summarize the conceptual underpinnings and the algorithmic implementations of Bayesian model comparison. I then discuss two representative applications of Bayesian model comparison to cosmological problems: determining whether the Universe is infinite and selecting the "best" model of inflation. I conclude by offering some reflections about open challenges and interpretational issues. Help and suggestions from the statistics community would be appreciated in further developing the field. © Springer Science+Business Media New York 2013.
March MC, Trotta R, Berkes P, et al., 2011, Improved constraints on cosmological parameters from Type Ia supernova data, Monthly Notices of the Royal Astronomical Society, Vol: 418, Pages: 2308-2329, ISSN: 1365-2966
We present a new method based on a Bayesian hierarchical model to extract constraints on cosmological parameters from Type Ia supernova (SNIa) data obtained with the SALT-II light-curve fitter. We demonstrate with simulated data sets that our method delivers tighter statistical constraints on the cosmological parameters over 90 per cent of the time, that it reduces statistical bias typically by a factor of ∼2–3 and that it has better coverage properties than the usual χ2 approach. As a further benefit, a full posterior probability distribution for the dispersion of the intrinsic magnitude of SNe is obtained. We apply this method to recent SNIa data, and by combining them with cosmic microwave background and baryonic acoustic oscillations data, we obtain Ωm= 0.28 ± 0.02, ΩΛ= 0.73 ± 0.01 (assuming w=−1) and Ωm= 0.28 ± 0.01, w=−0.90 ± 0.05 (assuming flatness; statistical uncertainties only). We constrain the intrinsic dispersion of the B-band magnitude of the SNIa population, obtaining σintμ= 0.13 ± 0.01 mag. Applications to systematic uncertainties will be discussed in a forthcoming paper.
Trotta R, Cranmer K, 2011, Statistical challenges of global SUSY fits
We present recent results aiming at assessing the coverage properties of Bayesian and frequentist inference methods, as applied to the reconstruction of supersymmetric parameters from simulated LHC data. We discuss the statistical challenges of the reconstruction procedure, and highlight the algorithmic difficulties of obtaining accurate profile likelihood estimates. Copyright © CERN, 2011.
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Eugenia Cabrera M, Alberto Casas J, Ruiz de Austri R, et al., 2011, Quantifying the tension between the Higgs mass and (<i>g</i>-2)<sub>μ</sub> in the constrained MSSM, PHYSICAL REVIEW D, Vol: 84, ISSN: 1550-7998
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March MC, Trotta R, Amendola L, et al., 2011, Robustness to systematics for future dark energy probes, Monthly Notices of the Royal Astronomical Society, Vol: 415, Pages: 143-152, ISSN: 1365-2966
We extend the figure of merit formalism usually adopted to quantify the statistical performance of future dark energy probes to assess the robustness of a future mission to plausible systematic bias. We introduce a new robustness figure of merit which can be computed in the Fisher matrix formalism given arbitrary systematic biases in the observable quantities. We argue that robustness to systematics is an important new quantity that should be taken into account when optimizing future surveys. We illustrate our formalism with toy examples, and apply it to future Type Ia supernova (SN Ia) and baryonic acoustic oscillation (BAO) surveys. For the simplified systematic biases that we consider, we find that SNe Ia are a somewhat more robust probe of dark energy parameters than the BAO. We trace this back to a geometrical alignment of systematic bias direction with statistical degeneracy directions in the dark energy parameter space.
Trotta R, Kunz M, Liddle AR, 2011, Designing decisive detections, Monthly Notices of the Royal Astronomical Society, Vol: 414, Pages: 2337-2344, ISSN: 1365-2966
We present a general Bayesian formalism for the definition of figures of merit (FoMs) quantifying the scientific return of a future experiment. We introduce two new FoMs for future experiments based on their model selection capabilities, called the decisiveness of the experiment and the expected strength of evidence. We illustrate these by considering dark energy probes and compare the relative merits of stages II, III and IV dark energy probes. We find that probes based on supernovae and on weak lensing perform rather better on model selection tasks than is indicated by their Fisher matrix FoM as defined by the Dark Energy Task Force. We argue that our ability to optimize future experiments for dark energy model selection goals is limited by our current uncertainty over the models and their parameters, which is ignored in the usual Fisher matrix forecasts. Our approach gives a more realistic assessment of the capabilities of future probes and can be applied in a variety of situations.
Feroz F, Cranmer K, Hobson M, et al., 2011, Challenges of profile likelihood evaluation in multi-dimensional SUSY scans, Journal of High Energy Physics, Vol: 2011, ISSN: 1126-6708
Statistical inference of the fundamental parameters of supersymmetric theoriesis a challenging and active endeavor. Several sophisticated algorithms have been employedto this end. While Markov-Chain Monte Carlo (MCMC) and nested sampling techniquesare geared towards Bayesian inference, they have also been used to estimate frequentistconfidence intervals based on the profile likelihood ratio. We investigate the performanceand appropriate configuration of MultiNest, a nested sampling based algorithm, whenused for profile likelihood-based analyses both on toy models and on the parameter spaceof the Constrained MSSM. We find that while the standard configuration previously usedin the literarture is appropriate for an accurate reconstruction of the Bayesian posterior,the profile likelihood is poorly approximated. We identify a more appropriate MultiNestconfiguration for profile likelihood analyses, which gives an excellent exploration of theprofile likelihood (albeit at a larger computational cost), including the identification of theglobal maximum likelihood value. We conclude that with the appropriate configurationMultiNest is a suitable tool for profile likelihood studies, indicating previous claims tothe contrary are not well founded.
Vardanyan M, Trotta R, Silk J, 2011, Applications of Bayesian model averaging to the curvature and size of the Universe, Monthly Notices of the Royal Astronomical Society, Vol: 413, Pages: L91-L95, ISSN: 1365-2966
Bayesian model averaging is a procedure to obtain parameter constraints that account for the uncertainty about the correct cosmological model. We use recent cosmological observations and Bayesian model averaging to derive tight limits on the curvature parameter, as well as robust lower bounds on the curvature radius of the Universe and its minimum size, while allowing for the possibility of an evolving dark energy component. Because flat models are favoured by Bayesian model selection, we find that model-averaged constraints on the curvature and size of the Universe can be considerably stronger than non-model-averaged ones. For the most conservative prior choice (based on inflationary considerations), our procedure improves on non-model-averaged constraints on the curvature by a factor of ∼2. The curvature scale of the Universe is conservatively constrained to be Rc > 42 Gpc (99 per cent), corresponding to a lower limit to the number of Hubble spheres in the Universe NU > 251 (99 per cent).
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