1 / 22

CMSSW

CMSSW. ROOT. Monte-Carlo methods are used to: - evaluate difficult integrals examples : cross section, decay rate - sample complicated distribution function The simplest pdf (probablity distribution function): uniform

Télécharger la présentation

CMSSW

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. P.Miné Monte Carlo

  2. P.Miné Monte Carlo

  3. P.Miné Monte Carlo

  4. P.Miné Monte Carlo

  5. CMSSW ROOT P.Miné Monte Carlo

  6. Monte-Carlo methods are used to: - evaluate difficult integrals examples : cross section, decay rate - sample complicated distribution function The simplest pdf (probablity distribution function): uniform 0 < x < 1 all values of x have the same probabilty f(x) =1 Programs exist to give a value of x at each call: Example: TRandom in ROOT P.Miné Monte Carlo

  7. Random generators simple cases Exponential distribution (decay time of a particle) f(t) = (1 / τ ) exp(- t / τ ) in the interval [a, b] let a’ = exp(- a / τ ) and b’ = exp(- b / τ ) t = - τ ln ( b’ + u ( a’–b’) ) where u is uniform in [0, 1] then dN / dt = (dN / du) (du / dt) = 1 x (1 / τ ) exp(- t / τ ) / (a’ –b’) For [0 , ∞] take simply t = - τ ln u This method works every time the primitive F(x) of f(x) is invertible x = F-1(u) where u is a uniform probability variable P.Miné Monte Carlo

  8. Isotropy in 3D Density is proportional to solid angle dΩ = d(cosθ) dϕ cosθ is uniform in [-1, 1] : take 2u1 -1 ϕ uniform in [0, 2π] Gaussian variable are gaussian independent P.Miné Monte Carlo

  9. The acceptance rejection method (Von Neumann) • Assume that for any x, the probability distribution function f(x) can be calculated and its maximum C is known Take the envelope Ch(x) ,h(x) is uniform f(x) and h(x) are normalized so C > 1 For each value of x, generate the random variable u, with uniform distribution If u C h (x) < f(x) , accept x ; if not , reject Try again P.Miné Monte Carlo

  10. Importance sampling Increase the efficiency if C >> 1 by changing h(x) The best solution: change the variable (Jacobian method) P.Miné Monte Carlo

  11. P.Miné Monte Carlo

  12. P.Miné Monte Carlo

  13. P.Miné Monte Carlo

  14. P.Miné Monte Carlo

  15. P.Miné Monte Carlo

  16. ν l W t b P.Miné Monte Carlo

  17. P.Miné Monte Carlo

  18. P.Miné Monte Carlo

  19. Proton proton cross section P.Miné Monte Carlo

  20. Parton density functions are obtained experimentally by deep inelastic scattering of leptons P.Miné Monte Carlo

  21. P.Miné Monte Carlo

  22. Practiceon ROOT Dowload from root.cern.ch/root/root_v5.22.00.source.tar.gz gzip -dc root-v5.22.00.source.tar.gz | tar –xf export ROOTSYS=<path>/root ./configure –help ./configure [<arch>] [set arch appropriately if not default] (g)make export PATH=$ROOTSYS/bin:$PATH export LD_LIBRARY_PATH=$ROOTSYS/lib:$LD_LIBRARY_PATH Exercise in tutorials P.Miné Monte Carlo

More Related