Renormalization-Group Improved Prediction for Higgs Production at Hadron Colliders

We use renormalization-group methods in effective field theory to improve the theoretical prediction for the cross section for Higgs-boson production at hadron colliders. In addition to soft-gluon resummation at NNNLL, we also resum enhanced contributions of the form (C_A\pi\alpha_s)^n, which arise in the analytic continuation of the gluon form factor to time-like momentum transfer. This resummation is achieved by evaluating the matching corrections arising at the Higgs-boson mass scale at a time-like renormalization point \mu^2<0, followed by renormalization-group evolution to \mu^2>0. We match our resummed result to NNLO fixed-order perturbation theory and give numerical predictions for the total production cross section as a function of the Higgs-boson mass. Resummation effects are significant even at NNLO, where our improved predictions for the cross sections at the Tevatron and the LHC exceed the fixed-order predictions by about 13% and 8%, respectively, for m_H=120 GeV. We also discuss the application of our technique to other time-like processes such as Drell-Yan production, e^+ e^- --> hadrons, and hadronic decays of the Higgs boson.Comment: 35 pages, 6 figures; v2: update to MSTW2008 PDFs, detailed comparison with moment-space formalism; v3: typo in equation (A.3) correcte

Predicting dataset popularity for the CMS experiment

The CMS experiment at the LHC accelerator at CERN relies on its computing infrastructure to stay at the frontier of High Energy Physics, searching for new phenomena and making discoveries. Even though computing plays a significant role in physics analysis we rarely use its data to predict the system behavior itself. A basic information about computing resources, user activities and site utilization can be really useful for improving the throughput of the system and its management. In this paper, we discuss a first CMS analysis of dataset popularity based on CMS meta-data which can be used as a model for dynamic data placement and provide the foundation of data-driven approach for the CMS computing infrastructure.Comment: Submitted to proceedings of 17th International workshop on Advanced Computing and Analysis Techniques in physics research (ACAT

Correlations of values of random diagonal forms

We study the value distribution of diagonal forms in k variables and degree d with random real coefficients and positive integer variables, normalized so that mean spacing is one. We show that the l-correlation of almost all such forms is Poissonian when k is large enough depending on l and d.Comment: 28 page

A numerical study of JKR-type adhesive contact of ellipsoids

Approximate solution by Johnson and Greenwood (2005) for an adhesive contact of an ellipsoid and an elastic half-space is revisited numerically using the FFT-based Boundary Element Method. While for moderate values of the ratio of principal radii of the ellipsoid, R1/R2, predictions of the Johnson-Greenwood approximate theory are very good, they become increasingly inaccurate for large values of this parameter. On the basis of numerical simulations, we provide analytical approximations for the dependencies between load, approach and contact area and compare the exact shape of the contact area with the elliptical one assumed in the Johnson-Greenwood-theory

Informative and misinformative interactions in a school of fish

It is generally accepted that, when moving in groups, animals process information to coordinate their motion. Recent studies have begun to apply rigorous methods based on Information Theory to quantify such distributed computation. Following this perspective, we use transfer entropy to quantify dynamic information flows locally in space and time across a school of fish during directional changes around a circular tank, i.e. U-turns. This analysis reveals peaks in information flows during collective U-turns and identifies two different flows: an informative flow (positive transfer entropy) based on fish that have already turned about fish that are turning, and a misinformative flow (negative transfer entropy) based on fish that have not turned yet about fish that are turning. We also reveal that the information flows are related to relative position and alignment between fish, and identify spatial patterns of information and misinformation cascades. This study offers several methodological contributions and we expect further application of these methodologies to reveal intricacies of self-organisation in other animal groups and active matter in general