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The inclusive charged particle transverse momentum distribution is measured in proton–proton collisions at s=900 GeV at the LHC using the ALICE detector. The measurement is performed in the central pseudorapidity region (|η|<0.8) over the transverse momentum range 0.15<pT<10 GeV/c. The correlation between transverse momentum and particle multiplicity is also studied. Results are presented for inelastic (INEL) and non-single-diffractive (NSD) events. The average transverse momentum for |η|<0.8 is 〈pT〉INEL=0.483±0.001 (stat.)±0.007 (syst.) GeV/c and 〈pT〉NSD=0.489±0.001 (stat.)±0.007 (syst.) GeV/c, respectively. The data exhibit a slightly larger 〈pT〉 than measurements in wider pseudorapidity intervals. The results are compared to simulations with the Monte Carlo event generators PYTHIA and PHOJET.
Inclusive transverse momentum spectra of primary charged particles in Pb–Pb collisions at √sNN=2.76 TeV have been measured by the ALICE Collaboration at the LHC. The data are presented for central and peripheral collisions, corresponding to 0–5% and 70–80% of the hadronic Pb–Pb cross section. The measured charged particle spectra in |η|<0.8 and 0.3<pT<20 GeV/c are compared to the expectation in pp collisions at the same sNN, scaled by the number of underlying nucleon–nucleon collisions. The comparison is expressed in terms of the nuclear modification factor RAA. The result indicates only weak medium effects (RAA≈0.7) in peripheral collisions. In central collisions, RAA reaches a minimum of about 0.14 at pT=6–7 GeV/c and increases significantly at larger pT. The measured suppression of high-pT particles is stronger than that observed at lower collision energies, indicating that a very dense medium is formed in central Pb–Pb collisions at the LHC.
A measurement of the multi-strange Ξ− and Ω− baryons and their antiparticles by the ALICE experiment at the CERN Large Hadron Collider (LHC) is presented for inelastic proton–proton collisions at a centre-of-mass energy of 7 TeV. The transverse momentum (pT) distributions were studied at mid-rapidity (|y|<0.5) in the range of 0.6<pT<8.5 GeV/c for Ξ− and Ξ¯+ baryons, and in the range of 0.8<pT<5 GeV/c for Ω− and Ω¯+. Baryons and antibaryons were measured as separate particles and we find that the baryon to antibaryon ratio of both particle species is consistent with unity over the entire range of the measurement. The statistical precision of the current data has allowed us to measure a difference between the mean pT of Ξ− (Ξ¯+) and Ω− (Ω¯+). Particle yields, mean pT, and the spectra in the intermediate pT range are not well described by the PYTHIA Perugia 2011 tune Monte Carlo event generator, which has been tuned to reproduce the early LHC data. The discrepancy is largest for Ω− (Ω¯+). This PYTHIA tune approaches the pT spectra of Ξ− and Ξ¯+ baryons below pT<0.85 GeV/c and describes the Ξ− and Ξ¯+ spectra above pT>6.0 GeV/c. We also illustrate the difference between the experimental data and model by comparing the corresponding ratios of (Ω−+Ω¯+)/(Ξ−+Ξ¯+) as a function of transverse mass.
The ALICE High-Level-Trigger (HLT) is a large scale computing farm designed and constructed for the purpose of the realtime reconstruction of particle interactions (events) inside the ALICE detector. The reconstruction of such events is based on the raw data produced in collisions inside the ALICE at the Large Hadron Collider. The online reconstruction in the HLT allows the triggering on certain event topologies and a significant data reduction by applying compression algorithms. Moreover, it enables a real-time verification of the quality of the data.
To receive the raw data from the various sub-detectors of ALICE, the HLT is equipped with 226 custom built FPGA-based PCI-X cards, the H-RORCs. The H-RORC interfaces the detector readout electronics to the nodes of the HLT farm. In addition to the transfer of raw data, 108 H-RORCs host 216 Fast-Cluster-Finder (FCF) processors for the Time-Projection-Chamber (TPC). The TPC is the main tracking detector of ALICE and contributes with up to 16 GB/s to over 90% of the overall data volume. The FCF processor implements the first of two steps in the data reconstruction of the TPC. It calculates the space points and their properties from charge clouds (clusters) created by charged particles traversing the TPCs gas volume. Those space points are not only the base for the tracking algorithm, but also allow for a Huffman-based data compression, which reduces the data volume by a factor of 4 to 6.
