arXiv subjects
Publications and source records attributed to David J. Miller.
We discuss a global fit of top quark BSM couplings, phrased in the model-independent language of higher-dimensional effective operators, to the currently available data from the LHC and Tevatron. We examine the interplay between inclusive and differential measurements, and the complementarity of LHC and Tevatron results. We conclude with a discussion of projections for improvement over LHC Run II.
We analyse the low energy predictions of the minimal supersymmetric standard model (MSSM) arising from a GUT scale Pati-Salam gauge group further constrained by an $A_4 \times Z_5$ family symmetry, resulting in four soft scalar masses at the GUT scale: one left-handed soft mass $m_0$ and three right-handed soft masses $m_1,m_2,m_3$, one for each generation. We demonstrate that this model, which was initially developed to describe the neutrino sector, can explain collider and non-collider measurements such as the dark matter relic density, the Higgs boson mass and, in particular, the anomalous magnetic moment of the muon $(g-2)_μ$. Since about two decades, $(g-2)_μ$ suffers a puzzling about 3$\,σ$ excess of the experimentally measured value over the theoretical prediction, which our model is able to fully resolve. As the consequence of this resolution, our model predicts specific regions of the parameter space with the specific properties including light smuons and neutralinos, which could also potentially explain di-lepton excesses observed by CMS and ATLAS.
We propose an algorithm for detecting patterns exhibited by anomalous clusters in high dimensional discrete data. Unlike most anomaly detection (AD) methods, which detect individual anomalies, our proposed method detects groups (clusters) of anomalies; i.e. sets of points which collectively exhibit abnormal patterns. In many applications this can lead to better understanding of the nature of the atypical behavior and to identifying the sources of the anomalies. Moreover, we consider the case where the atypical patterns exhibit on only a small (salient) subset of the very high dimensional feature space. Individual AD techniques and techniques that detect anomalies using all the features typically fail to detect such anomalies, but our method can detect such instances collectively, discover the shared anomalous patterns exhibited by them, and identify the subsets of salient features. In this paper, we focus on detecting anomalous topics in a batch of text documents, developing our algorithm based on topic models. Results of our experiments show that our method can accurately detect anomalous topics and salient features (words) under each such topic in a synthetic data set and two real-world text corpora and achieves better performance compared to both standard group AD and individual AD techniques. All required code to reproduce our experiments is available from https://github.com/hsoleimani/ATD
Blind Source Separation (BSS) has proven to be a powerful tool for the analysis of composite patterns in engineering and science. We introduce Convex Analysis of Mixtures (CAM) for separating non-negative well-grounded sources, which learns the mixing matrix by identifying the lateral edges of the convex data scatter plot. We prove a sufficient and necessary condition for identifying the mixing matrix through edge detection, which also serves as the foundation for CAM to be applied not only to the exact-determined and over-determined cases, but also to the under-determined case. We show the optimality of the edge detection strategy, even for cases where source well-groundedness is not strictly satisfied. The CAM algorithm integrates plug-in noise filtering using sector-based clustering, an efficient geometric convex analysis scheme, and stability-based model order selection. We demonstrate the principle of CAM on simulated data and numerically mixed natural images. The superior performance of CAM against a panel of benchmark BSS techniques is demonstrated on numerically mixed gene expression data. We then apply CAM to dissect dynamic contrast-enhanced magnetic resonance imaging data taken from breast tumors and time-course microarray gene expression data derived from in-vivo muscle regeneration in mice, both producing biologically plausible decomposition results.
In this paper we present a global fit of beyond the Standard Model (BSM) dimension six operators relevant to the top quark sector to currently available data. Experimental measurements include parton-level top-pair and single top production from the LHC and the Tevatron. Higher order QCD corrections are modelled using differential and global K-factors, and we use novel fast-fitting techniques developed in the context of Monte Carlo event generator tuning to perform the fit. This allows us to provide new, fully correlated and model-independent bounds on new physics effects in the top sector from the most current direct hadron-collider measurements in light of the involved theoretical and experimental systematics. As a by-product, our analysis constitutes a proof-of-principle that fast fitting of theory to data is possible in the top quark sector, and paves the way for a more detailed analysis including top quark decays, detector corrections and precision observables.
In this work we report on a next-to-leading order calculation of WW + jet production at hadron colliders, with subsequent leptonic decays of the W-bosons included. The calculation of the one-loop contributions is performed using generalized unitarity methods in order to derive analytic expressions for the relevant amplitudes. These amplitudes have been implemented in the parton-level Monte Carlo generator MCFM, which we use to provide a complete next-to-leading order calculation. Predictions for total cross-sections, as well as differential distributions for several key observables, are computed both for the LHC operating at 14 TeV as well as for a possible future 100 TeV proton-proton collider.
