Robert Bainbridge

Particles beyond the Standard Model (SM) can generically have lifetimes that are long compared to SM particles at the weak scale. When produced at experiments such as the Large Hadron Collider (LHC) at CERN, these long-lived particles (LLPs) can decay far from the interaction vertex of the primary proton-proton collision. Such LLP signatures are distinct from those of promptly decaying particles that are targeted by the majority of searches for new physics at the LHC, often requiring customized techniques to identify, for example, significantly displaced decay vertices, tracks with atypical properties, and short track segments. Given their non-standard nature, a comprehensive overview of LLP signatures at the LHC is beneficial to ensure that possible avenues of the discovery of new physics are not overlooked. Here we report on the joint work of a community of theorists and experimentalists with the ATLAS, CMS, and LHCb experiments --- as well as those working on dedicated experiments such as MoEDAL, milliQan, MATHUSLA, CODEX-b, and FASER --- to survey the current state of LLP searches at the LHC, and to chart a path for the development of LLP searches into the future, both in the upcoming Run 3 and at the High-Luminosity LHC. The work is organized around the current and future potential capabilities of LHC experiments to generally discover new LLPs, and takes a signature-based approach to surveying classes of models that give rise to LLPs rather than emphasizing any particular theory motivation. We develop a set of simplified models; assess the coverage of current searches; document known, often unexpected backgrounds; explore the capabilities of proposed detector upgrades; provide recommendations for the presentation of search results; and look towards the newest frontiers, namely high-multiplicity "dark showers", highlighting opportunities for expanding the LHC reach for these signals.

There are ongoing efforts to investigate theories that aim to explain the current shortcomings of the Standard Model of particle physics. One such effort is the Long-Lived Particle Jet Tagging Algorithm, based on a DNN (Deep Neural Network), which is used to search for exotic new particles. This paper describes two novel optimisations in the design of this DNN, suitable for implementation on an FPGA-based accelerator. The first involves the adoption of cyclic random access memories and the reuse of multiply-accumulate operations. The second involves storing matrices distributed over many RAM memories with elements grouped by index. An evaluation of the proposed methods and hardware architectures is also included. The proposed optimisations can yield performance enhancements by more than an order of magnitude compared to software implementations. The innovations can also lead to smaller FPGA footprints and accordingly reduce power consumption, allowing for instance duplication of compute units to achieve increases in effective throughput.

The front end driver (FED), is a 9U 400 mm VME64x card designed for reading out the compact muon solenoid (CMS), silicon tracker signals transmitted by the APV25 analogue pipeline application specific integrated circuits. The FED receives the signals via 96 optical fibers at a total input rate of 3.4 GB/sec. The signals are digitized and processed by applying algorithms for pedestal and common mode noise subtraction. Algorithms that search for clusters of hits are used to further reduce the input rate. Only the cluster data along with trigger information of the event are transmitted to the CMS data acquisition system using the S-LINK64 protocol at a maximum rate of 400 MB/sec. All data processing algorithms on the FED are executed in large on-board field programmable gate arrays. Results on the design, performance, testing and quality control of the FED are presented and discussed

The CMS experiment has recorded a high-purity sample of 10 billion unbiased b hadron decays. The CMS trigger and data acquisition systems were configured to deliver a custom data stream at an average throughput of 2 GB s −1 , which was “parked” prior to reconstruction. The data stream was defined by level-1 and high level trigger algorithms that operated at peak trigger rates in excess of 50 and 5 kHz, respectively. New algorithms have been developed to reconstruct and identify electrons with high efficiency at transverse momenta as low as 0.5 GeV. The trigger strategy and electron reconstruction performance were validated with pilot processing campaigns. The accumulation and reconstruction of this data set, now complete, were delivered without significant impact on the core physics programme of CMS. This unprecedented sample provides a unique opportunity for physics analyses in the flavour sector and beyond.

