Extracting the Dark Matter Mass from Single Stage Cascade Decays at the LHC

We explore a variant on the MT2 kinematic variable which enables dark matter mass measurements for simple, one stage, cascade decays. This will prove useful for constraining a subset of supersymmetric processes, or a class of leptophilic dark matter models at the LHC. We investigate the statistical reach of these measurements and discuss which sources of error have the largest effects. For example, we find that using only single stage cascade decays with initial state radiation, a measurement of a 150 GeV dark matter candidate can be made to O(10%) for a parent mass of 300 GeV with a production cross section of 100 fb and 100 fb^(-1) of integrated luminosity.Comment: 23 Pages, 14 Figures, 2 Appendice

Complementarity of the CERN Large Hadron Collider and the e+e−e^+e^- International Linear Collider

The next-generation high-energy facilities, the CERN Large Hadron Collider (LHC) and the prospective $e^+e^-$ International Linear Collider (ILC), are expected to unravel new structures of matter and forces from the electroweak scale to the TeV scale. In this report we review the complementary role of LHC and ILC in drawing a comprehensive and high-precision picture of the mechanism breaking the electroweak symmetries and generating mass, and the unification of forces in the frame of supersymmetry.Comment: 14 pages, 17 figures, to be published in "Supersymmetry on the Eve of the LHC", a special volume of European Physical Journal C, Particles and Fields (EPJC) in memory of Julius Wes

An adaptive prefix-assignment technique for symmetry reduction

This paper presents a technique for symmetry reduction that adaptively assigns a prefix of variables in a system of constraints so that the generated prefix-assignments are pairwise nonisomorphic under the action of the symmetry group of the system. The technique is based on McKay's canonical extension framework [J.~Algorithms 26 (1998), no.~2, 306--324]. Among key features of the technique are (i) adaptability---the prefix sequence can be user-prescribed and truncated for compatibility with the group of symmetries; (ii) parallelizability---prefix-assignments can be processed in parallel independently of each other; (iii) versatility---the method is applicable whenever the group of symmetries can be concisely represented as the automorphism group of a vertex-colored graph; and (iv) implementability---the method can be implemented relying on a canonical labeling map for vertex-colored graphs as the only nontrivial subroutine. To demonstrate the practical applicability of our technique, we have prepared an experimental open-source implementation of the technique and carry out a set of experiments that demonstrate ability to reduce symmetry on hard instances. Furthermore, we demonstrate that the implementation effectively parallelizes to compute clusters with multiple nodes via a message-passing interface.Comment: Updated manuscript submitted for revie

Stochastic Quantization of Real-Time Thermal Field Theory

We use the stochastic quantization method to obtain the free scalar propagator of a finite temperature field theory formulated in Minkowski spacetime. First we use the Markovian stochastic quantization approach to present the two-point function of the theory. Second, we assume a Langevin equation with a memory kernel and Einstein's relations with colored noise. The convergence of the stochastic processes in the asymptotic limit of the Markov parameter of these Markovian and non-Markovian Langevin equations for a free scalar theory is obtained. Our formalism can be the starting point to discuss systems at finite temperature out of equilibrium

Learning and generation of long-range correlated sequences

We study the capability to learn and to generate long-range, power-law correlated sequences by a fully connected asymmetric network. The focus is set on the ability of neural networks to extract statistical features from a sequence. We demonstrate that the average power-law behavior is learnable, namely, the sequence generated by the trained network obeys the same statistical behavior. The interplay between a correlated weight matrix and the sequence generated by such a network is explored. A weight matrix with a power-law correlation function along the vertical direction, gives rise to a sequence with a similar statistical behavior.Comment: 5 pages, 3 figures, accepted for publication in Physical Review

Understanding how kurtosis is transferred from input acceleration to stress response and it's influence on fatigue life

High cycle fatigue of metals typically occurs through long term exposure to time varying loads which, although modest in amplitude, give rise to microscopic cracks that can ultimately propagate to failure. The fatigue life of a component is primarily dependent on the stress amplitude response at critical failure locations. For most vibration tests, it is common to assume a Gaussian distribution of both the input acceleration and stress response. In real life, however, it is common to experience non-Gaussian acceleration input, and this can cause the response to be non-Gaussian. Examples of non-Gaussian loads include road irregularities such as potholes in the automotive world or turbulent boundary layer pressure fluctuations for the aerospace sector or more generally wind, wave or high amplitude acoustic loads. The paper first reviews some of the methods used to generate non-Gaussian excitation signals with a given power spectral density and kurtosis. The kurtosis of the response is examined once the signal is passed through a linear time invariant system. Finally an algorithm is presented that determines the output kurtosis based upon the input kurtosis, the input power spectral density and the frequency response function of the system. The algorithm is validated using numerical simulations. Direct applications of these results include improved fatigue life estimations and a method to accelerate shaker tests by generating high kurtosis, non-Gaussian drive signals