The FCF processor is designed to cope with any incoming data rate up to the maximum bandwidth of the incoming optical link (160 MB/s) without creating back-pressure to the detectors readout electronics. A performance comparison with the software implementation of the algorithm shows a speedup factor of about 20 compared with one AMD Opteron 6172 Core @ 2.1 GHz, the CPU type used in the HLT during the LHC Run1 campaign. Comparison with an Intel E5-2690 Core @ 3.0 GHz, the CPU type used by the HLT for the LHC Run2 campaign, results in a speedup factor of 8.5. In total numbers, the 216 FCF processors provide the computing performance of 4255 AMD Opteron cores or 2203 Intel cores of the previously mentioned type. The performance of the reconstruction with respect to the physics analysis is equivalent or better than the official ALICE Offline clusterizer. Therefore, ALICE data taking was switched in 2011 to FCF cluster recording and compression only, discarding the raw data from the TPC. Due to the capability to compress the clusters, the recorded data volume could be increased by a factor of 4 to 6.
For the LHC Run3 campaign, starting in 2020, the FCF builds the foundation of the ALICE data taking and processing strategy. The raw data volume (before processing) of the upgraded TPC will exceed 3 TB/s. As a consequence, online processing of the raw data and compression of the results before it enters the online computing farms is an essential and crucial part of the computing model.
Within the scope of this thesis, the H-RORC card and the FCF processor were developed and built from scratch. It covers the conceptual design, the optimisation and implementation, as well as the verification. It is completed by performance benchmarks and experiences from real data taking.
The production of low-mass dielectrons is one of the most promising tools for the investigation of chiral symmetry restoration and thermal radiation from the QGP created in heavy-ion collisions. To single out the signal characteristics of the QGP, it is crucial to understand the primordial e+e− pair production in vacuum, i.e. in inelastic proton-proton (pp) collisions. Low-mass dielectrons have been measured with ALICE at the LHC in pp collisions at s=7and13TeV, and in Pb–Pb collisions at sNN=2.76TeV. An overview of the results on dielectron production is presented, together with their implications for the direct-photon and heavy-quark production.
Die vorliegende Arbeit beschäftigt sich mit der Charakterisierung des ALTRO Chips (ALICE TPC Readout), der ein integraler und wichtiger Bestandteil der Auslesekette des TPC (Time Projection Chamber) Detektors von ALICE (A Large Ion Collider Experiment) ist. ALICE ist ein Experiment am noch im Bau befindlichen LHC (Large Hadron Collider) am CERN mit der zentralen Ausrichtung, Schwerionenkollisionen zu untersuchen. Diese sind von besonderem Interesse, da durch sie ein experimenteller Zugriff zu dem QGP (Quark Gluon Plasma) existiert, dem einzigen vom Standardmodell vorhergesagten Phasenübergang, der unter Laborbedingungen erreichbar ist. Im Jahr 2004 wurden Messungen an einem Teststrahl am CERN PS (Proton Synchrotron) durchgeführt. Der Prototyp wurde voll mit FECs bestückt, was 5400 Kanälen entspricht und einer anderen Gasmixtur (Ne/N2/CO2 90%/5%/5%) befüllt. Für das optimale Leistungsverhalten der ALICE TPC muß der Digitalprozessor im ALTRO, bestehend aus vier Berechnungseinheiten, mit den passenden Werten konfiguriert werden. Der Datenfluss beginnt mit dem BCS1 (Baseline Correction and Subtraction 1) Modul, das systematische Störungen und die Grundlinie entfernt. Da der ALTRO kontinuierlich das anliegende Signal abtastet, entfernt es automatisch langsame Grundlinienveränderungen, die Beispielsweise durch Temperaturänderungen auftreten können. Gefolgt von dem TCF (Tail Cancellation Filter), der den Schweif des langsam fallenden, vom PASA generierten Signals entfernt. Um die nichtsystematischen Störungen der Grundlinie zu entfernen, folgt die BCS2 (Baseline Correction and Subtraction 2), die auf einer gleitenden Mittelwertsberechnung mit Ausschluß von Detektorsignalen über einen doppelten Schwellenwert basiert. Die finale Einheit für die Signalverarbeitung ist die ZSU (Zero Suppression Unit), die Meßpunkte unterhalb eines definierten Schwellwertes entfernt. Hier wird der weg beschrieben die TCF und BCS1 Parameter aus vorhandenen Detektordaten zu extrahieren. Während der Analyse der Daten von kosmischen Teilchen fiel bei Signalen mit hoher Amplitude (>700 ADC) eine zusätzliche Struktur in dem Schweif auf. Der Monitor wurde deswegen mit einem gleitenden Mittelwertfilter erweitert, worauf sich diese Struktur auch in kleineren Signalen (> 200 ADC) zeigte. Dieses Signal wird von Ionen erzeugt, die zur Kathode oder zu den Pads driften, bisher ist jedoch weder die Streuung der Elektronenlawine an der Anode, noch die Variationsbreite in den erzeugten Elektronlawinen verstanden oder gemessen worden. Eine erfolgreiche Messung, sowie Charakterisierung wird in dieser Arbeit beschrieben. Im Jahr 2005 im Sommer beginnt der Einbau der Gaskammern der TPC in ALICE, die Elektronik folgt am Ende dieses Jahres. Parallel hierzu wurde der Prototyp der TPC wieder in Betrieb genommen und im Frühling wird ein kompletter Sektor mit der Detektorelektronik ausgestattet. An diesen zwei Aufbauten wird die ALTRO Charakterisierung fortgeführt, verfeinert und komplettiert.