The APV25 is the 128 channel CMOS chip developed for readout of the silicon microstrip tracker in the CMS experiment at the CERN Large Hadron Collider. The detector is now under construction and will be the largest silicon microstrip system ever built, with ∼200 m2 of silicon sensors. 75,000 chips are required to instrument the system, which must operate for 10 years in a high radiation environment with little or no possibility of replacement of any component. The readout chip is a crucial components, which must provide low noise and reliable operation. Thus, each readout chip must be carefully tested prior to installation in CMS modules and assurance of long-term performance of the readout electronics, especially verification of radiation tolerance, is highly desirable. This has been achieved by means of automated probe testing of every chip on the silicon wafers from the foundry, followed by studies of sample die to evaluate in more detail properties of the chips, which cannot easily be examined at the wafer level. During production, it was observed that the yield of good die varied unexpectedly from one production lot to another. This was investigated with significant help from the manufacturer and the process was optimised to ensure consistent high yield. A fraction of the dies, which successfully passed the wafer screening, are subjected to short-term X-ray irradiation to levels equivalent to that expected in CMS and are then annealed. Results are presented here and illustrate the excellent performance of APV25 under all expected operating conditions.

The CMS silicon strip tracker, providing a sensitive area of approximately 200 m2 and comprising 10 million readout channels, has recently been completed at the tracker integration facility at CERN. The strip tracker community is currently working to develop and integrate the online and offline software frameworks, known as XDAQ and CMSSW respectively, for the purposes of data acquisition and detector commissioning and monitoring. Recent developments have seen the integration of many new services and tools within the online data acquisition system, such as event building, online distributed analysis, an online monitoring framework, and data storage management. We review the various software components that comprise the strip tracker data acquisition system, the software architectures used for stand-alone and global data-taking modes. Our experiences in commissioning and operating one of the largest ever silicon micro-strip tracking systems are also reviewed.

The APV25 is the front end readout chip for the CMS silicon microstrip tracker. Approximately 75,000 chips are required and the production phase is now complete. Each chip on every wafer is subjected to detailed probe testing to verify full functionality and performance, and only chips that pass all tests are selected for mounting on detector modules. Over several years more than 500 wafers have been tested and results for all chips have been archived. An analysis of the database allows significant comparisons between chips, wafers and wafer production batches, giving a complete and final picture of the spread in yield and performance experienced in this large scale manufacturing task.

The CMS Silicon Tracker Front-End Driver (FED) is a 9U 400mm VME64x card which processes the raw data generated within the Silicon Tracker by the APV25 readout ASICs. The processed, zero-suppressed, data is then sent to the Data Acquisition System (DAQ). The first 2 FEDs were made at the beginning of 2003 and since then a further 15 FEDs of this type (FEDv1) have been manufactured. All hardware modifications to the FEDv1 design have now been completed and a new iteration of the board produced, called the FEDv2, which is expected to be the final version. The firmware and software development is close to completion. The performance of a FED in the laboratory is presented.

The front end driver is a 9U 400mm VME64x card designed for reading out the CMS silicon tracker signals transmitted by the APV25 analogue pipeline ASICs. The FED receives the signals via 96 optical fibers at a total input rate of 3.4 GBytes/sec. The signals are digitized and processed by applying algorithms for pedestal and common mode noise subtraction. Algorithms that search for clusters of hits are used to further reduce the input rate. Only the cluster data along with trigger information of the event are transmitted to the CMS DAQ system using the S-LINK64 protocol at a maximum rate of 400 Mbytes/sec. All data processing algorithms on the FED are executed in large on-board FPGAs. Results on the design, performance, testing and quality control of the FED are presented and discussed.

A bstract The determination of the infection fatality rate (IFR) for the novel SARS-CoV-2 coronavirus is a key aim for many of the field studies that are currently being undertaken in response to the pandemic. The IFR together with the basic reproduction number R 0 , are the main epidemic parameters describing severity and transmissibility of the virus, respectively. The IFR can be also used as a basis for estimating and monitoring the number of infected individuals in a population, which may be subsequently used to inform policy decisions relating to public health interventions and lockdown strategies. The interpretation of IFR measurements requires the calculation of confidence intervals. We present a number of statistical methods that are relevant in this context and develop an inverse problem formulation to determine correction factors to mitigate time-dependent effects that can lead to biased IFR estimates. We also review a number of methods to combine IFR estimates from multiple independent studies, provide example calculations throughout this note and conclude with a summary and “best practice” recommendations. The developed code is available online.

