arXiv subjects

Mike Williams

Publications and source records attributed to Mike Williams.

53 records · Page 3Linked to original sources

arXiv2017

Disentangling Heavy Flavor at Colliders

We propose two new analysis strategies for studying charm and beauty quarks at colliders. The first strategy is aimed at testing the kinematics of heavy-flavor quarks within an identified jet. Here, we use the SoftDrop jet-declustering algorithm to identify two subjets within a large-radius jet, using subjet flavor tagging to test the heavy-quark splitting functions of QCD. For subjets containing a $J / ψ$ or $Υ$, this declustering technique can also help probe the mechanism for quarkonium production. The second strategy is aimed at isolating heavy-flavor production from gluon splitting. Here, we introduce a new FlavorCone algorithm, which smoothly interpolates from well-separated heavy-quark jets to the gluon-splitting regime where jets overlap. Because of its excellent ability to identify charm and beauty hadrons, the LHCb detector is ideally suited to pursue these strategies, though similar measurements should also be possible at ATLAS and CMS. Together, these SoftDrop and FlavorCone studies should clarify a number of aspects of heavy-flavor physics at colliders, and provide crucial information needed to improve heavy-flavor modeling in parton-shower generators.

hep-ph↗

arXiv2016

Machine learning and multivariate goodness of fit

Multivariate goodness-of-fit and two-sample tests are important components of many nuclear and particle physics analyses. While a variety of powerful methods are available if the dimensionality of the feature space is small, such tests rapidly lose power as the dimensionality increases and the data inevitably become sparse. Machine learning classifiers are powerful tools capable of reducing highly multivariate problems into univariate ones, on which commonly used tests such as $χ^2$ or Kolmogorov-Smirnov may be applied. We explore applying both traditional and machine-learning-based tests to several example problems, and study how the power of each approach depends on the dimensionality. A pedagogical discussion is provided on which types of problems are best suited to using traditional versus machine-learning-based tests, and on the how to properly employ the machine-learning-based approach.

physics.data-an↗

arXiv2016

FastDIRC: a fast Monte Carlo and reconstruction algorithm for DIRC detectors

FastDIRC is a novel fast Monte Carlo and reconstruction algorithm for DIRC detectors. A DIRC employs rectangular fused-silica bars both as Cherenkov radiators and as light guides. Cherenkov-photon imaging and time-of-propagation information are utilized by a DIRC to identify charged particles. GEANT-based DIRC Monte Carlo simulations are extremely CPU intensive. The FastDIRC algorithm permits fully simulating a DIRC detector more than 10000 times faster than using GEANT. This facilitates designing a DIRC-reconstruction algorithm that improves the Cherenkov-angle resolution of a DIRC detector by about 30% compared to existing algorithms. FastDIRC also greatly reduces the time required to study competing DIRC-detector designs.

physics.data-an↗

arXiv2016

Inclusive Dark Photon Search at LHCb

We propose an inclusive search for dark photons $A'$ at the LHCb experiment based on both prompt and displaced di-muon resonances. Because the couplings of the dark photon are inherited from the photon via kinetic mixing, the dark photon $A' \to μ^+ μ^-$ rate can be directly inferred from the off-shell photon $γ^* \to μ^+ μ^-$ rate, making this a fully data-driven search. For Run 3 of the LHC, we estimate that LHCb will have sensitivity to large regions of the unexplored dark-photon parameter space, especially in the 210-520 MeV and 10-40 GeV mass ranges. This search leverages the excellent invariant-mass and vertex resolution of LHCb, along with its unique particle-identification and real-time data-analysis capabilities.

hep-ph↗

arXiv2016

Photoproduction of leptophobic bosons

We propose a search for photoproduction of leptophobic bosons that couple to quarks at the GlueX experiment at Jefferson Lab. We study in detail a new gauge boson that couples to baryon number $B$, and estimate that $γp \to p B$ will provide the best sensitivity for $B$ masses above 0.5 GeV. This search will also provide sensitivity to other proposed dark-sector states that couple to quarks. Finally, our results motivate a similar search for $B$ boson electroproduction at the CLAS experiment.

hep-ph↗

arXiv2016

A direct probe of the intrinsic charm content of the proton

Measurement of $Z$ bosons produced in association with charm jets $(Zc)$ in proton-proton collisions in the forward region provides a direct probe of a potential non-perturbative (intrinsic) charm component in the proton wave function. We provide a detailed study of the potential to measure $Zc$ production at the LHCb experiment in Runs 2 and 3 of the LHC. The sensitivity to valence-like (sea-like) intrinsic charm is predicted to be $\langle x \rangle_{\rm IC} \gtrsim 0.3\%(1\%)$. The impact of intrinsic charm on Higgs production at the LHC, including $Hc$, is also discussed in detail.

