Leonardo Giannini

mkFit is an implementation of the Kalman filter-based track reconstruction algorithm that exploits both threadand data-level parallelism. In the past few years the project transitioned from the R&D phase to deployment in the Run-3 offline workflow of the CMS experiment. The CMS tracking performs a series of iterations, targeting reconstruction of tracks of increasing difficulty after removing hits associated to tracks found in previous iterations. mkFit has been adopted for several of the tracking iterations, which contribute to the majority of reconstructed tracks. When tested in the standard conditions for production jobs, speedups in track pattern recognition are on average of the order of 3.5x for the iterations where it is used (3-7x depending on the iteration). Multiple factors contribute to the observed speedups, including vectorization and a lightweight geometry description, as well as improved memory management and single precision. Efficient vectorization is achieved with both the icc and the gcc (default in CMSSW) compilers and relies on a dedicated library for small matrix operations, Matriplex, which has recently been released in a public repository. While the mkFit geometry description already featured levels of abstraction from the actual Phase-1 CMS tracker, several components of the implementations were still tied to that specific geometry. We have further generalized the geometry description and the configuration of the run-time parameters, in order to enable support for the Phase-2 upgraded tracker geometry for the HL-LHC and potentially other detector configurations. The implementation strategy and high-level code changes required for the HL-LHC geometry are presented. Speedups in track building from mkFit imply that track fitting becomes a comparably time consuming step of the tracking chain. Prospects for an mkFit implementation of the track fit are also discussed.

The upgrade of the track classification and selection step of the CMS tracking to a Deep Neural Network is presented. The CMS tracking follows an iterative approach: tracks are reconstructed in multiple passes starting from the ones that are easiest to find and moving to the ones with more complex characteristics (lower transverse momentum, high displacement). The track classification comes into play at the end of each iteration. A classifier using a multivariate analysis is applied after each iteration and several selection criteria are defined. If a track meets the high purity requirement, its hits are removed from the hit collection, thus simplifying the later iterations, and making the track classification an integral part of the reconstruction process. Tracks passing loose selections are also saved for physics analysis usage. The CMS experiment improved the track classification starting from a parametric selection used in Run 1, moving to a Boosted Decision Tree in Run 2, and finally to a Deep Neural Network in Run 3. An overview of the Deep Neural Network training and current performance is shown.

The growing interest towards hydrogen technologies and their implementation in the hydrocarbon and chemical process industry makes maintenance planning of storage and transport equipment an emerging safety aspect. With respect to high-pressure working equipment, Risk-Based Inspection methodology (RBI) aims at minimizing the risk of loss of containment due to materials’ deterioration mechanisms. This set of procedures focuses on the mechanical integrity of equipment to achieve crucial risk mitigation by means of risk-informed inspection planning and maintenance activities. In addition, hydrogen-induced damages are often generalized or even neglected by the existing RBI standards and recommended practices. On this basis, high-pressure vessels, process piping and storage tanks working in gaseous or liquid hydrogen environments, which are exposed to hydrogen-induced deterioration mechanisms, might be subjected to an inaccurate evaluation of the associated risk and hazards when these RBI standards are applied. For this reason, this work proposes a review of the pipelines steels commonly used for gaseous hydrogen transport to investigate the possible limitations of the standard RBI planning methodologies, when applied to hydrogen technologies. More accurately, the pipeline steels’ susceptibility to hydrogen-induced degradations mechanisms will be discussed to underline assumptions and hypothesis limiting the conventional RBI applicability. Therefore, the overall suitability of standard RBI planning with respect to hydrogen equipment is discussed, highlighting possible relevant gaps as a general result.

One of the most computationally challenging problems expected for the High-Luminosity Large Hadron Collider (HL-LHC) is determining the trajectory of charged particles during event reconstruction. Algorithms used at the LHC today rely on Kalman filtering, which builds physical trajectories incrementally while incorporating material effects and error estimation. Recognizing the need for faster computational throughput, we have adapted Kalman-filter-based methods for highly parallel, many-core SIMD architectures that are now prevalent in high-performance hardware. In this paper, we discuss the design and performance of the improved tracking algorithm, referred to as MKFIT. A key piece of the algorithm is the MATRIPLEX library, containing dedicated code to optimally vectorize operations on small matrices. The physics performance of the MKFIT algorithm is comparable to the nominal CMS tracking algorithm when reconstructing tracks from simulated proton-proton collisions within the CMS detector. We study the scaling of the algorithm as a function of the parallel resources utilized and find large speedups both from vectorization and multi-threading. MKFIT achieves a speedup of a factor of 6 compared to the nominal algorithm when run in a single-threaded application within the CMS software framework.

One of the most challenging computational problems in the Run 3 of the Large Hadron Collider (LHC) and more so in the High-Luminosity LHC (HL-LHC) is expected to be finding and fitting charged-particle tracks during event reconstruction. The methods used so far at the LHC and in particular at the CMS experiment are based on the Kalman filter technique. Such methods have shown to be robust and to provide good physics performance, both in the trigger and offline. In order to improve computational performance, we explored Kalman-filter-based methods for track finding and fitting, adapted for many-core SIMD architectures. This adapted Kalman-filter-based software, called "mkFit", was shown to provide a significant speedup compared to the traditional algorithm, thanks to its parallelized and vectorized implementation. The mkFit software was recently integrated into the offline CMS software framework, in view of its exploitation during the Run 3 of the LHC. At the start of the LHC Run 3, mkFit will be used for track finding in a subset of the CMS offline track reconstruction iterations, allowing for significant improvements over the existing framework in terms of computational performance, while retaining comparable physics performance. The performance of the CMS track reconstruction using mkFit at the start of the LHC Run 3 is presented, together with prospects of further improvement in the upcoming years of data taking.

