30 Pages

martin

Course: LOOPFEST 6, Fall 2009
School: SUNY Buffalo
Rating:
 
 
 
 
 

Word Count: 2592

Document Preview

physical Refined masses in supersymmetry Loopfest VI April 17, 2007 Stephen P. Martin Northern Illinois University and Fermilab I will report on refined calculations of the gluino, squark and Higgs masses in the MSSM beyond leading order. Based in part on: hep-ph/0701051 hep-ph/0608026 hep-ph/0501132 (TSIL) with Dave Robertson Masses are key observables for deciphering SUSY breaking Most of what we do not...

Register Now

Unformatted Document Excerpt

Coursehero >> New York >> SUNY Buffalo >> LOOPFEST 6

Course Hero has millions of student submitted documents similar to the one
below including study guides, practice problems, reference materials, practice exams, textbook help and tutor support.

Course Hero has millions of student submitted documents similar to the one below including study guides, practice problems, reference materials, practice exams, textbook help and tutor support.
physical Refined masses in supersymmetry Loopfest VI April 17, 2007 Stephen P. Martin Northern Illinois University and Fermilab I will report on refined calculations of the gluino, squark and Higgs masses in the MSSM beyond leading order. Based in part on: hep-ph/0701051 hep-ph/0608026 hep-ph/0501132 (TSIL) with Dave Robertson Masses are key observables for deciphering SUSY breaking Most of what we do not already know about supersymmetric extensions of the Standard Model involves the soft SUSY-breaking terms with positive mass dimension. Predictions of specific models (Minimal Supergravity, Gauge Mediation, Anomaly Mediation, Extra-dimensional Mediation, ...) allow/require precise calculations. 60 50 40 1 -1 The apparent unification of gauge couplings in the MSSM invites us to -1 30 2 20 10 0 -1 extrapolate the soft masses up to high scales, to see if they obey some 3 2 -1 Organizing Principle. 6 8 10 12 14 Log10(Q/1 GeV) 16 18 4 What is the Organizing Principle behind SUSY breaking? A reasonable working hypothesis is the Minimal Flavor-Respecting Supersymmetric Standard Model. It is neither too painfully general, nor too naively specific: Minimal Supergravity (sic) General MSSM 105 new parameters MFRSSM no new flavor or CP violation Gauge-Mediated SUSY Breaking Anomaly-Mediated SUSY Breaking Stuff not thought of yet MFRSSM parameter count: 3 gaugino masses 5 sfermion (mass) 3 2 M1 , M 2 , M 3 m2 , m2 , m2 , m2 , m2 ~ ~ ~ u ~ e ~ Q L d Au0 , Ad0 , Ae0 , b, m2 u , m2 d (but MZ known) H H Q0 3 (scalar) couplings 3 Higgs mass parameters 1 input RG scale Total: 15 new parameters beyond the Standard Model Gaugino Mass Unification is a popular and recurring theme. M1 (Q) = M2 (Q) = M3 (Q) m1/2 resulting in at Q 2 1016 GeV, M1 : M2 : M3 1 : 2 : 6 for Q near the TeV scale. To test this, or alternatives to it, we have to relate physical masses to running masses in the Lagrangian (with no superpartners decoupled). Goal: reduce purely theoretical sources of uncertainty to a negligible level, if possible. Key features of the approach: To test high-scale organizing principles, work in non-decoupled SUSY theory with DR (mass-independent) renormalization scheme. Two-loop diagrams relevant for SUSY involve many comparable but distinct mass scales simultaneously. Mass orderings and hierarchies are difficult to anticipate in advance. So don't try. Methods should be generic and reusable. To calculate physical masses Evaluate self-energy = sum of 1-particle irreducible Feynman diagrams: (s) = (1) (s) + (2) (s) + . . . where s = the external momentum invariant. The complex pole mass spole = M 2 - iM is the solution for complex s of: spole = = m2 + (spole ) tree m2 + (1) (m2 ) 1 + (1) (m2 ) + (2) (m2 ) + . . . tree tree tree tree The pole mass is gauge invariant at each order in perturbation theory, can be related to kinematic masses as measured at colliders. In the MSSM, squarks, charginos, neutralinos, Higgs scalars can mix: j k (s) = j (1)k (s) + j (2)k (s) + . . . Define functions of the DR masses and couplings: j j (1)k = = smj +i lim 2 j (1)k (s) + j (1)k (2)k smj +i lim 2 j (2)k (1)k j s Then the complex pole mass is a gauge-invariant observable: 2 Mj - ij Mj = m2 + j j (1)k + j (2)k + k=j j (1)k k (1)j /(m2 - m2 ) + . . . j k However, with tree-level masses in the loop integrals, the kinematics