The Compact Muon Solenoid (CMS) experiment is one of four large high energy physics experiments presently being constructed for operation at the Large Hadron Collider (LHC) facility at CERN, Geneva. The motivation for the LHC and its experiments is the large range of new physics expected at the TeV energy scale. The CMS Silicon Strip Tracker (SST) will play a major role in all physics searches, providing precision tracking within a hostile radiation environment. The SST readout system is based on the APV25 front-end chip, of which 73000 are needed to fully instrument the sub-detector. It is essential for the SST readout system to be of the highest quality in order to maximise the physics performance of the sub-detector. Therefore, a production test station has been developed to screen wafers, each containing several hundred APV25 chips, prior to the integration of individual die into the final readout system. Several hundred wafers have already been screened and the number of chips identified to be fully functional and exhibit excellent performance corresponds to 70% of the SST requirement. Analysis results are presented. Inelastic nuclear collisions of hadrons incident on silicon sensors frequently generate highly ionising particles that can deposit as much energy within the sensor bulk as several hundred minimum ionising particles. These large signals can saturate the APV25 front-end chip, resulting in deadtime and introducing inefficiencies into the readout system. Analyses of beam test data that quantify the effect are presented. A change of a front-end component is shown to significantly reduce the induced inefficiencies, which are predicted to be at the sub-percent level for the final SST readout system. Subsequent Monte-Carlo studies have shown that the induced inefficiencies will have negligible effect on the tracking performance and b-tagging efficiency of the SST sub-detector.

The Front-End Driver (FED) is a 9U 400mm VME64x card designed for reading out the CMS silicon tracker. The FED was designed to maximise the number of channels that could be processed on a single 9U board and has a mixture of optical, analogue (96 ADC channels) and digital, Field Programmable Gate Array (FPGA), components. Nevertheless, a total of 440 FED boards are required to readout the entire tracker. Nearly 20 full-scale prototype 9U FED boards have been produced to date. This paper concentrates on the issues of the large-scale manufacture and assembly of PCBs. It also discusses the issues of production testing of such large and complex electronic cards.

CMS has a two level trigger system. The first stage is hardware based and provides fast trigger decisions up to a rate 100 kHz. The second stage, known as the High-Level Trigger, is entirely software based and required to provide a trigger decision within 40 ms and a rejection factor of a thousand to achieve a write-to-disk rate of 100 Hz. One of the most CPU-intensive tasks within the High-Level Trigger is the reconstruction of tracking hits using raw data from the strip tracker. This study profiles the performance of these reconstruction algorithms. Even at low luminosities, the average processing time is 5.5 s, which already exceeds the HLT budget. A new schema, optimised for speed and performance, has been developed to reconstruct hits within regions-of-interest only. For the entire sub-detector, hit reconstruction times are reduced to 140 ms. Since only 10 % of High-Level Trigger events are expected to require track reconstruction, the average contribution per event is then ∼14 ms i.e. 30 % of the full budget. Regional reconstruction is tested over Z0 → e+e− events, by unpacking in η-ϕ windows of 0.16 × 0.16 around seeds identified in the calorimeter. In this case, only 2 ± 1 % of the silicon strip tracker raw data is reconstructed in 5 ± 3 ms (or an average contribution per event of 0.5 ms) whilst maintaining 99 % of the original dielectron trigger efficiency.

