A
bstract
We present results from a state-of-the-art fit of electroweak precision observables and Higgs-boson signal-strength measurements performed using 7 and 8 TeV data from the Large Hadron ...Collider. Based on the HEPfit package, our study updates the traditional fit of electroweak precision observables and extends it to include Higgs-boson measurements. As a result we obtain constraints on new physics corrections to both electroweak observables and Higgs-boson couplings. We present the projected accuracy of the fit taking into account the expected sensitivities at future colliders.
We assess the impact of the very recent measurement of the top-quark mass by the CMS Collaboration on the fit of electroweak data in the standard model and beyond, with particular emphasis on the ...prediction for the mass of the W boson. We then compare this prediction with the average of the corresponding experimental measurements including the new measurement by the CDF Collaboration, and discuss its compatibility in the standard model, in new physics models with oblique corrections, and in the dimension-six standard model effective field theory. Finally, we present the updated global fit to electroweak precision data in these models.
HEPfit is a flexible open-source tool which, given the Standard Model or any of its extensions, allows to (i) fit the model parameters to a given set of experimental observables; (ii) obtain ...predictions for observables. HEPfit can be used either in Monte Carlo mode, to perform a Bayesian Markov Chain Monte Carlo analysis of a given model, or as a library, to obtain predictions of observables for a given point in the parameter space of the model, allowing HEPfit to be used in any statistical framework. In the present version, around a thousand observables have been implemented in the Standard Model and in several new physics scenarios. In this paper, we describe the general structure of the code as well as models and observables implemented in the current release.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Recent results at the Large Hadron Collider (LHC) have pointed to enhanced physics capabilities through the improvement of the real-time event processing techniques. Machine learning methods are ...ubiquitous and have proven to be very powerful in LHC physics, and particle physics as a whole. However, exploration of the use of such techniques in low-latency, low-power FPGA (Field Programmable Gate Array) hardware has only just begun. FPGA-based trigger and data acquisition systems have extremely low, sub-microsecond latency requirements that are unique to particle physics. We present a case study for neural network inference in FPGAs focusing on a classifier for jet substructure which would enable, among many other physics scenarios, searches for new dark sector particles and novel measurements of the Higgs boson. While we focus on a specific example, the lessons are far-reaching. A companion compiler package for this work is developed based on High-Level Synthesis (HLS) called hls4ml to build machine learning models in FPGAs. The use of HLS increases accessibility across a broad user community and allows for a drastic decrease in firmware development time. We map out FPGA resource usage and latency versus neural network hyperparameters to identify the problems in particle physics that would benefit from performing neural network inference with FPGAs. For our example jet substructure model, we fit well within the available resources of modern FPGAs with a latency on the scale of 100 ns.
Overall, light weighting strategies are mainly analysed in the aim of reducing impact during the use phase of a vehicle. In this paper environmental and economic assessments are combined to evaluate ...the sustainability of adopting an innovative lightweight material for an automotive component. The analysis is carried out according to the Life Cycle Assessment and Life Cycle Costing methods. A standard solution, based on talc filler-reinforced composite, and an innovative one made with hollow glass micro-spheres as plastic reinforcement, are compared to be applied to a vehicle dashboard. The use of hollow glass micro-spheres has expanded during the last years in the automotive sector, however evaluations of their environmental and economic performances along its whole life cycle have not yet been discussed extensively. In this study particular attention is given to the following aspects: i) balance between the use phase benefit and material production phase; ii) End-of-Life scenarios; iii) analysis of additional indicators besides CO2 emissions; iv) data accuracy concerning manufacturing phase. Results show that hollow glass microspheres-reinforced composite is likely better from an environmental point of view for those impact categories where the use phase is more involved. The increase of material processing impact does not compromise benefits in terms of GWP and PED due to weight reduction, nevertheless it affects resource depletion and ecotoxicity indicators negatively. Overall the End-of-Life phase is not affected significantly. Moreover, despite a higher material cost, the innovative solution was found economically convenient as demonstrated also by the breakeven point (within the life distance).
•Environmental and economic life cycle assessments of an innovative automotive dashboard in lightweighting perspective.•Detailed data collection mostly based on primary data directly measured during the industrial process was done.•Talc filler-polypropylene reinforced and hollow glass micro-spheres polypropylene composite were compared.•Different End-of-Life scenarios were analysed.