can lead to slow convergence of perturbation theory. . . For example, the imaginary parts of the one-loop contribution: 2 1 3 will turn on when the DR tree-level masses satisfy: m1 > m2 + m3 However, the physical width should actually be non-zero only for physical masses satisfying: M1 > M2 + M3 . This can be particularly troublesome for the gluino, squarks and h0 , where the tree-level masses differs greatly from the physical masses. In some cases, the computed in the pole mass can even be negative. To address this, re-expand the pole mass corrections by defining new functions at each loop order L: (L)k j = j (L)k with all tree-level masses replaced by the real parts of pole masses The pole mass can then be rewritten as: 2 Mj - ij Mj = m2 + j j - k (1)k + j (1)k (2)k + k=j j (1)k k (1)j 2 2 /(Mj - Mk ) Re[k ] (1)j j + ... 2 Mk This is formally equivalent to the previous expression, up to terms of 3-loop order. It is similar to the on-shell mass or pole mass computed in the on-shell scheme, but with all couplings and mixing matrices computed at tree-level in DR. In the following, I will refer to the first method as "expansion around tree masses", and the method just given as "expansion around pole masses". Method: Reduce all self-energies in general theory to a few basis integrals Numerically evaluate basis integrals quickly and reliably for arbitrary masses. Tarasov's basis and recurrence relations: S T U M ("Master integral") Can always reduce 2-loop self-energies to a linear combination of these, with coefficients rational functions of: s = p2 = external momentum invariant x, y, z, . . . = internal propagator masses To evaluate basis integrals: Values at s = 0 are known analytically, in terms of logs, polylogs. (basis integral) = (another self-energy integral) s = (linear combination of basis integrals) So, we have a set of coupled, first-order, linear differential equations. x Consider the Master integral M (x, y, z, u, v): y v u z Call these 13 integrals In , (n and the 12 U, S, T basis integrals obtained from it by removing propagators. = 1, . . . , 13). Differential equations method for basis integrals d In = ds Knm Im + Cn m Here Knm are rational functions of s and x, y, z . . ., and Cn are one-loop integrals. These are obtained by using Tarasov's recursion relations. Solve for basis integrals Runge-Kutta complex integration In using in the Im[s] s-plane, starting from known values at s = 0. thresholds Re[s] TSIL = Two-Loop Self-energy Integral Library D.G. Robertson, SPM, hep-ph/0501132 Program written in C, callable from C++, Fortran Basis integrals computed for any values of all masses and s. All subordinate integrals (S, T, U ) for a given master integral (M ) are obtained together in a single numerical computation. Checks on the numerical accuracy follow from changing choice of contour. TSIL knows all special cases that have been done analytically in terms of polylogarithms Computation times generically 1 second on modern hardware. About 5 to 10 times faster than s2lse, typically. For the SUSYQCD corrections to the gluino (g ) pole mass, here are the only ~ necessary Master topologies: 0 g ~ g ~ g ~ g ~ g ~ 0 Q g ~ ~ Qj g ~ Q ~ Qk 0 0 g ~ 0 (analytic) g ~ (numerical) If the first two family squarks are (are not) degenerate, then 12 (18) numerical evaluations by TSIL are required, after taking into account the symmetry. (Note: number of Feynman diagrams is far greater.) BF S BF V MSSF F S MSF F SF VF SSSS These are the Feynman diagram VSF F F S VF SSF F YF SSS MV SF F S MV F F SF topologies: MSSF F V VF SSSV VSF F F V VV F F F S YF SSV () Each diagram can be reduced to integrals calculated as part of VV F F F V MV F F V F MV V F F V VF V V V V YF V V V () the Master integral cases on the previous slide. VF V V F F VF V V SS YF V V S + fermion mass insertions + ghost diagrams + counterterms The same integrals have been applied to obtain the SUSYQCD neutralino and chargino pole mass calculation. (SPM hep-ph/0509115; see also the independent calculation of Robert Schofbeck, next talk.) ~ For the SUSYQCD corrections to the squark (Qj ) pole mass, the necessary Master topologies are: ~ Qj 0 ~ Qj ~ Qj ~ Qj 0 g ~ g ~ Q ~ Qj 0 0 ~ Qj 0 ~ Qj Q Q ~ ~ g Qj Q 0 g ~ Q Q (analytic) g ~ g ~ ~ Qk (numerical) ~ Qj 0 ~ Qj ~ Qj 0 ~ Qj ~ Qj g ~ Q Assuming small flavor violation, each of the 