CAPITULO I: ASPECTOS GENERALES DEL TURISMO RURAL COMUNITARIO

Data acquisition is an important part of the ATLAS experiment and introduces a challenge of recording the properties of processes of interest. The LHC must operate at a high rate to produce analysable quantities of rare events. The ATLAS detector must deal with a bunch crossing rate of 40 MHz and filter out events to an acceptable rate for storage while still

capturing the most important physics processes. The ATLASTrigger and Data Acquisition

system (TDAQ) is in place to reduce data to an adequate level for storage but this must be done in real-time. Decision-making and reconstruction at the triggering stage is known as

online, whereas general ATLAS reconstruction is referred to as offline. The trigger works

in two stages, firstly a hardware trigger is in place followed by a secondary software based

trigger system. The hardware component is known as the Level 1 (L1) [114] trigger which

first encounters all pp collisions. The rejection of events to ultimately reduce the rate to

7̃0 kHZ is mostly based on calorimeter and muon sub-detector information. The L1 trigger is the fastest part of the system and the inner detector algorithms cannot process events

at such a high rate so ID information is omitted at this stage. The High Level Trigger

(HLT) [115] is composed of the software triggers and is split into two parts: Level 2 (L2)

and the Event Filter (EF). The HLT reduces the event storage rate even further to around

7̃00-1000 Hz.

The initial stages of the L1 trigger are based on identifying high-p_T objects or events

with high E_Tmiss using basic calorimeter clustering and track information from the muon

spectrometer. The performance of the detector is sacrificed for the purpose of speed. Granularity of the EM calorimeter is reduced and only some compartments of the muon system are used for read out. Regions of Interest (RoIs) are identified by the L1 trigger in

η−φspace for each event if they pass certain threshold requirements, these are then passed

on the Central Trigger Processor (CTP) to be matched to a set menu of triggers.

The HLT receives the RoIs from L1 and begins to process the events using full reconstruction, calibration and identification algorithms similar to those applied offline. The L2 stage utilises additional detector information where necessary and then transfers the events to the EF to fully reconstruct the entire event and perform the final selection.

Chapter 4

Object Reconstruction

The ATLAS experiment relies heavily on the reconstruction, identification and calibration of objects within the detector for all physics analyses. Reconstructing tracks from charged particles and locating collision and decay vertices constitute the basis of ATLAS reconstruction. The association of tracks to the primary vertex (PV) and determination of track parameters with respect to a given vertex offers the opportunity to suppress pile-up effects. Pile-up is where physics objects are reconstructed from interactions other than the hard scatter collision within a bunch crossing.

Energy deposits in the calorimeters form the basic constituents of jets which are

supplemented by track and vertex information to define objects such ab-jets andτ-leptons.

Both of which are important for the final state addressed in this thesis. This chapters aims to give an overview of the reconstruction algorithms and identification procedures for all physics objects used within ATLAS.

4.1 Tracks and Vertices

Track reconstruction for charged particles begins in the pixel and SCT detectors with

cluster creation. Hits in the pixel and strip sensors are grouped into clusters where

the energy produces a charge above threshold. Three-dimensional measurements called space-points are created from the clusters to represent the point at which the particle crossed the sensor. The total collected charge in a pixel sensor is proportional to the length of the traversing path through the medium. Calorimeter cell clustering can aid in recovering trajectory information using the incident angle of the particle and the intersection point.

Additional information utilising the TRT can be found in [116].

4.1. Tracks and Vertices 40 Track seeds are defined as a combination of three space-points and must satisfy strict criteria to be identified as a track. An iterative track finding algorithm uses a combinatorial Kalman

filter [117] to build track candidates and combine additional space-points compatible with

the preliminary trajectory. Following this, ambiguity solving and track-fitting rejects track

candidates and calculates more precise track parameters [118,119].

Basic track quality criteria includes:

• p_T>400 MeV,

• |η|<2.5,

• minimum of 7 pixel and SCT clusters (12 are expected),

• maximum of one shared pixel cluster or two shared SCT clusters on the same layer,

• |dBL₀ |<2.0 mm,

• and |z₀BLsinθ|< 3.0 mm.

Impact parameters of a track, shown in Figure 4.1, are estimated using a perfect helical trajectory in a uniform magnetic field but compensated by particle energy loss throughout the material. Once the track candidates are fully reconstructed particle charge and momentum are calculated and added to perigee parameters particular to each track. The charged particles follow a circular trajectory in the transverse plane of the ID magnetic field and are described by a set of parameters with respect to the primary vertex. Parameters

include: the inverse transverse momentum q/pT, where q is the particle charge, the

azimuthal (φ) and polar (θ) angles, and the transverse (d0) and longitudinal (z0) impact

parameters. The impact parameters are defined as the point of closest approach to the beam line (BL).

The vertex reconstructionstrategy for ATLAS Run-2 mirrors that used in Run-1 where an iterative vertex finding algorithm aims to find a common origin point from the several charged particle tracks tagged in the ID. Vertex seeds are constructed from the reconstructed

tracksz-positions at the crossing point with the beam line [120,121]. The general algorithm

4.1. Tracks and Vertices 41 d0 φ x y θ z r z0

Figure 4.1: Diagrams of the transverse and longitudinal coordinate systems to define impact parameters with respect to the point of closest approach to the primary vertex.

• Select tracks that pass vertex reconstruction selection criteria [122].

• Perform aχ2 fit to find a common vertex using a jet seed and collected tracks.

• Iteratively perform the fit procedure and down-weight tracks as they become more

incompatible and recompute the vertex position. Tracks displaced from the fitted

vertex by>7σ of the three-dimensional Gaussian distribution are removed and used

to seed a new one.

• After determining a vertex position, incompatible tracks are removed from the

associated set and can be used to refit another vertex.

• The procedure is repeated until there are no free tracks left in the event or no

additional vertex can be computed from the remaining tracks.

- All vertices require association to at least two tracks.

The algorithm outputs a set of the vertex objects with their three-dimensional vertex position, covariance matrices and the associated tracks. A luminous region inside the ATLAS detector where proton collisions occur is known as the beamspot position. The shape and position of the beamspot can be used to constrain the transverse position

resolution of vertices reconstructed from a small collection of tracks [123,124].

The efficiency of the vertex reconstruction is dependent on the average number of inelastic