Related papers: Soft Classification of Diffractive Interactions at…
Detecting rare events, those defined to give rise to high impact but have a low probability of occurring, is a challenge in a number of domains including meteorological, environmental, financial and economic. The use of machine learning to…
We propose a model to describe diffractive events in hadron-hadron collisions where a rapidity gap is surrounded by two jets. The hard color-singlet object exchanged in the t-channel and responsible for the rapidity gap is described by the…
Many real-world data mining applications need varying cost for different types of classification errors and thus call for cost-sensitive classification algorithms. Existing algorithms for cost-sensitive classification are successful in…
The new framework for the simulations of hard diffractive events in photoproduction within Pythia 8 is presented. The model, originally introduced for proton-proton collisions, applies the dynamical rapidity gap survival probability based…
Models based on soft colour exchanges to rearrange colour strings in the final state provide a general framework for both diffractive and non-diffractive events in ep and hadron-hadron collisions. We study two such models and find that they…
Binary classification is a common statistical learning problem in which a model is estimated on a set of covariates for some outcome indicating the membership of one of two classes. In the literature, there exists a distinction between hard…
Supervised artificial neural networks with the rapidity-mass matrix (RMM) inputs were studied using several Monte Carlo event samples for various pp collision processes. The study shows the usability of this approach for general event…
The Monte Carlo implementation of different approaches for diffractive scattering in $e - p$ collisions (resolved $\PO$, pQCD, soft color interactions) is described, with emphasis on the construction of the hadronic final state. Simple…
Soft QCD is beyond perturbative control, and therefore phenomenological models have to be developed. These are implemented and combined within event generators. Typical aspects considered are multiparton interactions, colour reconnection,…
Measurements of soft and hard diffractive processes have been performed at the Tevatron p-pbar collider during the past decade. Diffractive events are studied by means of identification of one or more rapidity gaps and/or a leading…
This paper presents an novel object type classification method for automotive applications which uses deep learning with radar reflections. The method provides object class information such as pedestrian, cyclist, car, or non-obstacle. The…
We present a triple-Regge analysis of the available pp --> p + X high-energy data accounting for absorptive corrections. We describe a model for high-energy soft interactions which includes the whole set of multi-Pomeron (n --> m) vertices,…
The covariate shift is a challenging problem in supervised learning that results from the discrepancy between the training and test distributions. An effective approach which recently drew a considerable attention in the research community…
Experimental results on hadronic soft and hard diffractive processes are reviewed with emphasis on aspects of the data that point to the underlying QCD mechanism for diffraction. Diffractive differential cross sections are shown to be…
We note that the definition of diffractive events is a matter of convention. We discuss two possible `definitions': one based on unitarity and the other on Large Rapidity Gaps (LRG) or Pomeron exchange. LRG can also arise from fluctuations…
The process of soft diffractive dissociation in hadronic collisions is discussed in the framework of the Miettinen-Pumplin model. A good description of the data in the ISR-Tevatron energy range is found. Predictions for the total, elastic…
One of the major challenges for the LHC will be to extract precise information from hadronic final states in the presence of the large number of additional soft pp collisions, pileup, that occur simultaneously with any hard interaction in…
Machine-generated probability predictions are essential in modern classification tasks such as image classification. A model is well calibrated when its predicted probabilities correspond to observed event frequencies. Despite the need for…
Active learning aims to reduce labeling costs by selecting only the most informative samples on a dataset. Few existing works have addressed active learning for object detection. Most of these methods are based on multiple models or are…
Results on soft and hard diffraction are briefly reviewed and placed in a QCD perspective using a parton model approach. Issues addressed include factorization, scaling properties, universality of rapidity gap formation, and unitarity.…