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In this paper, we present the results of charged particle track reconstruction in CLAS12 using artificial intelligence. In our approach, we use neural networks working together to identify tracks based on the raw signals in the Drift Chambers. A Convolutional Auto-Encoder is used to de-noise raw data by removing the hits that do not satisfy the patterns for tracks, and second Multi-Layer Perceptron is used to identify tracks from combinations of clusters in the drift chambers. Our method increases the tracking efficiency by 50% for multi-particle final states already conducted experiments. The de-noising results indicate that future experiments can run at higher luminosity without degradation of the data quality. This in turn will lead to significant benefits for the CLAS12 physics program. [ABSTRACT FROM AUTHOR]

The Jiangmen Underground Neutrino Observatory (JUNO) experiment is set to begin data taking in 2024 with the aim of determining the neutrino mass ordering (NMO) to a significance of 3 σ after 6 years of data taking. Achieving this goal requires effective background suppression, with the background induced by cosmic-ray muons being one of the most significant sources of interference in the NMO study. Accurately simulating the cosmic-ray muon background is crucial for the success of the experiment, but the sheer number of optical photons produced by the muon makes this detector simulation process extremely time-consuming using traditional methods such as Geant4. This paper presents a fast muon simulation method that employs neural networks to expedite the simulation process. Our approach achieves an order-of-magnitude speed-up in simulation time compared to Geant4, while still producing accurate results. [ABSTRACT FROM AUTHOR]

The operating conditions defining the current data taking campaign at the Large Hadron Collider, known as Run 3, present unparalleled challenges for the real-time data acquisition workflow of the LHCb experiment at CERN. To address the anticipated surge in luminosity and consequent event rate, the LHCb experiment is transitioning to a fully software-based trigger system. This evolution necessitated innovations in hardware configurations, software paradigms, and algorithmic design. A significant advancement is the integration of monotonic Lipschitz Neural Networks into the LHCb trigger system. These deep learning models offer certified robustness against detector instabilities, and the ability to encode domain-specific inductive biases. Such properties are crucial for the inclusive heavy-flavour triggers and, most notably, for the topological triggers designed to inclusively select b-hadron candidates by exploiting the unique kinematic and decay topologies of beauty decays. This paper describes the recent progress in integrating Lipschitz Neural Networks into the topological triggers, highlighting the resulting enhanced sensitivity to highly displaced multi-body candidates produced within the LHCb acceptance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The findable, accessible, interoperable, and reusable (FAIR) data principles serve as a framework for examining, evaluating, and improving data sharing to advance scientific endeavors. There is an emerging trend to adapt these principles for machine learning models—algorithms that learn from data without specific coding—and, more generally, AI models, due to AI's swiftly growing impact on scientific and engineering sectors. In this paper, we propose a practical definition of the FAIR principles for AI models and provide a template program for their adoption. We exemplify this strategy with an implementation from high-energy physics, where a graph neural network is employed to detect Higgs bosons decaying into two bottom quarks. [ABSTRACT FROM AUTHOR]

Discontinuous Finite Element Methods (DFEM) have been widely used for solving SN radiation transport problems in participative and non-participative media. In this method, small matrix-vector systems are assembled and solved for each cell, angle, energy group, and time step while sweeping through the computational mesh. In practice, these systems are generally solved directly using Gaussian elimination, as computational acceleration for solving this small systems are often inadequate. Nonetheless, the computational cost of assembling and solving these local systems, repeated for each cell in the phase-space, can amount to a large fraction of the total computing time. In this paper, a Machine Learning algorithm is designed to accelerate the solution of local systems. This one is based on Artificial Neural Networks (ANNs). Its key idea is training an ANN with a large set of solutions to random one-cell transport problems and, then, replacing the assembling and solution of the local systems by the feedforward evaluation of the trained ANN. It is observed that the optimized ANNs are able to reduce the compute times by a factor of ~ 3:6 per source iteration, while introducing mean absolute errors between 0:5 – 2% in transport solutions. [ABSTRACT FROM AUTHOR]

