DELPHES, a framework for fast simulation of a generic collider experiment

By S. Ovyn, X. Rouby, V. Lemaitre. 2009

307 citations recorded by the source corpus.

It is sometimes difficult to know whether theoretical predictions can be observed in a high energy collider experiment, especially when expected experimental signature involve jets and missing transverse energy. For this purpose, we have designed a new C++ based framework, DELPHES, performing a fast multipurpose detector response simulation. The simulation includes a tracking system, embedded into a magnetic field, calorimeters and a muon system, and possible very forward detectors arranged along the beamline. The framework is interfaced to standard file formats (e.g. Les Houches Event File or HepMC) and outputs observables such as isolated leptons, missing transverse energy and collection of jets which can be used for dedicated analyses. The simulation of the detector response takes into account the effect of magnetic field, the granularity of the calorimeters and subdetector resolutions. A simplified preselection can also be applied on processed events for trigger emulation. Detection of very forward scattered particles relies on the transport in beamlines with the HECTOR software. Finally, the FROG 2D/3D event display is used for visualisation of the collision final states. Comment: 36 pages, 13 figures, 7 tables. Updated content for Delphes v1.8. Updated bibliography. Updated user manual

Subfields: Computer Networks and Communications, Information Systems and Management, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/2be389293c3f37a73477013d75bd39562999ac70

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Citations in the corpus

Characterising dark matter searches at colliders and direct detection experiments: vector mediators (2014, unknown): “mits, we use the MSTW2008NLO [70] PDFs. The parton level process produced by POWHEG is matched to Pythia 8.180 [71] for showering and hadronisation and put through a detector simulation using Delphes3[72,73], with parameters that are tuned to the CMS detector. Subsequently, the mono-jet selection cu”
Searching for exotic particles in high-energy physics with deep learning (2014, methodology): “Simulated events are generated with the madgraph5 [13] event generator, with showering and hadronization performed by pythia [14] and detector response simulated by delphes [15].”
MadGraph 5: going beyond (2011, methodology): “In that case, the user can also choose to directly pass the events through Pythia 6 for hadronization, and use a fast detector simulation, either Delphes [74] or PGS [75].”
The Large Hadron-Electron Collider at the HL-LHC (2020, methodology): “[483] for the LHeC including a DELPHES [467] detector simulation.”
Jet Substructure Classification in High-Energy Physics with Deep Neural Networks (2016, unknown): “426 , and response of the detectors simulated with delphes [30] v3.”
Deep Learning, Dark Knowledge, and Dark Matter (2014, background): “…that are standard in the high-energy physics community: collision events are generated with the madgraph 5 (Alwall et al., 2011) event generator, showering and hadronization is performed by pythia (Sjostrand et al., 2006), and the detector response is simulated by delphes (Ovyn et al., 2009).”
The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations (2014, methodology): “t to LO+PS and to fLO computations are identical. This is not the case at the NLO: the MC@NLO cross sections used in NLO+PS computations 44Or install Delphes2, if one wanted to use the older Delphes2 [217] version. – 64 – are diﬀerent from the ﬁxed-order ones used in fNLO computations (the former co”