hep-ph↗

arXiv2015

Dark photons from charm mesons at LHCb

We propose a search for dark photons $A^{\prime}$ at the LHCb experiment using the charm meson decay $D^*(2007)^0 \!\to D^0 A^{\prime}$. At nominal luminosity, $D^{*0} \!\to D^0 γ$ decays will be produced at about 700kHz within the LHCb acceptance, yielding over 5 trillion such decays during Run 3 of the LHC. Replacing the photon with a kinetically-mixed dark photon, LHCb is then sensitive to dark photons that decay as $A^{\prime}\!\to e^+e^-$. We pursue two search strategies in this paper. The displaced strategy takes advantage of the large Lorentz boost of the dark photon and the excellent vertex resolution of LHCb, yielding a nearly background-free search when the $A^{\prime}$ decay vertex is significantly displaced from the proton-proton primary vertex. The resonant strategy takes advantage of the large event rate for $D^{*0} \!\to D^0 A^{\prime}$ and the excellent invariant mass resolution of LHCb, yielding a background-limited search that nevertheless covers a significant portion of the $A^{\prime}$ parameter space. Both search strategies rely on the planned upgrade to a triggerless-readout system at LHCb in Run 3, which will permit identification of low-momentum electron-positron pairs online during data taking. For dark photon masses below about 100MeV, LHCb can explore nearly all of the dark photon parameter space between existing prompt-$A^{\prime}$ and beam-dump limits.

hep-ph↗

arXiv2015

Model selection for amplitude analysis

Model complexity in amplitude analyses is often a priori under-constrained since the underlying theory permits a large number of possible amplitudes to contribute to most physical processes. The use of an overly complex model results in reduced predictive power and worse resolution on unknown parameters of interest. Therefore, it is common to reduce the complexity by removing from consideration some subset of the allowed amplitudes. This paper studies a method for limiting model complexity from the data sample itself through regularization during regression in the context of a multivariate (Dalitz-plot) analysis. The regularization technique applied greatly improves the performance. An outline of how to obtain the significance of a resonance in a multivariate amplitude analysis is also provided.

physics.data-an↗

arXiv2015

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.

hep-ex↗

arXiv2014

New approaches for boosting to uniformity

The use of multivariate classifiers has become commonplace in particle physics. To enhance the performance, a series of classifiers is typically trained; this is a technique known as boosting. This paper explores several novel boosting methods that have been designed to produce a uniform selection efficiency in a chosen multivariate space. Such algorithms have a wide range of applications in particle physics, from producing uniform signal selection efficiency across a Dalitz-plot to avoiding the creation of false signal peaks in an invariant mass distribution when searching for new particles.

hep-ex↗

arXiv2013

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.

nucl-ex↗

arXiv2012

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.

physics.ins-det↗

arXiv2011

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.

physics.data-an↗

arXiv2011

Observing CP Violation in Many-Body Decays

It is well known that observing CP violation in many-body decays could provide strong evidence for physics beyond the Standard Model. Many searches have been carried out; however, no 5sigma evidence for CP violation has yet been found in these types of decays. A novel model-independent method for observing CP violation in many-body decays is presented in this paper. It is shown that the sensitivity of this method is significantly larger than those used to-date.

hep-ex↗

arXiv2011

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.

hep-ex↗

arXiv2010

How good are your fits? Unbinned multivariate goodness-of-fit tests in high energy physics

Multivariate analyses play an important role in high energy physics. Such analyses often involve performing an unbinned maximum likelihood fit of a probability density function (p.d.f.) to the data. This paper explores a variety of unbinned methods for determining the goodness of fit of the p.d.f. to the data. The application and performance of each method is discussed in the context of a real-life high energy physics analysis (a Dalitz-plot analysis). Several of the methods presented in this paper can also be used for the non-parametric determination of whether two samples originate from the same parent p.d.f. This can be used, e.g., to determine the quality of a detector Monte Carlo simulation without the need for a parametric expression of the efficiency.

hep-ex↗

arXiv2009

Prospects for the Measurement of the Unitarity Triangle Angle gamma from B0 to DK+pi- Decays

The potential for a precise measurement of the Unitarity Triangle angle $γ$ in future experiments from the decay $B^0 \to DK^{*0}$ is well-known. It has recently been suggested that the sensitivity can be significantly enhanced by analysing the $B^0 \to DK^+π^-$ Dalitz plot to extract amplitudes relative to those of the flavour-specific decay $B^0 \to D_2^{*-}K^+$. An extension to this method which includes the case where the neutral $D$ meson is reconstructed in suppressed final states is presented. The sensitivity to $γ$ is estimated using this method and compared to that obtained using the $B^0 \to DK^{*0}$ decay alone. Experimental effects, such as background contamination, are also considered. This approach appears to be a highly attractive addition to the family of methods that can be used to determine $γ$.

hep-ph↗