The data acquisition system for the CMS Silicon Strip Tracker (SST) is based around a custom analogue front-end ASIC, an analogue optical link system and an off-detector VME board that performs digitization, zero-suppression and data formatting. A complex procedure is required to optimally configure, calibrate and synchronize the 10 channels of the SST readout system. We present an overview of this procedure, which will be used to commission and calibrate the SST during the integration, Start-Up and operational phases of the experiment. Recent experiences from the CMS Magnet Test Cosmic Challenge and system tests at the Tracker Integration Facility are also reported. I. THE DATA ACQUISITION SYSTEM The CMS Silicon Strip Tracker (SST) is unprecedented in terms of its size and complexity, providing a sensitive area of >200 m and comprising 10 readout channels. Fig. 1 shows a schematic of the control and readout systems for the SST. The control system [1] comprises 300 “control rings” that start and end at the off-detector Front-End Controller (FEC) boards and is responsible for distributing slow control commands, clock and Level-1 triggers to the front-end electronics. The signals are transmitted optically from the FECs to front-end digital optohybrids via digital links, and then electrically via ‘token rings” of Communication and Control Units (CCUs) to the front-end electronics. The readout system is based around a custom front-end ASIC known as the APV25 chip [2], an analogue optical link system [3] and an off-detector Front-End Driver (FED) processing board [4]. The system comprises 76k APV25 chips, 38k optical fibres (each transmitting data from a pair of APV25 chips) and 440 FEDs. The APV25 chip samples, amplifies, buffers and processes signals from 128 channels of a silicon strip sensor at the LHC collision frequency of 40MHz. On receipt of a Level-1 trigger, pulse height and bunch-crossing information from pairs of APV25 chips are multiplexed onto a single line and the data are converted to optical signals that are transmitted via analogue fibres to the off-detector FED boards. The FEDs digitize, zerosuppress and format the pulse height data from up to 96 pairs of APV25 chips, before forwarding the resulting event fragments to the CMS event builder (EVB) and online computing farm. Figure 1: The SST control system uses ∼300 control rings (based around the FEC and CCU boards) to propagate clock, trigger and slow control information to the front-end. The SST readout system is based around the APV25 chip, an analogue optical link system and the off-

Searches for supersymmetric models with a compressed mass spectrum are presented using data samples collected at a centre-of-mass energy of 8 TeV with the CMS detector at the LHC and corresponding to an integrated luminosity of 19.4 19.7 fb 1 . This class of model is challenging to detect experimentally, primarily due to low-momentum SM particles arising from decay chains involving SUSY particles of comparable mass. Various searches that are optimised for models involving the production of coloured sparticles or electroweakinos and a compressed mass spectrum are presented.

The design, development, and operation of a liquid hydrogen plant with an hourly output of 100 litres of normal liquid hydrogen or 70 litres of 85–90% para-hydrogen are described. The liquid hydrogen produced was used for testing a rocket thrust chamber developed by Rolls Royce and for tank pressurization studies on behalf of ELDO. In a period of six months over 40 000 litres of liquid hydrogen were produced. The performance of a Linde cycle is briefly examined and the major design concepts required to ensure a safe and reliable production facility are discussed.

The CMS Silicon Strip Tracker at the LHC comprises a sensitive area of approximately 200 m2 and 10 million readout channels. Its data acquisition system is based around a custom analogue front-end chip. Both the control and the readout of the front-end electronics are performed by off-detector VME boards in the counting room, which digitise the raw event data and perform zero-suppression and formatting. The data acquisition system uses the CMS online software framework to configure, control and monitor the hardware components and steer the data acquisition. The first data analysis is performed online within the official CMS reconstruction framework, which provides many services, such as distributed analysis, access to geometry and conditions data, and a Data Quality Monitoring tool based on the online physics reconstruction.

The Tracker detector took data with cosmics rays at the Tracker Integration Facility (TIF) at CERN. First on-line monitoring tasks were executed at the Tracker Analysis Centre (TAC) which is a dedicated Control Room at TIF with limited computing resources. A set of software agents were developed to perform the real-time data conversion in a standard format, to archive data on tape at CERN and to publish them in the official CMS data bookkeeping systems. According to the CMS computing and analysis model, most of the subsequent data processing has to be done in remote Tier-1 and Tier-2 sites, so data were automatically transferred from CERN to the sites interested to analyze them, currently Fermilab, Bari and Pisa. Official reconstruction in the distributed environment was triggered in real-time by using the tool currently used for the processing of simulated events. Automatic end-user analysis of data was performed in a distributed environment, in order to derive the distributions of important physics variables. The tracker data processing is currently migrating to the Tier-0 CERN as a prototype for the global data taking chain. Tracker data were also registered into the most recent version of the data bookkeeping system, DBS-2, by profiting from the new features to handle real data. A description of the dataflow/workflow and of the tools developed is given, together with the results about the performance of the real-time chain. Almost 7.2 million events were officially registered, moved, reconstructed and analyzed in remote sites by using the distributed environment.

