A Novel in situ Trigger Combination Method

Searches for rare physics processes using particle detectors in high-luminosity colliding hadronic beam environments require the use of multi-level trigger systems to reject colossal background rates in real time. In analyses like the search for the Higgs boson, there is a need to maximize the signal acceptance by combining multiple different trigger chains when forming the offline data sample. In such statistically limited searches, datasets are often amassed over periods of several years, during which the trigger characteristics evolve and system performance can vary significantly. Reliable production cross-section measurements and upper limits must take into account a detailed understanding of the effective trigger inefficiency for every selected candidate event. We present as an example the complex situation of three trigger chains, based on missing energy and jet energy, that were combined in the context of the search for the Higgs (H) boson produced in association with a $W$ boson at the Collider Detector at Fermilab (CDF). We briefly review the existing techniques for combining triggers, namely the inclusion, division, and exclusion methods. We introduce and describe a novel fourth in situ method whereby, for each candidate event, only the trigger chain with the highest a priori probability of selecting the event is considered. We compare the inclusion and novel in situ methods for signal event yields in the CDF $WH$ search. This new combination method, by virtue of its scalability to large numbers of differing trigger chains and insensitivity to correlations between triggers, will benefit future long-running collider experiments, including those currently operating on the Large Hadron Collider.Comment: 17 pages, 2 figures, 6 tables, accepted by Nuclear Instruments and Methods in Physics Research

Using Node Combination Method in Time-expanded Networks

This study concerns the problem of finding shortest paths in time-expanded networks by repeatedly combining the source nodes nearest neighbor, time-expanded network is derived from dynamic network G= (V,A,T) and contains one copy of the node set of the underlying static network for each discrete time step (building a time layer). we use node combination (NC) method in networks which arc costs can vary with time, each arc has a transit time and parking with a corresponding time-varying cost is allowed at the nodes. The NC algorithm finds the shortest paths with three simple iterative steps: find the nearest neighbor of the source node, combine that node with the source node, and modify the costs on arcs that connect to the nearest neighbor. The NC algorithm is more comprehensible and convenient for programming as there is no need to maintain a set with the nodes distances

Photovoltaic power forecasting with a rough set combination method

One major challenge with integrating photovoltaic (PV) systems into the grid is that its power generation is intermittent and uncontrollable due to the variation in solar radiation. An accurate PV power forecasting is crucial to the safe operation of the grid connected PV power station. In this work, a combined model with three different PV forecasting models is proposed based on a rough set method. The combination weights for each individual model are determined by rough set method according to its significance degree of condition attribute. The three different forecasting models include a past-power persistence model, a support vector machine (SVM) model and a similar data prediction model. The case study results show that, in comparison with each single forecasting model, the proposed combined model can identify the amount of useful information in a more effective manner