Signal Selection Strategy Research Articles

New physics in multi-electron muon decays

We study the exotic muon decays with five charged tracks in the final state. First, we investigate the Standard Model rate for μ+ → 3e+ 2e− 2ν (B\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\mathcal{B} $$\\end{document} = 4.0 × 10−10) and find that the Mu3e experiment should have tens to hundreds of signal events per 1015μ+ decays, depending on the signal selection strategy. We then turn to a neutrinoless μ+ → 3e+ 2e− decay that may arise in new-physics models with lepton-flavor-violating effective operators involving a dark Higgs hd. Following its production in μ+ → e+hd decays, the dark Higgs can undergo a decay cascade to two e+e− pairs through two dark photons, hd→ γdγd→ 2(e+e−). We show that a μ+→ 3e+ 2e− search at the Mu3e experiment, with potential sensitivity to the branching ratio at the O\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\mathcal{O} $$\\end{document}(10−12) level or below, can explore new regions of parameter space and new physics scales as high as Λ ∼ 1015 GeV.

Dynamic signal selection strategies

This paper concerns the selection of a small number of predictors from a much larger set of potential stock signals. Selection is based on choosing predictors with high levels of dependency in stock returns. Eight different models of pairwise dependency are employed, including the Gaussian, t , Clayton, Frank and Gumbel copulas. In addition, predictive factors and returns are mapped to gamma, beta and standard bilateral gamma marginals with dependency constructed by using the magnitude of fractional common components from the same distributions. Each dependency model is used to select predictors. The predictions are used both directly and as required returns to build a measure for the value of an invested dollar. Stocks are ranked by these two metrics daily. For the mean-reversion strategy, we take short positions in a quarter of the top-ranked stocks and long positions in a quarter of the bottom-ranked stocks. The positioning is reversed under momentum. Trading performance results are presented for a variety of dependencies and sectors over the period from June 2006 to January 2021. The selection procedures are observed to deliver a reasonable set of trading strategies.

Dominant inter-area oscillation mode identification using local measurement and modal energy for large-scale power systems with high grid-tied VSCs penetration

Appropriate phasor measurement unit (PMU) signals must be selected to monitor electromechanical oscillations for large-scale renewable energy connected power system. Considering the response mechanism of the active and reactive power of grid-tied voltage source converters (VSCs) in the electromechanical time scale, a local measurement signal selection strategy for monitoring inter-area oscillations (0.1–0.7 Hz) based on the inherent electromechanical characteristics of the synchronous generators (SGs) is proposed. Moreover, optimized dynamic mode decomposition (OpDMD) algorithm is used to extract dominant modal parameters (e.g., oscillation frequency, damping ratio, and mode shape). First, from the perspective of frequency response characteristics, the dynamic interaction mechanism between grid-tied VSCs and the AC grid is revealed in the electromechanical time scale. Second, considering the intrinsic relationship between the inherent inertia constant of the SG and observability of response after a disturbance, a local measurement signal selection strategy for monitoring the inter-area electromechanical oscillation of power systems with high grid-tied VSCs penetration is proposed. On the basis of the selected local measurement signals, the OpDMD algorithm identifies dominant inter-area oscillation parameters. Results of the IEEE 16-generator 5-area system show that the proposed method can accurately extract electromechanical modal parameters from the limited PMU measurements, verifying the effectiveness of the local measurement signal selection strategy for inter-area oscillation monitoring of power systems with high grid-tied VSCs penetration.

Closing the light gluino gap with electron-proton colliders

The future electron-proton collider proposals, LHeC and FCC-he, can deliver $\mathcal{O}$(TeV) center-of-mass energy collisions, higher than most of the proposed lepton accelerators, with $\mathcal{O}$(ab$^{-1}$) luminosity, while maintaining a much cleaner experimental environment as compared to the hadron machines. This unique capability of $e^- p$ colliders can be harnessed in probing BSM scenarios giving final states that look like hadronic noise at $pp$ machines. In the present study, we explore the prospects of detecting such a prompt signal having multiple soft jets at the LHeC. Such a signal can come from the decay of gluino in RPV or Stealth SUSY, where there exists a gap in the current experimental search with $m_{\tilde{g}} \approx 50 - 70$ GeV. We perform a simple analysis to demonstrate that, with simple signal selection cuts, we can close this gap at the LHeC at 95 % confidence level, even in the presence of a reasonable systematic error. More sophisticated signal selection strategies and detailed knowledge of the detector can be used to improve the prospects of signal detection.

