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Author Balazs, C. et al; Mamuzic, J.; Ruiz de Austri, R. url  doi
openurl 
  Title A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications Type Journal Article
  Year 2021 Publication Journal of High Energy Physics Abbreviated Journal J. High Energy Phys.  
  Volume 05 Issue 5 Pages 108 - 46pp  
  Keywords Phenomenology of Field Theories in Higher Dimensions; Supersymmetry Phenomenology  
  Abstract Optimisation problems are ubiquitous in particle and astrophysics, and involve locating the optimum of a complicated function of many parameters that may be computationally expensive to evaluate. We describe a number of global optimisation algorithms that are not yet widely used in particle astrophysics, benchmark them against random sampling and existing techniques, and perform a detailed comparison of their performance on a range of test functions. These include four analytic test functions of varying dimensionality, and a realistic example derived from a recent global fit of weak-scale supersymmetry. Although the best algorithm to use depends on the function being investigated, we are able to present general conclusions about the relative merits of random sampling, Differential Evolution, Particle Swarm Optimisation, the Covariance Matrix Adaptation Evolution Strategy, Bayesian Optimisation, Grey Wolf Optimisation, and the PyGMO Artificial Bee Colony, Gaussian Particle Filter and Adaptive Memory Programming for Global Optimisation algorithms.  
  Address [Balazs, Csaba] Monash Univ, Sch Phys & Astron, Melbourne, Vic 3800, Australia, Email: bstienen@science.ru.nl;  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (up) 1029-8479 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000762408900002 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 5149  
Permanent link to this record
 

 
Author Caron, S.; Ruiz de Austri, R.; Zhang, Z.Y. url  doi
openurl 
  Title Mixture-of-Theories training: can we find new physics and anomalies better by mixing physical theories? Type Journal Article
  Year 2023 Publication Journal of High Energy Physics Abbreviated Journal J. High Energy Phys.  
  Volume 03 Issue 3 Pages 004 - 37pp  
  Keywords Specific BSM Phenomenology; Supersymmetry  
  Abstract Model-independent search strategies have been increasingly proposed in recent years because on the one hand there has been no clear signal for new physics and on the other hand there is a lack of a highly probable and parameter-free extension of the standard model. For these reasons, there is no simple search target so far. In this work, we try to take a new direction and ask the question: bearing in mind that we have a large number of new physics theories that go beyond the Standard Model and may contain a grain of truth, can we improve our search strategy for unknown signals by using them “in combination”? In particular, we show that a signal hypothesis based on a large, intermingled set of many different theoretical signal models can be a superior approach to find an unknown BSM signal. Applied to a recent data challenge, we show that “mixture-of-theories training” outperforms strategies that optimize signal regions with a single BSM model as well as most unsupervised strategies. Applications of this work include anomaly detection and the definition of signal regions in the search for signals of new physics.  
  Address [Caron, Sascha; Zhang, Zhongyi] Radboud Univ Nijmegen, High Energy Phys, Heyendaalseweg 135, NL-6525 AJ Nijmegen, Netherlands, Email: scaron@nikhef.nl;  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (up) 1029-8479 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000943095100001 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 5494  
Permanent link to this record
 

 
Author Jueid, A.; Kip, J.; Ruiz de Austri, R.; Skands, P. url  doi
openurl 
  Title The Strong Force meets the Dark Sector: a robust estimate of QCD uncertainties for anti-matter dark matter searches Type Journal Article
  Year 2024 Publication Journal of High Energy Physics Abbreviated Journal J. High Energy Phys.  
  Volume 02 Issue 2 Pages 119 - 48pp  
  Keywords Cosmic Rays; Particle Nature of Dark Matter; Specific QCD Phenomenology  
  Abstract In dark-matter annihilation channels to hadronic final states, stable particles – such as positrons, photons, antiprotons, and antineutrinos – are produced via complex sequences of phenomena including QED/QCD radiation, hadronisation, and hadron decays. These processes are normally modelled by Monte Carlo (MC) event generators whose limited accuracy imply intrinsic QCD uncertainties on the predictions for indirect-detection experiments like Fermi-LAT, Pamela, IceCube or Ams-02. In this article, we perform a comprehensive analysis of QCD uncertainties, meaning both perturbative and nonperturbative sources of uncertainty are included – estimated via variations of MC renormalization-scale and fragmentation-function parameters, respectively – in antimatter spectra from dark-matter annihilation, based on parametric variations of the Pythia 8 event generator. After performing several retunings of light-quark fragmentation functions, we define a set of variations that span a conservative estimate of the QCD uncertainties. We estimate the effects on antimatter spectra for various annihilation channels and final-state particle species, and discuss their impact on fitted values for the dark-matter mass and thermally-averaged annihilation cross section. We find dramatic impacts which can go up to O(10%) for the annihilation cross section. We provide the spectra in tabulated form including QCD uncertainties and code snippets to perform fast dark-matter fits, in this github repository.  
  Address [Jueid, Adil] Inst Basic Sci IBS, Ctr Theoret Phys Universe, Particle Theory & Cosmol Grp, Daejeon 34126, South Korea, Email: adiljueid@ibs.re.kr;  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (up) 1029-8479 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:001165531600003 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 5956  
Permanent link to this record
 

