Abstract The XENON100 dark matter experiment uses liquid xenon (LXe) in a time projection chamber (TPC) to search for xenon nuclear recoils resulting from the scattering of dark matter Weakly Interacting Massive Particles (WIMPs). In this paper we present a detailed description of the detector design and present performance results, as established during the commissioning phase and during the first science runs. The active target of XENON100 contains 62 kg of LXe, surrounded by an LXe veto of 99 kg, both instrumented with photomultiplier tubes (PMTs) operating inside the liquid or in xenon gas. The LXe target and veto are contained in a low-radioactivity stainless steel vessel, embedded in a passive radiation shield and is installed underground at the Laboratori Nazionali del Gran Sasso (LNGS), Italy. The experiment has recently published results from a 100 live-days dark matter search. The ultimate design goal of XENON100 is to achieve a spin-independent WIMP-nucleon scattering cross section sensitivity of σ  = 2 × 10 −45  cm 2 for a 100 GeV/c 2 WIMP.

The XENON100 dark matter experiment

The worldwide race towards direct dark matter detection in the form of Weakly Interacting Massive Particles (WIMPs) has been dramatically accelerated by the remarkable progress and evolution of liquid xenon time projection chambers (LXeTPCs). With a realistic discovery potential, Xenon100 has already reached a sensitivity of 7 × 10−45 cm2, and continues to accrue data at the Laboratori Nazionali del Gran Sasso (LNGS) in Italy towards its ultimate sensitivity reach at the σ SI ∼ 2 × 10−45 cm2 level for the spin-independent WIMP-nucleon cross-section. To fully explore the favoured parameter space for WIMP dark matter in search of a first robust and statistically significant discovery, or to confirm any hint of a signal from Xenon100, the next phase of the Xenon program will be a detector at the ton scale – Xenon1T. The Xenon1T detector, based on 2.2 ton of LXe viewed by low radioactivity photomultiplier tubes and housed in a water Cherenkov muon veto at LNGS, is presented. With an experimental aim of probing WIMP interaction cross-sections above of order σ SI ∼ 2 × 10−47 cm2 within 2 years of operation, Xenon1T will provide the sensitivity to probe a particularly favourable region of electroweak physics on a timescale compatible with complementary ground and satellite based indirect searches and with accelerator dark matter searches at the LHC. Indeed, for a σ SI ∼ 10−45 cm2 and 100 GeV/c2 WIMP mass, Xenon1T could detect of order 100 events in this exposure, providing statistics for placing significant constraints on the WIMP mass.

The XENONnT Dark Matter Search Experiment

The analysis of complex distributed systems requires dedicated software tools. The mCRL language and toolset have been developed to support such analysis. We highlight changes and improvements made to the toolset in recent years. On the one hand, these affect the scope of application, which has been broadened with extended support for data structures like infinite sets and functions. On the other hand, considerable progress has been made regarding the performance of our tools for state space generation and model checking, due to improvements in symbolic reduction techniques and due to a shift towards parity game-based solving. We also discuss the software architecture of the toolset, which was well suited to accommodate the above changes, and we address a number of case studies to illustrate the approach.

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An overview of the mCRL2 toolset and its recent advances

Reasoning about the correctness of parallel and distributed systems requires automated tools. By now, the mCRL2 toolset and language have been developed over a course of more than fifteen years. In this paper, we report on the progress and advancements over the past six years. Firstly, the mCRL2 language has been extended to support the modelling of probabilistic behaviour. Furthermore, the usability has been improved with the addition of refinement checking, counterexample generation and a user-friendly GUI. Finally, several performance improvements have been made in the treatment of behavioural equivalences. Besides the changes to the toolset itself, we cover recent applications of mCRL2 in software product line engineering and the use of domain specific languages (DSLs).

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The mCRL2 toolset for analysing concurrent systems : improvements in expressivity and usability

Grid computing is the federation of resources from multiple locations to facilitate resource sharing and problem solving over the Internet. The challenge of finding services or resources in Grid environments has recently been the subject of many papers and researches. These researches and papers evaluate their approaches only by simulation and experiments. Therefore, it is possible that some part of the state space of the problem is not analyzed and checked well. To overcome this defect, model checking as an automatic technique for the verification of the systems is a suitable solution. In this paper, an adopted type of resource discovery approach to address multi-attribute and range queries has been presented. Unlike the papers in this scope, this paper decouple resource discovery behavior model to data gathering, discovery and control behavior. Also it facilitates the mapping process between three behaviors by means of the formal verification approach based on Binary Decision Diagram (BDD). The formal approach extracts the expected properties of resource discovery approach from control behavior in the form of CTL and LTL temporal logic formulas, and verifies the properties in data gathering and discovery behaviors comprehensively. Moreover, analyzing and evaluating the logical problems such as soundness, completeness, and consistency of the considered resource discovery approach is provided. To implement the behavior models of resource discovery approach the ArgoUML tool and the NuSMV model checker are employed. The results show that the adopted resource discovery approach can discovers multi-attribute and range queries very fast and detects logical problems such as soundness, completeness, and consistency.