12 squark pole masses requires 3 (4) numerical evaluations of TSIL, if there is (is not) L-R squark mixing. Examples of reduction to basis integrals: x s z v y u MF F F F S (x, y, z, u, v) = (xu + yz - vs)M (x, y, z, u, v) - xU (z, x, y, v) - zU (x, z, u, v) -uU (y, u, z, v) - yU (u, y, x, v) + S(x, u, v) + S(y, z, v) +sB(x, z)B(y, u) v y u x = {(v - y - u)U (x, z, u, v) + [A(v) - A(u)]B(x, y)}/(y - z) + (y z) etc. The same functions are also applicable to Higgs and slepton self-energies. z VF F F F S (x, y, z, u, v) Numerical results for gluino and squark pole masses For simplicity of presentation, take all squarks degenerate and unmixed, and all quarks (including top!) massless. 1.30 1.25 1.20 1.15 1.10 1.05 1.00 0.95 1 1.05 Msquark/msquark, running Mgluino/mgluino, running The "expansion around pole" method is probably most accurate. (The two-loop gluino pole mass in the case of no squark mixing was computed independently and first by Youichi Yamada.) e e e e e o r p t d d d n n n up u u oe o o rl au arl a .o . pd pt p p xo x en e e ,a , pe p p p oo o o o orlrlr o,, o x x 1l2l 1.. 2 1.00 2 Msquark/Mgluino e e e e e o r p t d d d n n n up u u oe o o rl au arl a .o . pd pt p p xo x en e e ,a , pe p p p orrr o o o oo o,, o x x 1l2l 1l.l. 2 1 2 Msquark/Mgluino 3 0.95 3 The widths of the gluino and squark pole masses as extracted from the complex pole squared masses M 2 0.015 - iM : e e e o r p t d d n n u u o o r al a . p p x x e e , p p o, o or o 2l.l 2 e e e o r p t d d n n u u o o r al a . p p x x e e , p p o, o or o 2l.l 2 0.004 0.003 squark/Msquark 0.9 Msquark/Mgluino gluino/Mgluino 0.010 0.002 0.005 0.001 0.000 1 0.000 1 Msquark/Mgluino 1.1 The "expansion around tree" method fails to give a width consistent with kinematics near the threshold region, as remarked earlier. The "expansion around pole" method is consistent with kinematics, and gives exact agreement with the NLO gluino and squark decay widths found in Beenakker, Hopker, Zerwas hep-ph/9602378. Three-loop gluino mass corrections for heavy squarks Exploit the fact that beta functions are easier to compute, known to 3-loop order. Let the running parameters in the full MSSM be s , M3 , and in the effective theory with squarks decoupled, s , M3 . Full MSSM, no decoupling Msquarks L = ln( Msquarks M3 2-loop threshold corrections give (s , M3 ) (s , M3 ) ) 3-loop s and M3 beta functions known (same as "Split SUSY") pole 3-loop Mg known in terms of s and M3 ~ Standard Model: 4-loop QCD beta function known Mg ~ Mtop To obtain the 3-loop contributions for large L = ln(MQ /Mg ), need: ~ ~ 2-loop threshold corrections for M3 in MSSM (SPM 2006) 2-loop threshold corrections for s in MSSM (Bern, DeFreitas, Dixon, Wong 2002; Harlander, Mihaila, Steinhauser 2005) 2-loop pole mass in a theory with only fermions (Gray, Broadhurst, Grafe, Schilcher 1990) 3-loop mass beta function in a theory with only fermions but in different reps (Tarasov 1982, unpublished, available from KEK server, only in Russian!) In addition, using: 3-loop pole mass in a theory with only fermions (Melnikov and van Ritbergen 1999) but with different reps I obtain the subset of non-log-enhanced contributions that don't involve heavy particle loops, which I expect will be the most important. Using the effective field theory matching and RG running technique, one obtains n s Ln n s Ln-1 n s Ln-2 1-loop functions, 0-loop threshold matching 2-loop functions, 1-loop threshold matching 3-loop functions, 2-loop threshold matching for all n = loop order. S 3 = 0.163 + 1.112 + 3.74 + 9.62 + ? m2 g ~ 4 = ln(m2 /m2 ). ~ g ~ Q S 3 2 0.16L3 - 1.16L2 - 2.16L + 5.99 + ? Mg = ~ 4 2 2 = ln(MQ /Mg ). ~ ~ For the "expand around tree" method, 2 3-loop Mg , ~ For the "expand around poles" method, 2 3-loop Mg ~ L Here, "?" means non-log-enhanced contributions from heavy sparticle loops. Adding these 3-loop effects to the preceding 2-loop results: 1.35 1.30 Mgluino/mgluino, running 1.25 e e e e e o r p t d d d np n n uo u u odl olt o re ao ar a .n pu p p p xe x e, e e ,a prrr p o.. o, o o oxl2l ox o. o 2p 2l2p 2l2, 3 3 2 3 Msquark/Mgluino 1.20 1.15 1.10 1 4 5 The two three-loop approximations are much closer to each other, as expected. The difference between the two-loop and three-loop "expand around pole" results is also quite small. RG scale dependence of various approximations, fo...