In a variety of applications, one desires to detect groups of anomalous data samples, with a group potentially manifesting its atypicality (relative to a reference model) on a low-dimensional subset of the full measured set of features. Samples may only be weakly atypical individually, whereas they may be strongly atypical when considered jointly. What makes this group anomaly detection problem quite challenging is that it is a priori unknown which subset of features jointly manifests a particular group of anomalies. Moreover, it is unknown how many anomalous groups are present in a given data batch. In this work, we develop a group anomaly detection (GAD) scheme to identify the subset of samples and subset of features that jointly specify an anomalous cluster. We apply our approach to network intrusion detection to detect BotNet and peer-to-peer flow clusters. Unlike previous studies, our approach captures and exploits statistical dependencies that may exist between the measured features. Experiments on real world network traffic data demonstrate the advantage of our proposed system, and highlight the importance of exploiting feature dependency structure, compared to the feature (or test) independence assumption made in previous studies.
We analyse the hVV (V = W, Z) vertex in a model independent way using Vh production. To that end, we consider possible corrections to the Standard Model Higgs Lagrangian, in the form of higher dimensional operators which parametrise the effects of new physics. In our analysis, we pay special attention to linear observables that can be used to probe CP violation in the same. By considering the associated production of a Higgs boson with a vector boson (W or Z), we use jet substructure methods to define angular observables which are sensitive to new physics effects, including an asymmetry which is linearly sensitive to the presence of CP odd effects. We demonstrate how to use these observables to place bounds on the presence of higher dimensional operators, and quantify these statements using a log likelihood analysis. Our approach allows one to probe separately the hZZ and hWW vertices, involving arbitrary combinations of BSM operators, at the Large Hadron Collider.
Detecting complex interactions among risk factors in case-control studies is a fundamental task in clinical and population research. However, though hypothesis testing using logistic regression (LR) is a convenient solution, the LR framework is poorly powered and ill-suited under several common circumstances in practice including missing or unmeasured risk factors, imperfectly correlated "surrogates", and multiple disease sub-types. The weakness of LR in these settings is related to the way in which the null hypothesis is defined. Here we propose the Asymmetric Independence Model (AIM) as a biologically-inspired alternative to LR, based on the key observation that the mechanisms associated with acquiring a "disease" versus maintaining "health" are asymmetric. We prove mathematically that, unlike LR, AIM is a robust model under the abovementioned confounding scenarios. Further, we provide a mathematical definition of a "synergistic" interaction, and prove that theoretically AIM has better power than LR for such interactions. We then experimentally show the superior performance of AIM as compared to LR on both simulations and four real datasets. While the principal application here involves genetic or environmental variables in the life sciences, our methodology is readily applied to other types of measurements and inferences, e.g. in the social sciences.
We propose a parsimonious topic model for text corpora. In related models such as Latent Dirichlet Allocation (LDA), all words are modeled topic-specifically, even though many words occur with similar frequencies across different topics. Our modeling determines salient words for each topic, which have topic-specific probabilities, with the rest explained by a universal shared model. Further, in LDA all topics are in principle present in every document. By contrast our model gives sparse topic representation, determining the (small) subset of relevant topics for each document. We derive a Bayesian Information Criterion (BIC), balancing model complexity and goodness of fit. Here, interestingly, we identify an effective sample size and corresponding penalty specific to each parameter type in our model. We minimize BIC to jointly determine our entire model -- the topic-specific words, document-specific topics, all model parameter values, {\it and} the total number of topics -- in a wholly unsupervised fashion. Results on three text corpora and an image dataset show that our model achieves higher test set likelihood and better agreement with ground-truth class labels, compared to LDA and to a model designed to incorporate sparsity.
For a simple model of price-responsive demand, we consider a deregulated electricity marketplace wherein the grid (ISO, retailer-distributor) accepts bids per-unit supply from generators (simplified herein neither to consider start-up/ramp-up expenses nor day-ahead or shorter-term load following) which are then averaged (by supply allocations via an economic dispatch) to a common "clearing" price borne by customers (irrespective of variations in transmission/distribution or generation prices), i.e., the ISO does not compensate generators based on their marginal costs. Rather, the ISO provides sufficient information for generators to sensibly adjust their bids. Notwithstanding our idealizations, the dispatch dynamics are complex. For a simple benchmark power system, we find a price-symmetric Nash equilibrium through numerical experiments.
We consider ZH and WH production at the Large Hadron Collider, where the Higgs decays to a bb pair. We use jet substructure techniques to reconstruct the Higgs boson and construct angular observables involving leptonic decay products of the vector bosons. These efficiently discriminate between the tensor structure of the HVV vertex expected in the Standard Model and that arising from possible new physics, as quantified by higher dimensional operators. This can then be used to examine the CP nature of the Higgs as well as CP mixing effects in the HZZ and HWW vertices separately.