Motor vehicle collisions are a common cause of deaths or/and injuries. The key to lowering the death rate and damages to the health of collision accident victims is a timely arrival of Emergency Services to the accident scene. In the paper, we present and discuss the first results of the design and implementation of the vehicles collision detection system, which is based on a motion detector (MD) and Artificial Neural Network (ANN). To test MD and ANN separately, a small set of video records from traffic cameras that was not a part of a training dataset, were used. We found that while MD demonstrates reasonable performance, Haar Cascades-based pre-trained ANN requires significant improvements. Possible solutions to the aforementioned problem were proposed and discussed. [ABSTRACT FROM AUTHOR]

In this paper, we will discuss a formal link between neural networks and quantum computing. For that purpose we will present a simple model for the description of the neural network by forming sub-graphs of the whole network with the same or a similar state. We will describe the interaction between these areas by closed loops, the feedback loops. The change of the graph is given by the deformations of the loops. This fact can be mathematically formalized by the fundamental group of the graph. Furthermore the neuron has two basic states |0〉 (ground state) and |1〉 (excited state). The whole state of an area of neurons is the linear combination of the two basic state with complex coefficients representing the signals (with 3 Parameters: amplitude, frequency and phase) along the neurons. If something changed in this area, we need a transformation which will preserve this general form of a state (mathematically, this transformation must be an element of the group S L(2; C)). The same argumentation must be true for the feedback loops, i.e. a general transformation of states along the feedback loops is an assignment of this loop to an element of the transformation group. Then it can be shown that the set of all signals forms a manifold (character variety) and all properties of the network must be encoded in this manifold. In the paper, we will discuss how to interpret learning and intuition in this model. Using the Morgan-Shalen compactification, the limit for signals with large amplitude can be analyzed by using quasi-Fuchsian groups as represented by dessins d'enfants (graphs to analyze Riemannian surfaces). As shown by Planat and collaborators, these dessins d'enfants are a direct bridge to (topological) quantum computing with permutation groups. The normalization of the signal reduces to the group S U(2) and the whole model to a quantum network. Then we have a direct connection to quantum circuits. This network can be transformed into operations on tensor networks. Formally we will obtain a link between machine learning and Quantum computing. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the possibility of applying various approaches to solving the problem of medium-term forecasting of daily passenger traffic volumes in the Moscow metro (MM): 1) on the basis of artificial neural networks (ANN); 2) using the singular-spectral analysis implemented in the package “Caterpillar”-SSA; 3) sharing the ANN and the “Caterpillar”-SSA approach. We demonstrate that the developed methods and algorithms allow us to conduct medium-term forecasting of passenger traffic in the MM with reasonable accuracy. [ABSTRACT FROM AUTHOR]

Computer paralinguistic analysis is widely used in security systems, biometric research, call centers and banks. Paralinguistic models estimate different physical properties of voice, such as pitch, intensity, formants and harmonics to classify emotions. The main goal is to find such features that would be robust to outliers and will retain variety of human voice properties at the same time. Moreover, the model used must be able to estimate features on a time scale for an effective analysis of voice variability. In this paper a paralinguistic model based on Bidirectional Long Short-Term Memory (BLSTM) neural network is described, which was trained for vocal-based emotion recognition. The main advantage of this network architecture is that each module of the network consists of several interconnected layers, providing the ability to recognize flexible long-term dependencies in data, which is important in context of vocal analysis. We explain the architecture of a bidirectional neural network model, its main advantages over regular neural networks and compare experimental results of BLSTM network with other models. [ABSTRACT FROM AUTHOR]

The research conducted in this paper is in the field of machine learning. The main object of the research is the learning process of an artificial neural network in order to increase its efficiency. The algorithm based on the analysis of retrospective learning data. The dynamics of changes in the values of the weights of an artificial neural network during training is an important indicator of training efficiency. The algorithm proposed in this work is based on changing the weight gradients values. Changing of the gradients weights makes it possible to understand how actively the network weights change during training. This knowledge helps to diagnose the training process and makes an adjusting the training parameters. The results of the algorithm can be used to train an artificial neural network. The network will help to determine the set of measures (actions) needed to optimize the learning process by the algorithm results. [ABSTRACT FROM AUTHOR]