Measuring the spatial extent of texture pooling using reverse correlation

The local image representation produced by early stages of visual analysis is uninformative regarding spatially extensive textures and surfaces. We know little about the cortical algorithm used to combine local information over space, and still less about the area over which it can operate. But such operations are vital to support perception of real-world objects and scenes. Here, we deploy a novel reverse-correlation technique to measure the extent of spatial pooling for target regions of different areas placed either in the central visual field, or more peripherally. Stimuli were large arrays of micropatterns, with their contrasts perturbed individually on an interval-by-interval basis. By comparing trial-by-trial observer responses with the predictions of computational models, we show that substantial regions (up to 13 carrier cycles) of a stimulus can be monitored in parallel by summing contrast over area. This summing strategy is very different from the more widely assumed signal selection strategy (a MAX operation), and suggests that neural mechanisms representing extensive visual textures can be recruited by attention. We also demonstrate that template resolution is much less precise in the parafovea than in the fovea, consistent with recent accounts of crowding.

On Signal Selection for Visibility Enhancement in Trace-Based Post-Silicon Validation

Today's complex integrated circuit designs increasingly rely on post-silicon validation to eliminate bugs that escape from pre-silicon verification. One effective silicon debug technique is to monitor and trace the behaviors of the circuit during its normal operation. However, due to the associated overhead, designers can only afford to trace a small number of signals in the design. Selecting which signals to trace is therefore a crucial issue for the effectiveness of this technique. This paper proposes an automated trace signal selection strategy with a new probability-based evaluation metric, which is able to dramatically enhance the visibility in post-silicon validation. Experimental results on benchmark circuits show that the proposed technique is more effective than existing solutions.

Ranking of genome-wide association scan signals by different measures

The P-value approach has been employed to prioritizing genome-wide association (GWA) scan signals, with a genome-wide significance defined by a prior P-value threshold, although this is not ideal. A rationale put forward is that the association signals rather should be expected to give less support for single nucleotide polymorphisms (SNPs) that are rare (with associated low-power tests) than for common SNPs with equivalent P-values, unless investigators believe, a priori, that rare causative variants contribute to the disease and have more pronounced effects. Using data from a GWA scan for type 2 diabetes (1924 cases, 2938 controls, 393 453 SNPs), we compared P-values with four alternative signal measures: likelihood ratio (LR), Bayes factor (BF; with a specified prior distribution for true effects), 'frequentist factor' (FF; reflecting the ratio between estimated--post-data-- 'power' and P-value) and probability of pronounced effect size (PrPES). The 19 common SNPs [minor allele frequency (MAF) among the controls >29%] yielding strong P-value signals (P < 5 x 10(-7)) were also top ranked by the other approaches. There was a strong similarity between the P-values, LR and BF signals, in terms of ranking SNPs. In contrast, FF and PrPES signals down-weighted rare SNPs (control MAF <10%) with low P-values. For prioritization of signals that do not achieve compelling levels of evidence for association, the main driving force behind observed differences between the various association signals appears to be SNP MAF. The statistical power afforded by follow-up samples for establishing replication should be taken into account when tailoring the signal selection strategy.

A method for the precision mass measurement of the stop quark at the International Linear Collider

Many supersymmetric models predict new particles within the reach of the next generation of colliders. For an understanding of the model structure and the mechanism(s) of symmetry breaking, it is important to know the masses of the new particles precisely. In this article the measurement of the mass of the scalar partner of the top quark (stop) at an e+e- collider is studied. A relatively light stop is motivated by attempts to explain electroweak baryogenesis and can play an important role in dark matter relic density. A method is presented which makes use of cross-section measurements near the pair-production threshold as well as at higher center-of-mass energies. It is shown that this method not only increases the statistical precision, but also greatly reduces the systematic uncertainties, which can be important. Numerical results are presented, based on a realistic event simulation, for two signal selection strategies: using conventional selection cuts, and using an Iterative Discriminant Analysis (IDA). Our studies indicate that a precision of {Delta}m{tilde t}{sub 1} = 0.42 GeV can be achieved, representing a major improvement over previous studies. While the analysis of stops is particularly challenging due to the possibility of stop hadronization, the general procedure could be appliedmore » to the mass measurement of other particles as well. We also comment on the potential of the IDA to discover a stop quark in this scenario, and we revisit the accuracy of the theoretical predictions for the neutralino relic density« less

LHC Preparations for Measurements of Very Rare B Decays

As is well known, the rare B-decays and B0→μ+μ− offer a profound probe into the effects of physics beyond the Standard Model (SM). While present experimental upper limits from the Tevatron are roughly a factor of twenty above the SM predictions, the LHC experiments will reach SM values after the first year of nominal LHC luminosity and will allow a measurement of the decay probability. The three LHC experiments, ATLAS, CMS and LHCb are preparing triggers, offline reconstruction algorithms and signal selection strategies for these measurements. In this paper, special attention is devoted to background composition, especially to topologically-similar rare exclusive and exotic decays.