TauSpinner program for studies on spin effect in tau production at the LHC

Final states involving tau leptons are important components of searches for new particles at the Large Hadron Collider (LHC). A proper treatment of tau spin effects in the Monte Carlo (MC) simulations is important for understanding the detector acceptance as well as for the measurements of tau polarization and tau spin correlations. In this note we present a TauSpinner package designed to simulate the spin effects. It relies on the availability of the four-momenta of the taus and their decay products in the analyzed data. The flavor and the four-momentum of the boson decaying to the tau-tau+ or tau+- nu pair need to be known. In the Z/gamma* case the initial state quark configuration is attributed from the intermediate boson kinematics, and the parton distribution functions (PDF's). TauSpinner is the first algorithm suitable for emulation of tau spin effects in tau-embedded samples. It is also the first tool that offers the user the flexibility to simulate a desired spin effect at the analysis level. An algorithm to attribute tau helicity states to a previously generated sample is also provided.Comment: 13 pages, 6 figures New feature, an algorithm to attribute tau helicity states introduced in v

Bayesian computational methods

In this chapter, we will first present the most standard computational challenges met in Bayesian Statistics, focussing primarily on mixture estimation and on model choice issues, and then relate these problems with computational solutions. Of course, this chapter is only a terse introduction to the problems and solutions related to Bayesian computations. For more complete references, see Robert and Casella (2004, 2009), or Marin and Robert (2007), among others. We also restrain from providing an introduction to Bayesian Statistics per se and for comprehensive coverage, address the reader to Robert (2007), (again) among others.Comment: This is a revised version of a chapter written for the Handbook of Computational Statistics, edited by J. Gentle, W. Hardle and Y. Mori in 2003, in preparation for the second editio

Test Set Diameter: Quantifying the Diversity of Sets of Test Cases

A common and natural intuition among software testers is that test cases need to differ if a software system is to be tested properly and its quality ensured. Consequently, much research has gone into formulating distance measures for how test cases, their inputs and/or their outputs differ. However, common to these proposals is that they are data type specific and/or calculate the diversity only between pairs of test inputs, traces or outputs. We propose a new metric to measure the diversity of sets of tests: the test set diameter (TSDm). It extends our earlier, pairwise test diversity metrics based on recent advances in information theory regarding the calculation of the normalized compression distance (NCD) for multisets. An advantage is that TSDm can be applied regardless of data type and on any test-related information, not only the test inputs. A downside is the increased computational time compared to competing approaches. Our experiments on four different systems show that the test set diameter can help select test sets with higher structural and fault coverage than random selection even when only applied to test inputs. This can enable early test design and selection, prior to even having a software system to test, and complement other types of test automation and analysis. We argue that this quantification of test set diversity creates a number of opportunities to better understand software quality and provides practical ways to increase it.Comment: In submissio