Abstract Inspection planning, maintainability and safety aspects are yet to be consolidated topics of hydrogen technologies in most of their applications, including the marine sector. The implementation of electricity in the marine sector is almost only appealing for ferries, which in many cases have daily access to recharge stations. In addition, the climate roadmap of the Norwegian fishing fleet estimates that a low environmental impact technology can contribute to significantly reduce greenhouse emissions, especially carbon dioxide, by 2030. In fact, considering the longer working sessions of fishing vessels, the additional weight of batteries, and the considerable occupied volume, it is more than reasonable to discuss fuel cells as a possible solution. Against this background, this paper discusses a case study of a hydrogen-fueled fishing vessel, focusing on risk-based inspection (RBI) and maintenance planning as a way to significantly decrease safety-related uncertainties and optimize the associated operations. Different hydrogen-induced degradation mechanisms have been considered to investigate how the existing RBI standards might lead to an underestimation of the risks associated with the equipment selected for the fishing vessel. In addition, a discussion regarding the limitations in the applicability of standard RBI planning with respect to hydrogen technologies is carried out as an overall result, along with the limits of the implemented approach.

mkFit is an implementation of the Kalman filter-based track reconstruction algorithm that exploits both thread- and data-level parallelism. In the past few years the project transitioned from the R&D phase to deployment in the Run-3 offline workflow of the CMS experiment. The CMS tracking performs a series of iterations, targeting reconstruction of tracks of increasing difficulty after removing hits associated to tracks found in previous iterations. mkFit has been adopted for several of the tracking iterations, which contribute to the majority of reconstructed tracks. When tested in the standard conditions for production jobs, speedups in track pattern recognition are on average of the order of 3.5x for the iterations where it is used (3-7x depending on the iteration). Multiple factors contribute to the observed speedups, including vectorization and a lightweight geometry description, as well as improved memory management and single precision. Efficient vectorization is achieved with both the icc and the gcc (default in CMSSW) compilers and relies on a dedicated library for small matrix operations, Matriplex, which has recently been released in a public repository. While the mkFit geometry description already featured levels of abstraction from the actual Phase-1 CMS tracker, several components of the implementations were still tied to that specific geometry. We have further generalized the geometry description and the configuration of the run-time parameters, in order to enable support for the Phase-2 upgraded tracker geometry for the HL-LHC and potentially other detector configurations. The implementation strategy and high-level code changes required for the HL-LHC geometry are presented. Speedups in track building from mkFit imply that track fitting becomes a comparably time consuming step of the tracking chain.

Landscape capacity generally refers to the degree to which a landscape area is able to absorb change without significant effects on its character and perception. The aim of this study is to examine the changes caused by the construction of settlements and infrastructures by checking which patterns of landscape are affected and by what amount, through means of image analysis techniques integrated with GIS software. This type of landscape analysis allows us to create an objective image compounded by its basic elements, numerically identifying the colour ranges and geometries that constitute its structure, using the azimuth view (orthophoto) and views from the road as observation points as well as numerically comparing those values with all of the attributes that characterize a part of a territory (cultural, rarity, historical, scenic value, sound, etc.). In this way we can assess the level of impact caused by the construction of a settlement or an infrastructure by evaluating the degree of change introduced and comparing the pre-work to the post-work situation. This will give decision makers the possibility to:  have a more objective idea of a territory not influenced by cultural background and personal attributes;  assess with a methodology independent from any context;  rationalize the perceptual experience of the present day landscape;  univocally define the parameters that identify a territory; and  have a map of the territory’s vulnerability (the analysis regards its characters and visual aspects).

in porting existing serial algorithms to many-core devices. Measurements of both data processing and data transfer latency are shown, considering different I/O strategies to/from the parallel devices.

The most recent CMS Higgs boson (H) physics results, with the Higgs decaying to a bottom quark-antiquark pair ($\mathrm{b\overline{b}}$), are presented. The focus is on the analysis of data collected at $\sqrt{s}=13$ $\mathrm{TeV}$ in 2017, corresponding approximately to 41 $\mathrm{fb}^{-1}$. The analysis targeting the associated production of a Higgs boson and a vector boson ($\mathrm{VH}$), with $\mathrm{H\rightarrow b\overline{b}}$, yields an observed significance of 3.3 standard deviations ($\sigma$) above the background-only hypothesis, with an expected significance of 3.1$\sigma$. The corresponding measured signal strength is $\mu$ = 1.08 $\pm$ 0.34. When combined with previous $\mathrm{VH(b\overline{b})}$ measurements using data collected at $\sqrt{s}=$ 7, 8 and 13 $\mathrm{TeV}$, the observed (expected) significance is 4.8 (4.9) $\sigma$, corresponding to $\mu$ = 1.01 $\pm$ 0.22. Finally, the combination of this result with searches for $\mathrm{H\rightarrow b\overline{b}}$ in other production modes by the CMS experiment, including $\mathrm{t\overline{t}H(b\overline{b})}$ with 2016 data, results in a significance of 5.6 (5.5) $\sigma$, and $\mu=$ 1.04 $\pm$ 0.20. The analysis targeting the $\mathrm{t\overline{t}H(b\overline{b})}$ process using 2017 data is also covered. The significance for this production mode is of 3.7 (2.6) $\sigma$ , corresponding to $\mu$ = 1.49$\pm$0.42. In combination with the previous result using 2016 data, a significance of 3.9 (3.5) $\sigma$ is obtained, with $\mu$ = 1.15$\pm$0.30.