Geometry description markup language for physics simulation and analysis applications

The Geometry Description Markup Language (GDML) is a specialized XML-based language designed as an application-independent persistent format for describing the geometries of detectors associated with physics measurements. It serves to implement ''geometry trees'' which correspond to the hierarchy of volumes a detector geometry can be composed of, and to allow to identify the position of individual solids, as well as to describe the materials they are made of. Being pure XML, GDML can be universally used, and in particular it can be considered as the format for interchanging geometries among different applications. In this paper we will present the current status of the development of GDML. After having discussed the contents of the latest GDML schema, which is the basic definition of the format, we will concentrate on the GDML processors. We will present the latest implementation of the GDML ''writers'' as well as ''readers'' for either Geant4 [2], [3] or ROOT [4], [10]

Five-Dimensional Gauged Supergravity and Supersymmetry Breaking in MM~Theory

We extend the formulation of gauged supergravity in five dimensions, as obtained by compactification of $M$~theory on a deformed Calabi-Yau manifold, to include non-universal matter hypermultiplets. Even in the presence of this gauging, only the graviton supermultiplets and matter hypermultiplets can couple to supersymmetry breaking sources on the walls, though these mix with vector supermultiplets in the bulk. Whatever the source of supersymmetry breaking on the hidden wall, that on the observable wall is in general a combination of dilaton- and moduli-dominated scenarios.Comment: 20 pages, LaTex, corrected typos and notation, added reference

Flat directions, String Compactification and 3 Generation Models

We show how identification of absolutely flat directions allows the construction of a new class of compactified string theories with reduced gauge symmetry that may or may not be continuously connected to the original theory. We use this technique to construct a class of 3 generation models with just the Standard Model gauge group after compactification. We discuss the low-energy symmetries necessary for a phenomenologically viable low-energy model and construct an example in which these symmetries are identified with string symmetries which remain unbroken down to the supersymmetry breaking scale. Remarkably the same symmetry responsible for stabilising the nucleon is also responsible for ensuring one and only one pair of Higgs doublets is kept light. We show how the string symmetries also lead to textures in the quark and lepton mass matrices which can explain the hierarchy of fermion masses and mixing angles.Comment: 32 page

Five-Dimensional Aspects of M-Theory Dynamics and Supersymmetry Breaking

We discuss the reduction of the eleven-dimensional M-theory effective Lagrangian, considering first compactification from eleven to five dimensions on a Calabi-Yau manifold, followed by reduction to four dimensions on an S_1/Z_2 line segment at a larger distance scale. The Calabi-Yau geometry leads to a structure of the five-dimensional Lagrangian that has more freedom than the eleven-dimensional theory. In five dimensions one obtains a non-linear sigma-model coupled to gravity, which implies non-trivial dynamics for the scalar moduli fields in the bulk of the Z_2 orbifold. We discuss solutions to the five-dimensional equations of motion in the presence of sources localized on the boundaries of the Z_2 orbifold that may trigger supersymmetry breaking, e.g., gaugino condensates. The transmission of supersymmetry breaking from the hidden wall to the visible wall is demonstrated in specific models. The role of the messenger of supersymmetry breaking may be played by the gravity supermultiplet and/or by scalar hypermultiplets. The latter include the universal hypermultiplet associated with the Calabi-Yau volume, and also the hypermultiplets associated with deformations of its complex structure, which mix in general.Comment: 35 pages, Latex, 9 figures, discussion of the extension of the results to the gauged supergravity model has been added at the end of section

A Roadmap for HEP Software and Computing R&D for the 2020s

Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facilities and experiments, or to upgrade existing ones. Similarly, it requires commensurate investment in the R&D of software to acquire, manage, process, and analyse the shear amounts of data to be recorded. In planning for the HL-LHC in particular, it is critical that all of the collaborating stakeholders agree on the software goals and priorities, and that the efforts complement each other. In this spirit, this white paper describes the R&D activities required to prepare for this software upgrade.Peer reviewe