Quarkonia, i.e. bound states of bb‾ and cc‾ quarks, are powerful observables to study the properties of nuclear matter under extreme conditions. The formation of a Quark-Gluon Plasma (QGP), which is predicted by lattice QCD calculations at high temperatures as reached at the LHC energies, has a strong influence on the production and behavior of quarkonia. The latest ALICE results on bottomonium and charmonium production in nucleus−nucleus collisions are presented. This includes measurements of the ϒ(1S) and ϒ(2S) nuclear modification factor (RAA) at forward rapidity and the J/ψ RAA and ν2 as a function of centrality, pT and rapidity in Pb–Pb collisions at sNN=5.02TeV. Also, first results from J/ψ measurements in Xe–Xe collisions at sNN=5.44TeV are presented. Further on, the experimental results are compared to various calculations from theoretical models.
Programmable hardware in the form of FPGAs found its place in various high energy physics experiments over the past few decades. These devices provide highly parallel and fully configurable data transport, data formatting, and data processing capabilities with custom interfaces, even in rigid or constrained environments. Additionally, FPGA functionalities and the number of their logic resources have grown exponentially in the last few years, making FPGAs more and more suitable for complex data processing tasks. ALICE is one of the four main experiments at the LHC and specialized in the study of heavy-ion collisions. The readout chain of the ALICE detectors makes use of FPGAs at various places. The Read-Out Receiver Cards (RORCs) are one example of FPGA-based readout hardware, building the interface between the custom detector electronics and the commercial server nodes in the data processing clusters of the Data Acquisition (DAQ) system as well as the High Level Trigger (HLT). These boards are implemented as server plug-in cards with serial optical links towards the detectors. Experimental data is received via more than 500 optical links, already partly pre-processed in the FPGAs, and pushed towards the host machines. Computer clusters consisting of a few hundred nodes collect, aggregate, compress, reconstruct, and prepare the experimental data for permanent storage and later analysis. With the end of the first LHC run period in 2012 and the start of Run 2 in 2015, the DAQ and HLT systems were renewed and several detector components were upgraded for higher data rates and event rates. Increased detector link rates and obsolete host interfaces rendered it impossible to reuse the previous RORCs in Run 2.
This thesis describes the development, integration, and maintenance of the next generation of RORCs for ALICE in Run 2. A custom hardware platform, initially developed as a joint effort between the ALICE DAQ and HLT groups in the course of this work, found its place in the Run 2 readout systems of the ALICE and ATLAS experiments. The hardware fulfills all experiment requirements, matches its target performance, and has been running stable in the production systems since the start of Run 2. Firmware and software developments for the hardware evaluation, the design of the board, the mass production hardware tests, as well as the operation of the final board in the HLT, were carried out as part of this work. 74 boards were integrated into the HLT hardware and software infrastructure, with various firmware and software developments, to provide the main experimental data input and output interface of the HLT for Run 2. The hardware cluster finder, an FPGA-based data pre-processing core from the previous generation of RORCs, was ported to the new hardware. It has been improved and extended to meet the experimental requirements throughout Run 2. The throughput of this firmware component could be doubled and the algorithm extended, providing an improved noise rejection and an increased overall mean data compression ratio compared to its previous implementation. The hardware cluster finder forms a crucial component in the HLT data reconstruction and compression scheme with a processing performance of one board equivalent to around ten server nodes for comparable processing steps in software.
The work on the firmware development, especially on the hardware cluster finder, once more demonstrated that developing and maintaining data processing algorithms with the common low-level hardware description methods is tedious and time-consuming. Therefore, a high-level synthesis (HLS) hardware description method applying dataflow computing at an algorithmic level to FPGAs was evaluated in this context. The hardware cluster finder served as an example of a typical data processing algorithm in a high energy physics readout application. The existing and highly optimized low-level implementation provided a reference for comparisons in terms of throughput and resource usage. The cluster finder algorithm could be implemented in the dataflow description with comparably little effort, providing fast development cycles, compact code and at, the same time, simplified extension and maintenance options. The performance results in terms of throughput and resource usage are comparable to the manual implementation. The dataflow environment proved to be highly valuable for design space explorations. An integration of the dataflow description into the HLT firmware and software infrastructure could be demonstrated as a proof of concept. A high-level hardware description could ease both the design space exploration, the initial development, the maintenance, and the extension of hardware algorithms for high energy physics readout applications.
