| 104 | | >>>(Michele suggested introduction) |
| 105 | | |
| 106 | | High energy particle collisions can produce a large variety of final states. Highly sophisticated detectors are designed in order to detect and precisely measure particles originating from such collisions. Experimental collaborations often rely on Monte-Carlo event generation for designing and optimizing specific analysis strategies. Whenever such studies require a high level of accuracy, the interactions of long-lived particles with the detector matter content are fully simulated with the \GEANT package~\cite{bib:geant4}, electronics response is emulated by dedicated routines, and final observables are reconstructed by means of complex algorithms. For preliminary studies, where such a high level of accuracy is not needed, LHC collaborations have developed their own fast-simulation techniques~\cite{bib:atlfast1,bib:atlfast2,bib:cmsfast1,bib:cmsfast2,bib:cmsfast3} which are 2 to 3 orders of magnitude faster than fully GEANT based simulation. |
| 107 | | |
| 108 | | This procedure requires expertise and the deployment of large scale computing resources that can be handled only by large collaborations. |
| 109 | | For most phenomenological studies, such a level of complexity is not needed and a simplified approach based on the parametrisation of the detector response is in general good enough. In 2009, the \DELPHES framework~\cite{bib:delphes} was designed to achieve such goal. |
| 110 | | |
| 111 | | \DELPHES takes as input the most common event generator output data-formats and performs a fast and realistic simulation of a general purpose collider detector. |
| 112 | | To do so, long-lived particles emerging from the hard scattering are propagated to the calorimeters within a uniform magnetic field along the beam direction. The particle energies are computed by smearing the initial long-lived visible particles momenta according to the resolution of the relevant sub-detectors. As a result, high-level physics objects such as jets, missing energy, isolated leptons and photons, and taus can be computed. |
| 113 | | |
| 114 | | With respect to its predecessor~\cite{bib:delphes}, the present \DELPHES version now includes a technique allowing to combine and optimally use the information of all the sub-detectors. This approach is particularly suitable to the treatment of pile-up, which has also been included in \DELPHES 3.0. Other features such as $b$ and $\tau$-tagging have been revisited, and it is now possible to apply an energy scale correction on jets. From a technical perspective, the code structure is now fully modular, providing a greater flexibility to the user. |
| 115 | | |
| 116 | | The modeling of the detector, as well as the reconstruction and validation of the physical observables will be described. A couple of illustrative use cases of Delphes in the context of LHC studies are presented. |
