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    Chromaticity measurement in physics conditions with BTF and ADT-AC dipole (MD13463)

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    The result of an experiment demonstrating the readiness of the tools put in place to measure chromaticity through beam transfer function measurement on a single bunch while the full physics beam is circulating is presented. The resolution of the technique is about 1 to 2 units and could be further improved by considering a low damper gain on the witness bunches. Chromaticity measurement based on the measurement of intra-bunch motion driven by the ADT-AC dipole were attempted but without success

    ATLAS and CMS non-resonant HH searches in the 2b2γ final state

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    ATLAS and CMS non-resonant HH searches in the 2b2γ final stat

    12th International Conference of the Balkan Physical Union - BPU12 Congress

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    The 12 BPU Congress will be held in Bucharest, Romania, from July 8 to July 12, 2025. It will be hosted by the National University of Science and Technology POLITEHNICA Bucharest

    HHH Workshop

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    Anomalies and Precision in the Belle II Era Workshop

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    Jet finding in real-time using an object detection CNN

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    The ATLAS trigger system will undergo a comprehensive upgrade in advance of the HL-LHC programme. In order to deal with the increased data bandwidth trigger algorithms will be required to satisfy stricter latency requirements. We propose a method to speed up the current calorimeter-only preselection step and to aid trigger decisions for hadronic signals containing jets. We demonstrate the use of a dedicated object-detection Convolutional Neural Network (CNN) for jet finding in the ATLAS calorimeter. The modified computer vision model is employed in the task of jet detection to identify and localise jets within the central calorimeter acceptance and to subsequently estimate their transverse momenta. A custom architecture is introduced to reduce the number of learnable parameters required for improved inference speed. The model performance is evaluated on a set of simulated particle interactions in the ATLAS detector with up to 200 concurrent pile-up interactions

    AMD AI-Engines in fixed latency environments

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    The ATLAS Level-0 Global Trigger is a mission critical system opting to take advantage of the full calorimeter granularity during Run-4 and beyond. Level-0 Global will be executing a cascade of trigger algorithms combined both the calorimeter information and the muons. Within the Next Generation Trigger (NGT) project at CERN there is a dedicated work package (WP2.1) exploring large deployment of Machine Learning based algorithms to further enhance selections within the Global system. Given the tight latency and throughput conditions that Global is operating at, any solution developed at WP2.1 has to be deployed in custom hardware solution using FPGAs. The cutting edge FPGA technologies include within the same package dedicated co-processing chiplets optimised for Machine Learning applications. Such a device is the Adaptive Interface Engines provided in the Versal Premium packages. In this talk we will present the work performed within the scope of the NGT WP2.1 aiming to characterise the performance of those devices, provide some generic implementations for specific ML models and explore the feasibility of deploying them in the harsh environment of a mission critical system (L0-Global)

    The end of measurement and the last lab

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    Measurements in fundamental physics are becoming increasingly difficult. In particular, many high-precision measurements are now dealing with the intrinsic quantum mechanical noise of the detectors themselves. LIGO is an example: it is limited by Heisenberg uncertainty in the laser light. However, this quantum noise can in principle be engineered away by clever use of quantum resources. I will discuss these issues through some examples (cosmic neutrino detection, gravitational wave detection, and tests of quantum gravity), and offer some speculations on the practical and perhaps fundamental limitations on quantum engineering for measurements of this type.</p

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