During RUN3 (2021-2023) of the Large Hadron Collider, the Time Projection Chamber (TPC) of ALICE will be operated with quadruple stacks of Gas Electron Multipliers (GEMs). This technology will allow to overcome the rate limitation due to the gated operation of the Multi-Wire Proportional Chambers (MWPCs) used in RUN1 (2009-2013) and RUN2 (2015-2018).
As part of the Upgrade project, long-term irradiation tests, so called "ageing tests", have been carried out. A test setup with a detector using a quadruple stack of 10x10cm2 GEMs was built and operated in Ar-CO2 and Ne-CO2-N2 gas mixtures. The detector performance such as gas gain and energy resolution were monitored continuously. In addition, outgassing tests of materials used for the assembly process of the upgraded TPC were performed. To reach the expected dose of the GEM-based TPC, the detector was operated at much higher gains than the TPC. It was found, that the GEMs could keep their performance within the projected lifetime of the TPC. Most of the tested materials showed no negative impact on the detector. For the tested epoxy adhesive no certain conclusion could be drawn.
At much higher doses than expected for the upgraded TPC, a new phenomenon was observed, which changed the hole geometry of the GEMs and led to a degradation of the energy resolution. Even though its occurrence is not expected during the lifetime of the GEM-based TPC, simulations were carried out to study this effect more systematically. The simulations confirmed, that a change of the hole geometries of the GEMs, lead to an increase of the local gain variation, which results in a decrease of the energy resolution.
Furthermore the effect of methane as quench gas on GEMs was studied, even though this gas is not foreseen to be used in the TPC. From ageing tests with single-wire proportional counters it is well known that hydrocarbons are produced in the plasma of the avalanches, which cover the electrodes and lead to a degradation of the detector performance. Even though GEMs have a quite different geometry, the ageing tests showed, that also this technology tends to methane-induced ageing. A loss of gas gain as well as a degradation of the energy resolution due to deposits on the electrodes was monitored. A qualitative and quantitative comparison between ageing in GEMs and proportional counters was performed.
As an integral part of ALICE, the dedicated heavy ion experiment at CERN’s Large Hadron Collider, the Transition Radiation Detector (TRD) contributes to the experiment’s tracking, triggering and particle identification. Central element in the TRD’s processing chain is its trigger and readout processor, the Global Tracking Unit (GTU). The GTU implements fast triggers on various signatures, which rely on the reconstruction of up to 20 000 particle track segments to global tracks, and performs the buffering and processing of event raw data as part of a complex detector readout tree.
The high data rates the system has to handle and its dual use as trigger and readout processor with shared resources and interwoven processing paths require the GTU to be a unique, high-performance parallel processing system. To achieve high data taking efficiency, all elements of the GTU are optimized for high running stability and low dead time.
The solutions presented in this thesis for the handling of readout data in the GTU, from the initial reception to the final assembly and transmission to the High-Level Trigger computer farm, address all these aspects. The presented concepts employ multi-event buffering, in-stream data processing, extensive embedded diagnostics, and advanced features of modern FPGAs to build a robust high-performance system that can conduct the high- bandwidth readout of the TRD with maximum stability and minimized dead time. The work summarized here not only includes the complete process from the conceptual layout of the multi-event data handling and segment control, but also its implementation, simulation, verification, operation and commissioning. It also covers the system upgrade for the second data taking period and presents an analysis of the actual system performance.
The presented design of the GTU’s input stage, which is comprised of 90 FPGA-based nodes, is built to support multi-event buffering for the data received from the 18 TRD supermodules on 1080 optical links at the full sender aggregate net bandwidth of 2.16 Tbit/s. With careful design of the control logic and the overall data path, the readout on the 18 concentrator nodes of the supermodule stage can utilize an effective aggregate output bandwidth of initially 3.33 GiB/s, and, after the successful readout bandwidth upgrade, 6.50 GiB/s via 18 optical links. The high possible readout link utilization of more than 99 % and the intermediate buffering of events on the GTU helps to keep the dead time associated with the local event building and readout typically below 10%. The GTU has been used for production data taking since start-up of the experiment and ever since performs the event buffering, local event building and readout for the TRD in a correct, efficient and highly dependable fashion.