Behavioral modeling and formal verification of a resource discovery approach in Grid computing

We present LHCbDirac, an extension of the DIRAC community Grid solution that handles LHCb specificities. The DIRAC software has been developed for many years within LHCb only. Nowadays it is a generic software, used by many scientific communities worldwide. Each community wanting to take advantage of DIRAC has to develop an extension, containing all the necessary code for handling their specific cases. LHCbDirac is an actively developed extension, implementing the LHCb computing model and workflows handling all the distributed computing activities of LHCb. Such activities include real data processing (reconstruction, stripping and streaming), Monte-Carlo simulation and data replication. Other activities are groups and user analysis, data management, resources management and monitoring, data provenance, accounting for user and production jobs. LHCbDirac also provides extensions of the DIRAC interfaces, including a secure web client, python APIs and CLIs. Before putting in production a new release, a number of certification tests are run in a dedicated setup. This contribution highlights the versatility of the system, also presenting the experience with real data processing, data and resources management, monitoring for activities and resources.

LHCbDirac: Distributed computing in LHCb

Developing correct concurrent software is challenging. Design errors can result in deadlocks, race conditions and livelocks, and discovering these is difficult. A serious obstacle for an industrial uptake of rigorous analysis techniques such as model checking is the learning curve associated to the languages — typically temporal logics — used for specifying the application-specific properties to be checked. To bring the process of correctly eliciting functional properties closer to software engineers, we introduce PASS, a Property ASSistant wizard as part of a UML-based front-end to the mCRL2 toolset. PASS instantiates pattern templates using three notations: a natural language summary, a μ-calculus formula and a UML sequence diagram depicting the desired behavior. Most approaches to date have focused on LTL, which is a state-based formalism. Conversely, μ-calculus is event-based, making it a good match for sequence diagrams, where communication between components is depicted. We revisit a case study from the Grid domain, using PASS to obtain the formula and monitor for checking the property.

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Property specification made easy : harnessing the power of model checking in UML designs

DIRAC (Distributed Infrastructure with Remote Agent Control) is the grid solution designed to support production activities as well as user data analysis for the Large Hadron Collider "beauty" experiment. It consists of cooperating distributed services and a plethora of light-weight agents delivering the workload to the grid resources. Services accept requests from agents and running jobs, while agents actively fulfill specific goals. Services maintain database back-ends to store dynamic state information of entities such as jobs, queues, or requests for data transfer. Agents continuously check for changes in the service states, and react to these accordingly. The logic of each agent is rather simple, the main source of complexity lies in their cooperation. These agents run concurrently, and communicate using the services' databases as a shared memory for synchronizing the state transitions. Despite the effort invested in making DIRAC reliable, entities occasionally get into inconsistent states. Tracing and fixing such behaviors is difficult, given the inherent parallelism among the distributed components and the size of the implementation. In this paper we present an analysis of DIRAC with mCRL2, process algebra with data. We have reverse engineered two critical and related DIRAC subsystems, and subsequently modeled their behavior with the mCRL2 toolset. This enabled us to easily locate race conditions and live locks which were confirmed to occur in the real system. We further formalized and verified several behavioral properties of the two modeled subsystems.

Using Model Checking to Analyze the System Behavior of the LHC Production Grid

One of the challenges in concurrent software development is early discovery of design errors which could lead to deadlocks or race-conditions. For safety-critical and complex distributed applications, traditional testing does not always expose such problems. Performing more rigorous formal analysis typically requires a model, which is an abstraction of the system. For object-oriented software, UML is the industry-adopted modeling language. UML offers a number of views to present the system from different perspectives. Behavioral views are necessary for the purpose of model checking, as they capture the dynamics of the system. Among them are sequence diagrams, in which the interaction between components is modeled by means of message exchanges. UML 2.x includes rich features that enable modeling code-like structures, such as loops, conditions and referring to existing interactions. We present an automatic procedure for translating UML into mCRL2 process algebra models. Our prototype is able to produce a formal model, and feed model-checking traces back into any UML modeling tool, without the user having to leave the UML domain. We argue why previous approaches of which we are aware have limitations that we overcome. We further apply our methodology on the Grid framework used to support production activities of one of the LHC experiments at CERN.

Daniela Remenska

Papers

LHCbDirac: Distributed computing in LHCb

Property specification made easy : harnessing the power of model checking in UML designs

Using Model Checking to Analyze the System Behavior of the LHC Production Grid

From UML to Process Algebra and Back: An Automated Approach to Model-Checking Software Design Artifacts of Concurrent Systems

Using model checking to analyze the system behavior of the LHC production grid