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The search for new comparably light (well below the electroweak scale) feebly interacting particles is an exciting possibility to explain some mysterious phenomena in physics, among them the origin of Dark Matter. The sensitivity study through detailed simulation of projected experiments is a key point in estimating their potential for discovery. Several years ago we created the DMG4 package for the simulation of DM (Dark Matter) particles in fixed target experiments. The natural approach is to integrate this simulation into the same program that performs the full simulation of particles in the experiment setup. The Geant4 toolkit framework was chosen as the most popular and versatile solution nowadays. The simulation of DM particles production by this package accommodates several possible scenarios, employing electron, muon or photon beams and involving various mediators, such as vector, axial vector, scalar, pseudoscalar, or spin 2 particles. The bremsstrahlung, annihilation or Primakoff processes can be simulated. The package DMG4 contains a subpackage DarkMatter with cross section methods weakly connected to Geant4. It can be used in different frameworks. In this paper, we present the latest developments of the package, such as extending the list of possible mediator particle types, refining formulas for the simulation and extending the mediator mass range. The user interface is also made more flexible and convenient. In this work, we also demonstrate the usage of the package, the improvements in the simulation accuracy and some cross check validations. Program title: DMG4 CPC Library link to program files: https://doi.org/10.17632/cmr4bcrj6j.1 Licensing provisions: GNU General Public License 3 Programming language: c++ Journal reference of previous version: Comput. Phys. Commun. 269 (2021) 108129 Does the new version supersede the previous version?: Yes Reasons for the new version: Numerous developments, addition of new features Summary of revisions: WW approximation cross sections for the muon beam are implemented and cross-checked, models with semivisible A ′ (inelastic Dark Matter) and spin 2 mediators are added. The range of possible mediator masses is extended. Several important improvements for the annihilation processes are made, the number of possible annihilation processes is extended. User interface is improved. Several bugs are fixed. Nature of problem: For the simulation of Dark Matter production processes in fixed target experiments a code that can be easily integrated in programs for the full simulation of experimental setup is needed. Solution method: A fully Geant4 compatible DM simulation package DMG4 was presented in 2020. We present numerous further developments of this package. [ABSTRACT FROM AUTHOR]

A common feature of computational modelling and simulation research is the need to perform many tasks in complex sequences to achieve a usable result. This will typically involve tasks such as preparing input data, pre-processing, running simulations on a local or remote machine, post-processing, and performing coupling communications, validations and/or optimisations. Tasks like these can involve manual steps which are time and effort intensive, especially when it involves the management of large ensemble runs. Additionally, human errors become more likely and numerous as the research work becomes more complex, increasing the risk of damaging the credibility of simulation results. Automation tools can help ensure the credibility of simulation results by reducing the manual time and effort required to perform these research tasks, by making more rigorous procedures tractable, and by reducing the probability of human error due to a reduced number of manual actions. In addition, efficiency gained through automation can help researchers to perform more research within the budget and effort constraints imposed by their projects. This paper presents the main software release of FabSim3, and explains how our automation toolkit can improve and simplify a range of tasks for researchers and application developers. FabSim3 helps to prepare, submit, execute, retrieve, and analyze simulation workflows. By providing a suitable level of abstraction, FabSim3 reduces the complexity of setting up and managing a large-scale simulation scenario, while still providing transparent access to the underlying layers for effective debugging. The tool also facilitates job submission and management (including staging and curation of files and environments) for a range of different supercomputing environments. Although FabSim3 itself is application-agnostic, it supports a provably extensible plugin system where users automate simulation and analysis workflows for their own application domains. To highlight this, we briefly describe a selection of these plugins and we demonstrate the efficiency of the toolkit in handling large ensemble workflows. Program Title: FabSim3 CPC Library link to program files: https://doi.org/10.17632/6nfrwy7ptj.1 Licensing provisions: BSD 3-clause Programming language: Python 3 Nature of problem: Many aspects are crucial for obtaining reproducible and robust simulation results. For instance, we need to curate all the inputs and outputs for later scrutiny, scrutinize the model behaviour under slightly perturbed circumstances, quantify the propagation of key uncertainties from input data and known parameters and analyze the sensitivity for any parameters for which the exact specification eludes us. Solution method: FabSim3 uses a range of methods to provide automation. These primarily include: (i) SSH + Fabric2 to enable remote execution of SSH commands, (ii) an internal parameter state space using primarily Python dict objects that can be customized with machine- plugin- and user-specific modifications, (iii) Python templating to quickly enable the insertion of state space variables into supercomputing scripts, (iv) multiprocessing and/or QCG-PilotJob to enable efficient submission and execution of job arrays and (v) a system of flexibly installable and modifiable Python3 plugins which allows users to create and customize application-specific functionalities without modifying the core code base. In addition to the written code, FabSim3 also relies on a set of user conventions to maintain a separation of concerns (particularly between machine-, user- and application-specific settings). Additional comments including restrictions and unusual features: This paper serves as the definitive reference for FabSim3. [ABSTRACT FROM AUTHOR]

A review of the video game "Walden" developed by Game Innovation Lab and based on the novel "Walden" by Henry David Thoreau is presented.