Find millions of documents on Course Hero - Study Guides, Lecture Notes, Reference Materials, Practice Exams and more. Course Hero has millions of course specific materials providing students with the best way to expand their education.

Below is a small sample set of documents:

Rutgers - ECE - 331
Electrical and Computer Engineering Department14:332:331Computer Architecture and Assembly LanguageFall 2008Grigore C. Burdea Ph.D. Professor, ECE Department, Rutgers University.http:/www.caip.rutgers.edu/vrlab/ Burdea@caip.rutgers.eduCourse
SUNY Buffalo - CSE - 626
A Priori Algorithm for Association Rule Learning Association rule is a representation for local patterns in data mining What is an Association Rule? It is a probabilistic statement about the co-occurrence of certain events in the data base Partic
SUNY Buffalo - CSE - 626
Pattern Structures1Pattern Structures Models describe whole or a large part of the data Pattern characterizes some local aspect of the data Pattern is a predicate that returns true for those objects or parts of objects in the data for which th
SUNY Buffalo - CSE - 15
Concept LearningLearning Concepts from Examples Concept typically means categorization based on features1A Concept Learning TaskFour Examples:Example 1 2 3 4 Sky Sunny Sunny Rainy Sunny AirTemp Warm Warm Cold Warm Humidity Normal High High Hig
SUNY Buffalo - CSE - 574
Concept LearningLearning Concepts from Examples Concept typically means categorization based on features1A Concept Learning TaskFour Examples:Example 1 2 3 4 Sky Sunny Sunny Rainy Sunny AirTemp Warm Warm Cold Warm Humidity Normal High High Hig
SUNY Buffalo - CSE - 574
Machine Learning OverviewSargur N. SrihariUniversity at Buffalo, State University of New York USA1Outline1. What is Machine Learning (ML)? 2. Types of Information Processing Problems Solved1. 2. 3. 4. Regression Classification Clustering Mod
SUNY Buffalo - CSE - 574
Machine LearningSrihariMixture Density Networks and Bayesian Neural NetworksSargur Srihari 1Machine LearningSrihariMixture Density Networks Gaussian assumption can lead to poor results In regression p(t|x) is typicall
SUNY Buffalo - CSE - 555
CSE555: Introduction to Pattern Recognition Spring, 2007 Mid-Term Exam(100 points, Closed book/notes) The last page contains some formulas that might be useful. 1. Part(i) (10 pts) Suppose a bank classies customers as either good or bad credit risks
SUNY Buffalo - CSE - 555
CSE555: Introduction to Pattern Recognition Midterm Exam Solution(100 points, Closed book/notes) There are 5 questions in this exam. The last page is the Appendix that contains some useful formulas. 1. (15pts) Bayes Decision Theory. (a) (5pts) Assum
SUNY Buffalo - CSE - 17
Machine Learning, Chapter 7, Part 2CSE 574, Spring 2004Computational Learning Theory (VC Dimension)1. Difficulty of machine learning problems 2. Capabilities of machine learning algorithms1Machine Learning, Chapter 7, Part 2CSE 574, Spring
SUNY Buffalo - CSE - 574
Machine Learning, Chapter 7, Part 2CSE 574, Spring 2004Computational Learning Theory (VC Dimension)1. Difficulty of machine learning problems 2. Capabilities of machine learning algorithms1Machine Learning, Chapter 7, Part 2CSE 574, Spring
SUNY Buffalo - CSE - 14
Introduction to BoostingCynthia Rudin PACM, Princeton UniversityAdvisorsIngrid Daubechies and Robert SchapireSay you have a database of news articles.( ( ( (, +1 , +1 , +1 , +1) ) ) )( ( ( (, +1 , +1 , +1 , +1) ) ) )( ( ( (, -1
SUNY Buffalo - CSE - 574
Introduction to BoostingCynthia Rudin PACM, Princeton UniversityAdvisorsIngrid Daubechies and Robert SchapireSay you have a database of news articles.( ( ( (, +1 , +1 , +1 , +1) ) ) )( ( ( (, +1 , +1 , +1 , +1) ) ) )( ( ( (, -1
