Time-integrated measurements and prospects for the CKM angle gamma at LHCb

The status and prospects of time-integrated measurements of the CKM angle gamma at LHCb, the LHC's dedicated flavor physics experiment, are reviewed. Yields obtained from early data taking are presented and extrapolations are made to estimate what can be expected to be obtained from the 2011 data. The conclusions drawn from these extrapolations are that LHCb will produce the world's best measurement of gamma by the end of 2011 and that the long-term outlook is excellent.Comment: Proceedings of CKM2010, the 6th International Workshop on the CKM Unitarity Triangle, University of Warwick, UK, 6-10 September 201

A novel approach to the bias-variance problem in bump hunting

This study explores various data-driven methods for performing background-model selection, and for assigning uncertainty on the signal-strength estimator that arises due to the choice of background model. The performance of these methods is evaluated in the context of several realistic example problems. Furthermore, a novel strategy is proposed that greatly simplifies the process of performing a bump hunt when little is assumed to be known about the background. This new approach is shown to greatly reduce the potential bias in the signal-strength estimator, without degrading the sensitivity by increasing the variance, and to produce confidence intervals with valid coverage properties.Comment: 26 pages, 17 figure

Searching for a particle of unknown mass and lifetime in the presence of an unknown non-monotonic background

Many extensions to the Standard Model of particle physics hypothesize the existence of new low-mass particles. Typically there are few theoretical constraints on the mass or lifetime of such particles. This requires the experimentalist to perform a search in which both the mass and lifetime of the particle are unknown. Such searches for low-mass particles are complicated by the possible presence of resonances and other non-monotonic backgrounds. This paper presents a simple and fast approach to assigning significance and setting limits in such searches

uBoost: A boosting method for producing uniform selection efficiencies from multivariate classifiers

The use of multivariate classifiers, especially neural networks and decision trees, has become commonplace in particle physics. Typically, a series of classifiers is trained rather than just one to enhance the performance; this is known as boosting. This paper presents a novel method of boosting that produces a uniform selection efficiency in a user-defined multivariate space. Such a technique is ideally suited for amplitude analyses or other situations where optimizing a single integrated figure of merit is not what is desired

Efficient, reliable and fast high-level triggering using a bonsai boosted decision tree

High-level triggering is a vital component in many modern particle physics experiments. This paper describes a modification to the standard boosted decision tree (BDT) classifier, the so-called "bonsai" BDT, that has the following important properties: it is more efficient than traditional cut-based approaches; it is robust against detector instabilities, and it is very fast. Thus, it is fit-for-purpose for the online running conditions faced by any large-scale data acquisition system.Comment: 10 pages, 2 figure

Angular correlations between LBQS and APM: Weak Lensing by the Large Scale Structure

We detect a positive angular correlation between bright, high-redshift QSOs and foreground galaxies. The QSOs are taken from the optically selected LBQS Catalogue, while the galaxies are from the APM Survey. The correlation amplitude is about a few percent on angular scales of over a degree. It is a function of QSO redshift and apparent magnitude, in a way expected from weak lensing, and inconsistent with QSO-galaxy correlations being caused by physical associations, or uneven obscuration by Galactic dust. The correlations are ascribed to the weak lensing effect of the foreground dark matter, which is traced by the APM galaxies. The amplitude of the effect found here is compared to the analytical predictions from the literature, and to the predictions of a phenomenological model, which is based on the observed counts-in-cells distribution of APM galaxies. While the latter agree reasonably well with the analytical predictions (namely those of Dolag & Bartelmann 1997, and Sanz et al. 1997), both under-predict the observed correlation amplitude on degree angular scales. We consider the possible ways to reconcile these observations with theory, and discuss the implications these observations have on some aspects of extragalactic astronomy.Comment: 9 pages; MNRAS, in pres

Coupling QCD-scale axion-like particles to gluons

We present a novel data-driven method for determining the hadronic interaction strengths of axion-like particles (ALPs) with QCD-scale masses. Using our method, it is possible to calculate the hadronic production and decay rates of ALPs, along with many of the largest ALP decay rate to exclusive final states. To illustrate the impact on QCD-scale ALP phenomenology, we consider the scenario where the ALP-gluon coupling is dominant over the ALP coupling to photons, electroweak bosons, and all fermions for $m_{\pi} \lesssim m_a \lesssim 3$ GeV. We emphasize, however, that our method can easily be generalized to any set of ALP couplings to SM particles. Finally, using the approach developed here, we provide calculations for the branching fractions of $\eta_c \to VV$ decays, i.e. $\eta_c$ decays into two vector mesons, which are consistent with the known experimental values.Comment: 19 pages, 7 figures; v3 Fig 4 updated to account for a small change in the limit taken from [1903.03586

Nonparametric Regression using the Concept of Minimum Energy

It has recently been shown that an unbinned distance-based statistic, the energy, can be used to construct an extremely powerful nonparametric multivariate two sample goodness-of-fit test. An extension to this method that makes it possible to perform nonparametric regression using multiple multivariate data sets is presented in this paper. The technique, which is based on the concept of minimizing the energy of the system, permits determination of parameters of interest without the need for parametric expressions of the parent distributions of the data sets. The application and performance of this new method is discussed in the context of some simple example analyses.Comment: 10 pages, 4 figure