Astronomical and cosmological observations support the existence of invisible matter that can only be detected through its gravitational effects, thus making it very difficult to study. Dark matter ...makes up about 27% of the known universe. As a matter of fact, one of the main goals of the physics program of the experiments at the Large Hadron Collider of the CERN laboratory is the search of new particles that can explain dark matter. This review discusses both experimental and theoretical aspects of searches for Weakly Interacting Massive Particle candidates for dark matter at the LHC. An updated overview of the various experimental search channels performed by the ATLAS experiment is presented in order to pinpoint complementarity among different types of LHC searches and the interplay between the LHC and direct and indirect dark matter searches.
The Level-0 muon trigger system of the ATLAS experiment will undergo a full upgrade for the High Luminosity LHC to stand the challenging requirements imposed by the increase in instantaneous ...luminosity. The upgraded trigger system will send raw hit data to off-detector processors, where trigger algorithms run on a new generation of FPGAs. To exploit the flexibility provided by the FPGA systems, ATLAS is developing novel precision deep neural network architectures based on trained ternary quantisation, optimised to run on FPGAs for efficient reconstruction and identification of muons in the ATLAS “Level-0” trigger. Physics performance in terms of efficiency and fake rates and FPGA logic resource occupancy and timing obtained with the developed algorithms are discussed.
Resource utilization plays a crucial role for successful implementation of fast real-time inference for deep neural networks (DNNs) and convolutional neural networks (CNNs) on latest generation of ...hardware accelerators (FPGAs, SoCs, ACAPs, GPUs). To fulfil the needs of the triggers that are in development for the upgraded LHC detectors, we have developed a multi-stage compression approach based on conventional compression strategies (pruning and quantization) to reduce the memory footprint of the model and knowledge transfer techniques, crucial to streamline the DNNs simplifying the synthesis phase in the FPGA firmware and improving explainability. We present the developed methodologies and the results of the implementation in a working engineering pipeline used as pre-processing stage to high level synthesis tools (HLS4ML, Xilinx Vivado HLS, etc.). We show how it is possible to build ultra-light deep neural networks in practice, by applying the method to a realistic HEP use-case: a toy simulation of one of the triggers planned for the HL-LHC.
We investigate the possibility to apply quantum machine learning techniques for data analysis, with particular regard to an interesting use-case in high-energy physics. We propose an anomaly ...detection algorithm based on a parametrized quantum circuit. This algorithm was trained on a classical computer and tested with simulations as well as on real quantum hardware. Tests on NISQ devices were performed with IBM quantum computers. For the execution on quantum hardware, specific hardware-driven adaptations were devised and implemented. The quantum anomaly detection algorithm was able to detect simple anomalies such as different characters in handwritten digits as well as more complex structures such as anomalous patterns in the particle detectors produced by the decay products of long-lived particles produced at a collider experiment. For the high-energy physics application, the performance was estimated in simulation only, as the quantum circuit was not simple enough to be executed on the available quantum hardware platform. This work demonstrates that it is possible to perform anomaly detection with quantum algorithms; however, as an amplitude encoding of classical data is required for the task, due to the noise level in the available quantum hardware platform, the current implementation cannot outperform classic anomaly detection algorithms based on deep neural networks.
Several scenarios beyond the Standard Model predict long-lived particles resulting in a wide variety of detector signatures depending on the nature of the particles and the decay lengths. Signals ...from long-lived particles are investigated by the ATLAS and CMS experiments exploiting different signatures, ranging from abnormal energy losses, to appearing or disappearing tracks, displaced vertices, lepton-jet signatures, long time-of-flight or late calorimetric energy deposits. This contribution summarizes the most recent results of the searches performed at the Large Hadron Collider (LHC) with the ATLAS and CMS detectors during the Run-1 data taking campaign. No evidence of any new physics is observed so far in any analysis, and the results are used to set stringent constraint on supersymmetric or hidden sector models.
Efficient and accurate reconstruction and identification of tau lepton decays plays a crucial role in the program of measurements and searches under the study for the future high-energy particle ...colliders. Leveraging recent advances in machine learning algorithms, which have dramatically improved the state of the art in visual object recognition, we have developed novel tau identification methods that are able to classify tau decays in leptons and hadrons and to discriminate them against QCD jets. We present the methodology and the results of the application at the interesting use case of the IDEA dual-readout calorimeter detector concept proposed for the future FCC-ee electron–positron collider.
Graphs are versatile structures for the representation of many real-world data. Deep Learning on graphs is currently able to solve a wide range of problems with excellent results. However, both the ...generation of graphs and the handling of large graphs still remain open challenges. This work aims to introduce techniques for generating large graphs and test the approach on a complex problem such as the calculation of dose distribution in oncological radiotherapy applications. To this end, we introduced a pooling technique (ReNN-Pool) capable of sampling nodes that are spatially uniform without computational requirements in both model training and inference. By construction, the ReNN-Pool also allows the definition of a symmetric un-pooling operation to recover the original dimensionality of the graphs. We also present a Variational AutoEncoder (VAE) for generating graphs, based on the defined pooling and un-pooling operations, which employs convolutional graph layers in both encoding and decoding phases. The performance of the model was tested on both the realistic use case of a cylindrical graph dataset for a radiotherapy application and the standard benchmark dataset sprite. Compared to other graph pooling techniques, ReNN-Pool proved to improve both performance and computational requirements.
Abstract
Experimental particle physics demands a sophisticated trigger and acquisition system capable to efficiently retain the collisions of interest for further investigation. Heterogeneous ...computing with the employment of FPGA cards may emerge as a trending technology for the triggering strategy of the upcoming high-luminosity program of the Large Hadron Collider at CERN In this context, we present two machine-learning algorithms for selecting events where neutral long-lived particles decay within the detector volume studying their accuracy and inference time when accelerated on commercially available Xilinx FPGA accelerator cards. The inference time is also confronted with a CPU- and GPU-based hardware setup. The proposed new algorithms are proven efficient for the considered benchmark physics scenario and their accuracy is found to not degrade when accelerated on the FPGA cards. The results indicate that all tested architectures fit within the latency requirements of a second-level trigger farm and that exploiting accelerator technologies for real-time processing of particle-physics collisions is a promising research field that deserves additional investigations, in particular with machine-learning models with a large number of trainable parameters.
The decoding of error syndromes of surface codes with classical algorithms may slow down quantum computation. To overcome this problem it is possible to implement decoding algorithms based on ...artificial neural networks. This work reports a study of decoders based on convolutional neural networks, tested on different code distances and noise models. The results show that decoders based on convolutional neural networks have good performance and can adapt to different noise models. Moreover, explainable machine learning techniques have been applied to the neural network of the decoder to better understand the behaviour and errors of the algorithm, in order to produce a more robust and performing algorithm.
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