 
Author Cabrera, M.E.; Casas, J.A.; Ruiz de Austri, R. url  doi
openurl 
  Title MSSM forecast for the LHC Type Journal Article
  Year 2010 Publication Journal of High Energy Physics Abbreviated Journal J. High Energy Phys.  
  Volume 05 Issue 5 Pages 043 - 48pp  
  Keywords Beyond Standard Model; Supersymmetric Effective Theories  
  Abstract We perform a forecast of the MSSM with universal soft terms (CMSSM) for the LHC, based on an improved Bayesian analysis. We do not incorporate ad hoc measures of the fine-tuning to penalize unnatural possibilities: such penalization arises from the Bayesian analysis itself when the experimental value of M-Z is considered. This allows to scan the whole parameter space, allowing arbitrarily large soft terms. Still the low-energy region is statistically favoured (even before including dark matter or g-2 constraints). Contrary to other studies, the results are almost unaffected by changing the upper limits taken for the soft terms. The results are also remarkable stable when using flat or logarithmic priors, a fact that arises from the larger statistical weight of the low-energy region in both cases. Then we incorporate all the important experimental constrains to the analysis, obtaining a map of the probability density of the MSSM parameter space, i.e. the forecast of the MSSM. Since not all the experimental information is equally robust, we perform separate analyses depending on the group of observables used. When only the most robust ones are used, the favoured region of the parameter space contains a significant portion outside the LHC reach. This effect gets reinforced if the Higgs mass is not close to its present experimental limit and persits when dark matter constraints are included. Only when the g-2 constraint (based on e(+)e(-) data) is considered, the preferred region (for μ> 0) is well inside the LHC scope. We also perform a Bayesian comparison of the positive- and negative-mu possibilities.  
  Address [Eugenia Cabrera, Maria; Alberto Casas, J.] UAM, IFT UAM CSIC, Inst Fis Teor, Madrid 28049, Spain, Email: maria.cabrera@uam.es  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (up) 1126-6708 ISBN Medium  
  Area Expedition Conference  
  Notes ISI:000278251300005 Approved no  
  Is ISI yes International Collaboration no  
  Call Number IFIC @ elepoucu @ Serial 435  
Permanent link to this record
 

 
Author Bridges, M.; Cranmer, K.; Feroz, F.; Hobson, M.; Ruiz de Austri, R.; Trotta, R. url  doi
openurl 
  Title A coverage study of the CMSSM based on ATLAS sensitivity using fast neural networks techniques Type Journal Article
  Year 2011 Publication Journal of High Energy Physics Abbreviated Journal J. High Energy Phys.  
  Volume 03 Issue 3 Pages 012 - 23pp  
  Keywords Supersymmetry; Phenomenology  
  Abstract We assess the coverage properties of confidence and credible intervals on the CMSSM parameter space inferred from a Bayesian posterior and the profile likelihood based on an ATLAS sensitivity study. In order to make those calculations feasible, we introduce a new method based on neural networks to approximate the mapping between CMSSM parameters and weak-scale particle masses. Our method reduces the computational effort needed to sample the CMSSM parameter space by a factor of similar to 10(4) with respect to conventional techniques. We find that both the Bayesian posterior and the profile likelihood intervals can significantly over-cover and identify the origin of this effect to physical boundaries in the parameter space. Finally, we point out that the effects intrinsic to the statistical procedure are conflated with simplifications to the likelihood functions from the experiments themselves.  
  Address [Bridges, Michael; Feroz, Farhan; Hobson, Mike] Univ Cambridge, Cavendish Lab, Astrophys Grp, Cambridge CB3 0HE, England, Email: mb435@mrao.cam.ac.uk  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (up) 1126-6708 ISBN Medium  
  Area Expedition Conference  
  Notes ISI:000289295200012 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 610  
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