High fidelity simulation of large-sized complex networks can be realized on a distributed computing platform that leverages the combined resources of multiple processors or machines. In a discrete event driven simulation, the assignment of logical processes (LPs) to machines is a critical step that affects the computational and communication burden on the machines, which in turn affects the simulation execution time of the experiment. We study a network partitioning game wherein each node (LP) acts as a selfish player. We derive two local node-level cost frameworks which are feasible in the sense that the aggregate state information required to be exchanged between the machines is independent of the size of the simulated network model. For both cost frameworks, we prove the existence of stable Nash equilibria in pure strategies. Using iterative partition improvements, we propose game theoretic partitioning algorithms based on the two cost criteria and show that each descends in a global cost. To exploit the distributed nature of the system, the algorithm is distributed, with each node's decision based on its local information and on a few global quantities which can be communicated machine-to-machine. We demonstrate the performance of our partitioning algorithm on an optimistic discrete event driven simulation platform that models an actual parallel simulator.
Much is known in the analysis of a finitely ramified self-similar fractal when the fractal has a harmonic structure: a Dirichlet form which respects the self-similarity of a fractal. What is still an open question is when such structure exists in general. In this paper, we introduce two fractals, the fractalina and pillow, and compute their resistance scaling factor. This is the factor which dictates how the Dirichlet form scales with the self-similarity of the fractal. By knowing this factor one can compute the harmonic structure on the fractal. The fractalina has scaling factor $(3+\sqrt{41})/16$, and the pillow fractal has scaling factor $\sqrt[3]{2}$.
Alzheimer's disease (AD) and mild cognitive impairment (MCI), continue to be widely studied. While there is no consensus on whether MCIs actually "convert" to AD, the more important question is not whether MCIs convert, but what is the best such definition. We focus on automatic prognostication, nominally using only a baseline image brain scan, of whether an MCI individual will convert to AD within a multi-year period following the initial clinical visit. This is in fact not a traditional supervised learning problem since, in ADNI, there are no definitive labeled examples of MCI conversion. Prior works have defined MCI subclasses based on whether or not clinical/cognitive scores such as CDR significantly change from baseline. There are concerns with these definitions, however, since e.g. most MCIs (and ADs) do not change from a baseline CDR=0.5, even while physiological changes may be occurring. These works ignore rich phenotypical information in an MCI patient's brain scan and labeled AD and Control examples, in defining conversion. We propose an innovative conversion definition, wherein an MCI patient is declared to be a converter if any of the patient's brain scans (at follow-up visits) are classified "AD" by an (accurately-designed) Control-AD classifier. This novel definition bootstraps the design of a second classifier, specifically trained to predict whether or not MCIs will convert. This second classifier thus predicts whether an AD-Control classifier will predict that a patient has AD. Our results demonstrate this new definition leads not only to much higher prognostic accuracy than by-CDR conversion, but also to subpopulations much more consistent with known AD brain region biomarkers. We also identify key prognostic region biomarkers, essential for accurately discriminating the converter and nonconverter groups.
We examine the CP-conserving (CPC) and CP-violating (CPV) effects of a general HZZ coupling through a study of the process H -> ZZ* -> 4 leptons at the LHC. We construct asymmetries that directly probe these couplings. Further, we present complete analytical formulae for the angular distributions of the decay leptons and for some of the asymmetries. Using these we have been able to identify new observables which can provide enhanced sensitivity to the CPV $H ZZ$ coupling. We also explore probing CP violation through shapes of distributions in different kinematic variables, which can be used for Higgs bosons with mH < 2 mZ.
We examine the effect of a general HZZ coupling through a study of the Higgs decay to leptons via Z bosons at the LHC. We discuss various methods for placing limits on additional couplings, including measurement of the partial width, threshold scans, and asymmetries constructed from angular observables. We find that only the asymmetries provide a definitive test of additional couplings. We further estimate the significances they provide.
We analyze a portion of the SDSS photometric catalog, consisting of approximately 10,000 objects that have been spectroscopically classified into stars, galaxies, QSOs, late-type stars and unknown objects (spectroscopically unclassified objects, SUOs), in order to investigate the existence and nature of subclasses of the unclassified objects. We use a modified mixture modeling approach that makes use of both labeled and unlabeled data and performs class discovery on the data set. The modeling was done using four colors derived from the SDSS photometry: (u-g), (g-r), (r-i), and (i-z). This technique discovers putative novel classes by identifying compact clusters that largely contain objects from the spectroscopically unclassified class of objects. These clusters are of possible scientific interest because they represent structured groups of outliers, relative to the known object classes. We identify two such well defined subclasses of the SUOs. One subclass contains 58% SUOs, 40% stars, and 2% galaxies, QSOs, and late-type stars. The other contains 91% SUOs, 6% late-type stars, and 3% stars, galaxies, and QSOs. We discuss possible interpretations of these subclasses while also noting some caution must be applied to purely color-based object classifications. As a side benefit of this limited study we also find two distinct classes, consisting largely of galaxies, that coincide with the recently discussed bimodal galaxy color distribution.