The version 3.0 of the Delphes fast-simulation is presented. The goal of Delphes is to allow the simulation of a multipurpose detector for phenomenological studies. The simulation includes a track ...propagation system embedded in a magnetic field, electromagnetic and hadron calorimeters, and a muon identification system. Physics objects that can be used for data analysis are then reconstructed from the simulated detector response. These include tracks and calorimeter deposits and high level objects such as isolated electrons, jets, taus, and missing energy. The new modular approach allows for greater flexibility in the design of the simulation and reconstruction sequence. New features such as the particle-flow reconstruction approach, crucial in the first years of the LHC, and pile-up simulation and mitigation, which is needed for the simulation of the LHC detectors in the near future, have also been implemented. The Delphes framework is not meant to be used for advanced detector studies, for which more accurate tools are needed. Although some aspects of Delphes are hadron collider specific, it is flexible enough to be adapted to the needs of electron-positron collider experiments.
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The Matrix Element Method (MEM) is a powerful method to extract information from measured events at collider experiments. Compared to multivariate techniques built on large sets of ...experimental data, the MEM does not rely on an examples-based learning phase but directly exploits our knowledge of the physics processes. This comes at a price, both in term of complexity and computing time since the required multi-dimensional integral of a rapidly varying function needs to be evaluated for every event and physics process considered. This can be mitigated by optimizing the integration, as is done in the MoMEMta package, but the computing time remains a concern, and often makes the use of the MEM in full-scale analysis unpractical or impossible. We investigate in this paper the use of a Deep Neural Network (DNN) built by regression of the MEM integral as an ansatz for analysis, especially in the search for new physics.
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•We studied copper (Cu) and silver (Ag) deposition in lake sediment.•Two sediment cores from Lake Titicaca provide a ∼2400 years history of metallurgy.•Pb isotopic fingerprinting ...allowed identifying ore sources in the central Andes.•Tiwanaku copper mining was performed by smelting of local ores.•Late intermediate, Inca and Colonial mining was performed in the Potosi region.
Copper, silver, and gold exploitation has been a foundation of economic and socio-cultural development of Andean societies, at least for the last three millennia. The main centers of pre-colonial metallurgy are well-known from archeological artifacts, but temporal gaps inherent in this record handicap a finer understanding of the modalities of ore exploitation by succeeding civilizations. A continuous record over time of trace metals emitted during ore smelting operations make lake sediments excellent candidates to fill those gaps. Two millennia of metallurgy were reconstructed from atmospherically derived metals together with lead (Pb) isotope ratios in two dated sediment cores from Lake Titicaca. The first evidence for metallurgy is found during the apogee of the Tiwanaku state (AD 800–1150), with a higher copper (Cu) accumulation that can be attributed to the smelting of local Cu ores, based on Pb isotopic fingerprinting. During the Late Intermediate Period (AD 1150–1450), recorded peaks in metal deposition that persisted for ∼ twenty years show that mining activities were intensive but discontinuous. Pb isotope ratios suggest diversified extractive activities, mainly located in the southern part of the central Altiplano. Finally, the most intense mining epoch began during the Inca Empire (ca. AD 1500) and lasted until the end of the Colonial Period (AD 1830), with unprecedented metal deposition over this interval. Pb isotope fingerprinting shows that mining operations occurred mainly in the Lake Titicaca and Potosi areas and were responsible for metal emissions recorded in the entire Altiplano, as evidenced by other studies.
The CMS silicon strip tracker, providing a sensitive area of approximately 200 m2 and comprising 10 million readout channels, has recently been completed at the tracker integration facility at CERN. ...The strip tracker community is currently working to develop and integrate the online and offline software frameworks, known as XDAQ and CMSSW respectively, for the purposes of data acquisition and detector commissioning and monitoring. Recent developments have seen the integration of many new services and tools within the online data acquisition system, such as event building, online distributed analysis, an online monitoring framework, and data storage management. We review the various software components that comprise the strip tracker data acquisition system, the software architectures used for stand-alone and global data-taking modes. Our experiences in commissioning and operating one of the largest ever silicon micro-strip tracking systems are also reviewed.
The CMS Silicon Strip Tracker at the LHC comprises a sensitive area of approximately 200 m2 and 10 million readout channels. Its data acquisition system is based around a custom analogue front-end ...chip. Both the control and the readout of the front-end electronics are performed by off-detector VME boards in the counting room, which digitise the raw event data and perform zero-suppression and formatting. The data acquisition system uses the CMS online software framework to configure, control and monitor the hardware components and steer the data acquisition. The first data analysis is performed online within the official CMS reconstruction framework, which provides many services, such as distributed analysis, access to geometry and conditions data, and a Data Quality Monitoring tool based on the online physics reconstruction. The data acquisition monitoring of the Strip Tracker uses both the data acquisition and the reconstruction software frameworks in order to provide real-time feedback to shifters on the operational state of the detector, archiving for later analysis and possibly trigger automatic recovery actions in case of errors. Here we review the proposed architecture of the monitoring system and we describe its software components, which are already in place, the various monitoring streams available, and our experiences of operating and monitoring a large-scale system.
A total of 628 bp-1 of data collected with the ALEPH detector at centre-of-mass energies from 189 to 209 GeV is analysed in the search for gauge mediated SUSY breaking (GMSB) topologies. These ...topologies include two acoplanar photons, non-pointing single photons, acoplanar leptons, large impact parameter leptons, detached slepton decay vertices, heavy stable charged sleptons and multi-leptons plus missing energy final states. No evidence is found for new phenomena, and lower limits on masses of supersymmetric particles are derived. A scan of a minimal GMSB parameter space is performed and lower limits are set for the next-to-lightest supersymmetric particle (NLSP) mass at 54 GeV/c2 and for the mass scale parameter \(\Lambda\) at 10 TeV/c2, independently of the NLSP lifetime. Including the results from the neutral Higgs boson searches, a NLSP mass limit of 77 GeV/c2 is obtained and values of \(\Lambda\) up to 16 TeV/c2 are excluded.
A measurement of the underlying activity in scattering processes with transverse momentum scale in the GeV region is performed in proton-proton collisions at sqrt(s) = 0.9 TeV, using data collected ...by the CMS experiment at the LHC. Charged hadron production is studied with reference to the direction of a leading object, either a charged particle or a set of charged particles forming a jet. Predictions of several QCD-inspired models as implemented in PYTHIA are compared, after full detector simulation, to the data. The models generally predict too little production of charged hadrons with pseudorapidity eta < 2, p_T > 0.5 GeV/c, and azimuthal direction transverse to that of the leading object.
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