We describe the implementation of Boosted Decision Trees in the hls4ml library, which allows the translation of a trained model into FPGA firmware through an automated conversion process. Thanks to ...its fully on-chip implementation, hls4ml performs inference of Boosted Decision Tree models with extremely low latency. With a typical latency less than 100 ns, this solution is suitable for FPGA-based real-time processing, such as in the Level-1 Trigger system of a collider experiment. These developments open up prospects for physicists to deploy BDTs in FPGAs for identifying the origin of jets, better reconstructing the energies of muons, and enabling better selection of rare signal processes.
.
At the Large Hadron Collider, the high-transverse-momentum events studied by experimental collaborations occur in coincidence with parasitic low-transverse-momentum collisions, usually referred to ...as pileup. Pileup mitigation is a key ingredient of the online and offline event reconstruction as pileup affects the reconstruction accuracy of many physics observables. We present a classifier based on Graph Neural Networks, trained to retain particles coming from high-transverse-momentum collisions, while rejecting those coming from pileup collisions. This model is designed as a refinement of the PUPPI algorithm (D. Bertolini
et al.
, JHEP
10
, 059 (2014)), employed in many LHC data analyses since 2015. Thanks to an extended basis of input information and the learning capabilities of the considered network architecture, we show an improvement in pileup-rejection performances with respect to state-of-the-art solutions.
•Use of motorcycle ABS was analysed in emergency braking test with an opponent car.•Some experienced rider underused the full power capacity of the ABS.•Underusing ABS at 50 km/h can result in an ...increase in braking distance of 6 m.•New user-adapted safety systems such as Emergency Brake Assist can be supportive.•New training methods and reinforcement of the braking skills are recommended.
This study aims to investigate whether motorcyclists are able to use the full potential of anti-lock braking systems (ABS) in demanding braking situations that maintain the natural coupling of action and perception of emergency events, or whether instead the lack of braking skills in riders makes ABS almost ineffective and comparable to non-ABS brakes on dry pavement. Six experienced riders performed two experimental tests. First test included 12 emergency braking trials in a realistic scenario using a mock-up of an intersection conflict with a car initiating a left turn manoeuvre across the path (LTAP) of a motorcycle approaching from the opposite direction as an unpredicted moving hazard. Second test included three trials in a planned self-timed hard braking. The speed at the onset of braking was 35–45 km/h. The braking performance was measured from the initiation of brake pressure until the full stop of the vehicle. Front wheel ABS usage was determined by the pressure in the master cylinder and wheel callipers. The testing resulted in 85 data runs with full stop braking manoeuvres.
Results revealed four categories of riders classified by their front wheel ABS usage during the emergency braking tests, which included two riders who underused front wheel ABS (9.6% and 27.4% of braking time on average). The worst case resulted in a significantly longer braking distance (braking deceleration of 5.2 m/s2). The highest skilled rider, who reached initial jerks close to 30 m/s3, used the ABS of the front wheel 93.7% of the braking time on average, resulting in a braking deceleration of 7.71 m/s2. Overall, the best braking performance was achieved in trials where the front ABS was activated for more than 80% of the braking. In planned self-timed hard braking test, where riders have more time to plan the braking manoeuvre, the experience rider with lowest performance during the emergency braking test improved braking efficiency and was able to increase ABS activation from 9.6% to 26.8% of the time, achieving a deceleration of 6.24 m/s2.
ABS is demonstrated to reduce stopping distances and to improve stability under all braking conditions, but such features are not enough to guarantee a good braking performance in emergency events if the riders have not the skills to utilize the full braking power of the motorcycle. Less skilled riders, even with ABS, may not have the confidence to increase braking power further when reaching high decelerations that push them to the limit of their stabilisation control in emergency braking, thus increasing braking distance with potentially life-threatening consequences. Our results suggest that many experience riders still need knowledge and skill to make the ABS work to its optimum in emergency events to avoid crashes. Further research with larger sample sizes including the full diversity of the motorcyclist population is recommended to determine the actual proportion of motorcyclists underusing ABS.
We apply an Adversarially Learned Anomaly Detection (ALAD) algorithm to the problem of detecting new physics processes in proton–proton collisions at the Large Hadron Collider. Anomaly detection ...based on ALAD matches performances reached by Variational Autoencoders, with a substantial improvement in some cases. Training the ALAD algorithm on 4.4 fb
-
1
of 8 TeV CMS Open Data, we show how a data-driven anomaly detection and characterization would work in real life, re-discovering the top quark by identifying the main features of the
t
t
¯
experimental signature at the LHC.
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