This paper presents an overview of NARMER-1, the new generation of photon point-kernel code developed by the Reactor Studies and Applied Mathematics Unit (SERMA) at CEA Saclay Center. After a short introduction giving some history points and the current context of development of the code, the paper exposes the principles implemented in the calculation, the physical quantities computed and surveys the generic features: programming language, computer platforms, geometry package, sources description, etc. Moreover, specific and recent features are also detailed: exclusion sphere, tetrahedral meshes, parallel operations. Then some points about verification and validation are presented. Finally we present some tools that can help the user for operations like visualization and pre-treatment. [ABSTRACT FROM AUTHOR]

A revolution is underway in which deep neural networks are routinely used to solve dificult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such methods might be used to automate certain aspects of data analysis in particle physics. Next, the connection to Bayesian methods is discussed and the paper ends with thoughts on a significant practical issue, namely, how, from a Bayesian perspective, one might optimize the construction of deep neural networks. [ABSTRACT FROM AUTHOR]

High-energy physics computing is a typical data-intensive calculation. Each year, petabytes of data needs to be analyzed, and data access performance is increasingly demanding. The tiered storage system scheme for building a unified namespace has been widely adopted. Generally, data is stored on storage devices with different performances and different prices according to different access frequency. When the heat of the data changes, the data is then migrated to the appropriate storage tier. At present, heuristic algorithms based on artificial experience are widely used in data heat prediction. Due to the differences in computing models of different users, the accuracy of prediction is low. A method for predicting future access popularity based on file access characteristics with the help of LSTM deep learning algorithm is proposed as the basis for data migration in hierarchical storage. This paper uses the real data of high-energy physics experiment LHAASO as an example for comparative testing. The results show that under the same test conditions, the model has higher prediction accuracy and stronger applicability than existing prediction models. [ABSTRACT FROM AUTHOR]

For the High Luminosity LHC, the CMS collaboration made the ambitious choice of a high granularity design to replace the existing endcap calorimeters. Thousands of particles coming from the multiple interactions create showers in the calorimeters, depositing energy simultaneously in adjacent cells. The data are similar to 3D gray-scale image that should be properly reconstructed. In this paper, we investigate how to localize and identify the thousands of showers in such events with a Deep Neural Network model. This problem is well-known in the "Vision" domain, it belongs to the challenging class: "Object Detection". Our project shares a lot of similarities with the ones treated in Industry but faces several technological challenges like the 3D treatment. We present the Mask R-CNN model which has already proven its efficiency in Industry (for 2D images). We also present the first results and our plans to extend it to tackle 3D HGCAL data. [ABSTRACT FROM AUTHOR]

This paper proposes a method for reconstructing a depth map obtained using a stereo pair image. The proposed approach is based on a geometric model for the synthesis of patches. The entire image is preliminarily divided into blocks of different size, where large blocks are used to restore homogeneous areas, and small blocks are used to restore details of the image structure. Lost pixels are recovered by copying the pixel values from the source based on the similarity criterion. We used a trained neural network to select the "best like" patch. Experimental results show that the proposed method gives better results than other modern methods, both in subjective and objective measurements for reconstructing a depth map. [ABSTRACT FROM AUTHOR]

This paper presents an artificial neural network method for long-term prediction of the thermal dynamic parameters of the IBR-2M reactor. Attention is focused mainly on the prediction of the temperature and sodium flow at the entry into the core as well as the thermal power. It is shown that the prediction makes it possible to reduce by a factor of 3 the influence of slow fluctuations of reactivity on the power and thereby reduce the operational requirements for the automatic power stabilization system. [ABSTRACT FROM AUTHOR]