SUNY Buffalo - CSE - 555
Unsupervised Learning and ClusteringWhy consider unlabeled samples?1. Collecting and labeling large set of samples is costlyGetting recorded speech is free, labeling is time consuming2. Classifier could be designed on small set of labeled sampl
SUNY Buffalo - CSE - 555
Discriminant Analysis1. Fisher Linear Discriminant 2. Multiple Discriminant AnalysisCSE 555: Srihari0MotivationProjection that best separates the data in a leastsquares sense PCA finds components that are useful for representing data Howev
SUNY Buffalo - MAE - 381
SUNY Buffalo - MAE - 381
SUNY Buffalo - MAE - 381
SUNY Buffalo - MAE - 381
SUNY Buffalo - MAE - 381
SUNY Buffalo - MAE - 381
SUNY Buffalo - MAE - 381
SUNY Buffalo - MAE - 381
SUNY Buffalo - MAE - 381
SUNY Buffalo - MAE - 381
SUNY Buffalo - MAE - 381
SUNY Buffalo - MAE - 381
SUNY Buffalo - MAE - 381
SUNY Buffalo - MAE - 381
SUNY Buffalo - MAE - 443
MAE 443/543 Continuous Control Homework 1 Solutions1) Determine the Laplace Transform for the following time functions from first principles i)f (t ) = 2 e 4 t L[ f (t )] = F ( s ) = 2 e 4t e st dt = 2 e(4 s ) t dt = 2[0 0 1 ( s 4) t 2
SUNY Buffalo - MAE - 19
SUNY Buffalo - MAE - 381
SUNY Buffalo - MAE - 19
SUNY Buffalo - MAE - 19
SUNY Buffalo - MAE - 381
SUNY Buffalo - MAE - 381
SUNY Buffalo - MAE - 381
SUNY Buffalo - WECHSLER - 2008
No. 07-9999In the Supreme Court of the United StatesOctober Term, 2007_ Patrick Kennedy, PETITIONER, v. Louisiana, RESPONDENT. _ ON WRIT OF CERTIORARI TO THE UNITED STATES SUPREME COURT FOR THE SUPREME COURT OF LOUISIANNABRIEF FOR PETITIONER
SUNY Buffalo - WECHSLER - 2006
STATEMENT OF JURISDICTION This Court has jurisdiction of Petitioner's appeal pursuant to 28 U.S.C. 1291. Petitioner is in violation of 18 U.S.C. 922(g)(1), which makes it a crime for a felon to possess a firearm, which has traveled in interstate or
Rutgers - V - 11
Rutgers - V - 13
Rutgers - V - 01
Rutgers - V - 02
Rutgers - V - 02
Rutgers - V - 09
Rutgers - V - 09
Rutgers - V - 13
Rutgers - V - 12
Rutgers - V - 10
Rutgers - V - 08
Rutgers - V - 13
Rutgers - V - 02
Rutgers - V - 08
Rutgers - V - 10
Rutgers - V - 05
Rutgers - V - 06
Rutgers - MS - 320
Watercolors in the Coastal ZoneWhat Can We See?BY OSCAR SCHOFIELD, ROBERT ARNONE, PA U L B I S S E T T, T O M M Y D I C K E Y, C U R T D A V I S , Z O E F I N K E L , M AT T H E W O L I V E R , A N D M A R K A . M O L I N EThe Role of Optics in O
Rutgers - WEEK - 7
Proc. Natl. Acad. Sci. USA Vol. 95, pp. 606611, January 1998 EvolutionOrigin of the metazoan phyla: Molecular clocks confirm paleontological estimates FRANCISCO JOSE AYALA*, ANDREY RZHETSKY,ANDFRANCISCO J. AYALA*Institute of Molecular Evoluti
Rutgers - HISTEARTHS - 2006
What were the Constraints on the Earliest Photolithotrophs on Land?Implicit comparison with marine/freshwater biota: (1) Water vapour loss during CO2 uptake from atmosphere. (2) Increased incident UV flux (3) Greater short-term extent of temperatur
Rutgers - WEEK - 7
GEOSCIENCE:Enhanced: Giant Lava Flows, Mass Extinctions, and Mantle Plumes - Ol. Page 1 of 9Current IssuePrevious IssuesScience Express About the JournalScience ProductsMy ScienceHome > Science Magazine > 23 April 1999 > Olsen , pp. 604 -
Rutgers - WEEK - 7
The Deep Roots of Eukaryotes S. L. Baldauf, et al. Science 300, 1703 (2003); DOI: 10.1126/science.1085544The following resources related to this article are available online at www.sciencemag.org (this information is current as of October 16, 2008