Silicon pixel sensor R&amp;D for the CLIC tracking detector

Kröger, Jens

doi:10.1088/1748-0221/15/08/c08005

Cited by 9 publications

(10 citation statements)

References 23 publications

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“…Silicon vertex and tracking detector R&D for CLIC Katharina Dort A hit detection efficiency well above 99.5 % has been measured. While the efficiency decreases with increasing threshold, it has been shown that a larger resistivity and a higher bias voltage extend the efficient operation window [8]. The larger depleted volume, giving rise to a higher signal, is the reason for the better performance.…”

Section: Pos(ichep2020)836mentioning

confidence: 99%

Silicon Vertex and Tracking Detector R&D for CLIC

Dort¹

2021

Proceedings of 40th International Conference on High Energy Physics — PoS(ICHEP2020)

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The physics aims at the proposed future high-energy linear e+e-collider CLIC pose challenging demands on the performance of the detector system. In particular, the vertex and tracking detectors have to combine a spatial resolution of a few micrometres and a low material budget with a timestamping accuracy of a few nanoseconds. For the vertex detector, fine-pitch sensors, dedicated 65nm readout ASICs, fine-pitch bonding techniques using solder bumps or anisotropic conductive films as well as monolithic devices based on Silicon-On-Insulator technology are explored. Fully monolithic CMOS sensors with large and small collection electrodes are under investigation for the large surface CLIC tracker. This contribution gives an overview of the CLIC vertex and tracking detector R&D, focusing on recent results from test-beam campaigns and simulation-based sensor optimisation studies.

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Section: Pos(ichep2020)836mentioning

confidence: 99%

Silicon Vertex and Tracking Detector R&D for CLIC

Dort¹

2021

Proceedings of 40th International Conference on High Energy Physics — PoS(ICHEP2020)

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“…In the conventional approach, which has been widely used, e.g. in [11], a fixed time window corresponding to the largest possible time interval is used as illustrated at the top left of Fig. 3.…”

Section: Conventional Event Building With the Aida-tlu And The Mimosa26 Sensorsmentioning

confidence: 99%

“…It was initially developed within the CLIC Detector & Physics (CLICdp) collaboration [6,7] and is now employed and extended by a number of users and developers from different experiments supporting an increasing variety of sensor prototypes. A number of publications underline the versatility and the success of the framework [8][9][10][11][12][13][14][15]. Through its modularity, it aims to maximize synergies between different research groups by providing a reusable and extendable code basis and thus suppressing the need for numerous similar frameworks.…”

Section: Introductionmentioning

confidence: 99%

Efficient Analysis of Test-beam Data with the Corryvreckan Framework

Kröger

Huth

2021

Proceedings of the 29th International Workshop on Vertex Detectors (VERTEX2020)

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Stringent requirements are posed on the next generations of vertex and tracking detectors for highenergy physics experiments to reach the foreseen physics goals. A large variety of silicon pixel sensors targeting the specific needs of each use case are developed and tested both in laboratory and test-beam measurement campaigns. Corryvreckan is a flexible, fast and lightweight test-beam data reconstruction and analysis framework based on a modular concept of the reconstruction chain. It is designed to fulfil the requirements for offline event building in complex data-taking environments combining detectors with different readout schemes. Its modular architecture separates the framework core from the implementation of reconstruction, analysis and detector specific algorithms. In this paper, a brief overview of the software framework and the reconstruction and analysis chain is provided. This is complemented by an example analysis of a data set using the offline event building capabilities of the framework and an improved event building scheme allowing for a more efficient usage of test-beam data exploiting the pivot pixel information of the Mimosa26 sensors.

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“…The framework has already been used in a number of different scenarios, such as the reconstruction of data recorded using the high-rate CLICdp beam telescope at the CERN SPS [13,14], the analysis of test beam data from EUDET-type beam telescopes at the DESY II Test Beam Facility [15][16][17][18][19], the qualification of calorimeter modules with minimum ionising particles [20], and the reconstruction of silicon pixel detector simulations [12].…”

Section: Introductionmentioning

confidence: 99%

Corryvreckan: A Modular 4D Track Reconstruction and Analysis Software for Test Beam Data

Dannheim,

Dort,

Huth

et al. 2020

Preprint

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Corryvreckan is a versatile, highly configurable software with a modular structure designed to reconstruct and analyse test beam and laboratory data. It caters to the needs of the test beam community by providing a flexible offline event building facility to combine detectors with different read-out schemes, with or without trigger information, and includes the possibility to correlate data from multiple devices based on timestamps.Hit timing information, available with high precision from an increasing number of detectors, can be used in clustering and tracking to reduce combinatorics. Several algorithms, including an implementation of Millepede-II, are provided for offline alignment. A graphical user interface enables direct monitoring of the reconstruction progress and can be employed for quasi-online monitoring during data taking.This work introduces the Corryvreckan framework architecture and user interface, and provides a detailed overview of the event building algorithm. The reconstruction and analysis capabilities are demonstrated with data recorded at the DESY II Test Beam Facility using the EUDAQ2 data acquisition framework with an EUDET-type beam telescope, a Timepix3 timing reference, a fine-pitch planar silicon sensor with CLICpix2 readout and the AIDA Trigger Logic Unit. The individual steps of the reconstruction chain are presented in detail. K: Software architectures (event data models, frameworks and databases); Analysis and statistical methods; Data processing methods; Pattern recognition, cluster finding, calibration and fitting methods; Solid state detectors

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Silicon pixel sensor R&D for the CLIC tracking detector

Cited by 9 publications

References 23 publications

Silicon Vertex and Tracking Detector R&D for CLIC

Silicon Vertex and Tracking Detector R&D for CLIC

Efficient Analysis of Test-beam Data with the Corryvreckan Framework

Corryvreckan: A Modular 4D Track Reconstruction and Analysis Software for Test Beam Data

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