Related papers: Soft Classification of Diffractive Interactions at…
We are interested in the problem of classifying Multivariate Hawkes Processes (MHP) paths coming from several classes. MHP form a versatile family of point processes that models interactions between connected individuals within a network.…
Measurements of hadron production in pp collisions by the ATLAS and CMS experiments are presented, including charged particle transverse momentum, pseudorapidity and event-by-event multiplicity distributions at sqrt(s) = 0.9, 2.36 and 7…
The increasing data rates in modern high-energy physics experiments such as ALICE at the LHC and the upcoming ePIC experiment at the Electron-Ion Collider (EIC) present significant challenges in real-time event selection and data storage.…
We introduce soft colour interactions as a novel mechanism to understand the observed events with large rapidity gaps in $ep$ collisions at HERA. Colour exchanges between produced partons and colour-charges in the proton remnant modifies…
In the framework of our model (GLM) for soft interaction with $\alpha'_{\pom}(0)=0$, we propose a procedure based on Gribov's partonic interpretation of the Pomeron, which enables one to calculate the diffractive mass distributions in…
We briefly present the soft colour interaction models which are successful in reproducing a multitude of diffractive hard scattering data, and give predictions for diffractive Higgs production at the Tevatron and LHC. Only a few diffractive…
We propose a new soft clustering scheme for classifying galaxies in different activity classes using simultaneously 4 emission-line ratios; log([NII ]/Ha), log([SII]/Ha), log([OI]/Ha) and log([OIII]/Hb). We fit 20 multivariate Gaussian…
An improved understanding of nonperturbative QCD can be obtained by the recently developed soft color interaction models. Their essence is the variation of color string-field topologies, giving a unified description of final states in high…
Simulation is one of the key components in high energy physics. Historically it relies on the Monte Carlo methods which require a tremendous amount of computation resources. These methods may have difficulties with the expected High…
We define a new set of observables to probe the structure of the underlying event in hadron collisions. We use the conventional definition of the `transverse region' in jet events and, for a fixed window in jet $p_\perp$, propose to measure…
Optimal decision making requires that classifiers produce uncertainty estimates consistent with their empirical accuracy. However, deep neural networks are often under- or over-confident in their predictions. Consequently, methods have been…
Hadron production in single and central diffraction dissociation is studied in a model which includes soft hadron interaction as controlled by a supercritical pomeron parametrization and hard diffraction. Within this model, particle…
We present a new framework for the modelling of hard diffraction in pp and ppbar collisions. It starts from the the approach pioneered by Ingelman and Schlein, wherein the single diffractive cross section is factorized into a Pomeron flux…
Few-shot classification consists of learning a predictive model that is able to effectively adapt to a new class, given only a few annotated samples. To solve this challenging problem, meta-learning has become a popular paradigm that…
Trustworthy classifiers are essential to the adoption of machine learning predictions in many real-world settings. The predicted probability of possible outcomes can inform high-stakes decision making, particularly when assessing the…
Hazard and operability analysis (HAZOP) is the paradigm of industrial safety that can reveal the hazards of process from its node deviations, consequences, causes, measures and suggestions, and such hazards can be considered as hazard…
Many classification problems consider classes that form a hierarchy. Classifiers that are aware of this hierarchy may be able to make confident predictions at a coarse level despite being uncertain at the fine-grained level. While it is…
Lightly triggered events may yield surprises about the nature of "soft" particle production at LHC energies. I suggest that event displays in coordinates matched to the dynamics of particle production (rapidity and transverse momentum) may…
The application of deep learning techniques using convolutional neural networks to the classification of particle collisions in High Energy Physics is explored. An intuitive approach to transform physical variables, like momenta of…
Collaboration between different data centers is often challenged by heterogeneity across sites. To account for the heterogeneity, the state-of-the-art method is to re-weight the covariate distributions in each site to match the distribution…