Simulation Application for the LHCb Experiment

We describe the LHCb detector simulation application (Gauss) based on the Geant4 toolkit. The application is built using the Gaudi software framework, which is used for all event-processing applications in the LHCb experiment. The existence of an underlying framework allows several common basic services such as persistency, interactivity, as well as detector geometry description or particle data to be shared between simulation, reconstruction and analysis applications. The main benefits of such common services are coherence between different event-processing stages as well as reduced development effort. The interfacing to Geant4 toolkit is realized through a façade (GiGa) which minimizes the coupling to the simulation engine and provides a set of abstract interfaces for configuration and event-by-event communication. The Gauss application is composed of three main blocks, i.e. event generation, detector response simulation and digitization which reflect the different stages performed during the simulation job. We describe the overall design as well as the details of Gauss application with a special emphasis on the configuration and control of the underlying simulation engine. We also briefly mention the validation strategy and the planing for the LHCb experiment simulation

Recent Developments in the Geant4 Hadronic Framework

In this paper we present the recent developments in the Geant4 hadronic framework. Geant4 is the main simulation toolkit used by the LHC experiments and therefore a lot of effort is put into improving the physics models in order for them to have more predictive power. As a consequence, the code complexity increases, which requires constant improvement and optimization on the programming side. At the same time, we would like to review and eventually reduce the complexity of the hadronic software framework. As an example, a factory design pattern has been applied in Geant4 to avoid duplications of objects, like cross sections, which can be used by several processes or physics models. This approach has been applied also for physics lists, to provide a flexible configuration mechanism at run-time, based on macro files. Moreover, these developments open the future possibility to build Geant4 with only a specified sub-set of physics models. Another technical development focused on the reproducibility of the simulation, i.e. the possibility to repeat an event once the random generator status at the beginning of the event is known. This is crucial for debugging rare situations that may occur after long simulations. Moreover, reproducibility in normal, sequential Geant4 simulation is an important prerequisite to verify the equivalence with multithreaded Geant4 simulations

Aspects of String unification

We consider the phenomenological implications of a class of compactified string theories which naturally reproduces the flavour multiplet structure of the Standard Model. The implications for gauge unification depends on which of three possibilities is realised for obtaining light Higgs multiplets. The more conventional one leads to predictions for the gauge couplings close to that of the MSSM but with an increased value of the unification scale. The other two cases offer a mechanism for bringing the prediction for the strong coupling into agreement with the measured value while still increasing the unification scale. The various possibilities lead to different expectations for the structure of the quark masses

MetaHEP: Meta learning for fast shower simulation of high energy physics experiments

For High Energy Physics (HEP) experiments, such as the Large Hadron Collider (LHC) experiments, the calorimeter is a key detector to measure the energy of particles. Incident particles interact with the material of the calorimeter, creating cascades of secondary particles, so-called showers. A detailed description of the showering process relies on simulation methods that precisely describe all particle interactions with matter. Constrained by the need for precision, the simulation of calorimeters is inherently slow and constitutes a bottleneck for HEP analysis. Furthermore, with the upcoming high luminosity upgrade of the LHC and a much-increased data production rate, the amount of required simulated events will increase. Several research directions have recently investigated the use of Machine Learning based models to accelerate particular calorimeter response simulation. These models typically require a large amount of data and time for training, and the result is a simulation tuned specifically to this configuration. Meta-learning has emerged recently as a fast learning algorithm using small training datasets. In this paper, we use a meta-learning approach that “learns to learn” to generate showers from multiple calorimeter geometries, using a first-order gradient-based algorithm. We present MetaHEP, the first application of the meta-learning approach to accelerate shower simulation using very high granular data and using one of the calorimeters proposed for the Future Circular Collider (FCC), a next-generation of high-performance particle colliders

Meta-learning for multiple detector geometry modeling

The simulation of the passage of particles through the detectors of High Energy Physics (HEP) experiments is a core component of any physics analysis. A detailed and accurate simulation of the detector response using the Geant4 toolkit is a time and CPU consuming process. With the upcoming high luminosity LHC upgrade, with more complex events and a much increased trigger rate, the amount of required simulated events will increase. Several research directions investigated the use of Machine Learning based models to accelerate particular detector response simulation. This results in a specifically tuned simulation and generally these models require a large amount of data for training. Meta learning has emerged recently as fast learning algorithm using small training datasets. In this paper, we propose a meta-learning model that “learns to learn” to generate electromagnetic showers using a first-order gradient based algorithm. This model is trained on multiple detector geometries and can rapidly adapt to a new geometry using few training samples