The determination of the infection fatality rate (IFR) for the novel SARS-CoV-2 coronavirus is a key aim for many of the field studies that are currently being undertaken in response to the pandemic. The IFR together with the basic reproduction number $R_0$, are the main epidemic parameters describing severity and transmissibility of the virus, respectively. The IFR can be also used as a basis for estimating and monitoring the number of infected individuals in a population, which may be subsequently used to inform policy decisions relating to public health interventions and lockdown strategies. The interpretation of IFR measurements requires the calculation of confidence intervals. We present a number of statistical methods that are relevant in this context and develop an inverse problem formulation to determine correction factors to mitigate time-dependent effects that can lead to biased IFR estimates. We also review a number of methods to combine IFR estimates from multiple independent studies, provide example calculations throughout this note and conclude with a summary and best practice recommendations. The developed code is available online.

The determination of the infection fatality rate (IFR) for the novel SARS-CoV-2 coronavirus is a key aim for many of the field studies that are currently being undertaken in response to the pandemic. The IFR together with the basic reproduction number $R_0$, are the main epidemic parameters describing severity and transmissibility of the virus, respectively. The IFR can be also used as a basis for estimating and monitoring the number of infected individuals in a population, which may be subsequently used to inform policy decisions relating to public health interventions and lockdown strategies. The interpretation of IFR measurements requires the calculation of confidence intervals. We present a number of statistical methods that are relevant in this context and develop an inverse problem formulation to determine correction factors to mitigate time-dependent effects that can lead to biased IFR estimates. We also review a number of methods to combine IFR estimates from multiple independent studies, provide example calculations throughout this note and conclude with a summary and "best practice" recommendations. The developed code is available online.

The CMS Silicon Tracker comprises a complicated set of hardware and software components that have been thoroughly tested at CERN before final integration of the Tracker. A vertical slice of the full readout chain has been operated under near-final conditions. In the absence of the tracker front-end modules, simulated events have been created within the FED (Front End Driver) and used to test the readout reliability and efficiency of the final DAQ (Data Acquisition). The data are sent over the S-Link 64 bit links to the FRL (Fast Readout Link) modules at rates in excess of 200 MBytes/s per FED depending on setup and conditions. The current tracker DAQ is fully based on the CMS communication and acquisition tool XDAQ. This paper discusses setup and results of a vertical slice of the full Tracker final readout system comprising 2 full crates of FEDs, 30 in total, read out through 1 full crate of final FRL modules. This test is to complement previous tests done at Imperial College[3] taking them to the next level in order to prove that a complete crate of FRLs using the final DAQ system, including all subcomponents of the final system both software and hardware with the exception of the detector modules themselves, is capable of sustained readout at the desired rates and occupancy of the CMS Tracker. Simulated data are created with varying hit occupancy (1-10%) and Poisson distributed trigger rates (<200KHz) and the resulting behaviour of the system is recorded. Data illustrating the performance of the system and data readout are presented.

Inelastic nuclear interactions in silicon sensors can produce highly ionising particles and result in signals equivalent of up to ~1000 minimum ionising particles. These rare events have been observed to cause measurable deadtime in all 128 channels of the CMS Tracker APV25 front-end readout chip. Analysis of beam test data, laboratory measurements and simulation studies have provided measurements of both the rate at which non-negligible deadtime occurs and the most probable deadtime resulting from a highly ionising event. Laboratory measurements also predict that through a suitable choice of a front-end hybrid resistor, the deadtime may be reduced. The inefficiency due to highly ionising events is predicted to be at the sub-percent level in CMS. I. HIGHLY IONISING EVENTS AND THE DEADTIME

Equine Veterinary EducationVolume 3, Issue 4 p. 241-242 BEVA Congress - Cambridge R. Bainbridge, Search for more papers by this author R. Bainbridge, Search for more papers by this author First published: December 1991 https://doi.org/10.1111/j.2042-3292.1991.tb01534.xAboutPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume3, Issue4December 1991Pages 241-242 RelatedInformation