The paper demonstrates the advantages of the deep learning networks over the ordinary neural networks on their comparative applications to image classifying. An autoassociative neural network is used as a standalone autoencoder for prior extraction of the most informative features of the input data for neural networks to be compared further as classifiers. The main efforts to deal with deep learning networks are spent for a quite painstaking work of optimizing the structures of those networks and their components, as activation functions, weights, as well as the procedures of minimizing their loss function to improve their performances and speed up their learning time. It is also shown that the deep autoencoders develop the remarkable ability for denoising images after being specially trained. Convolutional Neural Networks are also used to solve a quite actual problem of protein genetics on the example of the durum wheat classification. Results of our comparative study demonstrate the undoubted advantage of the deep networks, as well as the denoising power of the autoencoders. In our work we use both GPU and cloud services to speed up the calculations. [ABSTRACT FROM AUTHOR]

With the emergence of the research field of Quantum Machine Learning, interest in finding advantageous real-world applications is growing as well. However, challenges concerning the number of available qubits on Noisy Intermediate Scale Quantum (NISQ) devices and accuracy losses due to hardware imperfections still remain and limit the applicability of such approaches in real-world scenarios. Therefore, for simplification, most studies assume nearly noise-free conditions as they are expected with logical, i.e. error-corrected, qubits instead of real qubits provided by hardware. However, the number of logical qubits is expected to scale slowly as they require a high number of real qubits for error correction. This is our motivation to deal with noise as an unavoidable, non-negligible problem on NISQ devices. As an application, we use the example of particle decay tree reconstruction as a highly complex combinatoric problem in High Energy Physics. We investigate methods to reduce the noise impact of such devices and propose a hybrid architecture that extends a classical graph neural network by a parameterized quantum circuit. While we have shown that such a hybrid architecture enables a reduction of the amount of trainable parameters compared to the fully classical case, we are now specifically interested in the actual performance in more realistic, i.e. noise prone scenarios. Using simple synthetic Decay Trees, we train the network in classical simulations to allow for efficient optimization of the parameters. The trained parameters are validated in noisy simulations based on devices by "IBM Quantum" and are used in interpretability and significance studies, enabling improvements in the accuracy on real devices. [ABSTRACT FROM AUTHOR]

An increasingly frequent challenge faced in HEP data analysis is to characterize the agreement between a prediction that depends on a dozen or more model parameters—such as predictions coming from an effective field theory (EFT) framework—and the observed data. Traditionally, such characterizations take the form of a negative log likelihood (NLL) function, which can only be evaluated numerically. The lack of a closed-form description of the NLL function makes it difficult to convey results of the statistical analysis. Typical results are limited to extracting "best fit" values of the model parameters and 1D intervals or 2D contours extracted from scanning the higher dimensional parameter space. It is desirable to explore these high-dimensional model parameter spaces in more sophisticated ways. One option for overcoming this challenge is to use a neural network to approximate the NLL function. This approach has the advantage of being continuous and differentiable by construction, which are essential properties for an NLL function and may also provide useful handles in exploring the NLL as a function of the model parameters. In this talk, we describe the advantages and limitations of this approach in the context of applying it to a CMS data analysis using the framework of EFT. [ABSTRACT FROM AUTHOR]

The evolution of modern heterogeneous accelerators, such as GPUs, has significantly advanced the landscape of artificial intelligence (AI). There is a notable surge to adopt AI within the nuclear physics domain (AI4NP). While most AI4NP studies focus on feasibility analysis, our attention is directed towards evaluating their performance on contemporary GPUs that integrate tensor cores. We first benchmark the throughput of hyperparameterized multi-layer perceptron (MLP) models. We then examine the performance of an AI4NP application: Hydra. We assess the performance gain and accuracy loss caused by the tensor cores for low-precision floating-point operations. Our experiments encompass the PyTorch and TensorFlow Keras frameworks on NVIDIA's T4 and A100 GPUs. We explore the behavior of different GPU hardware platforms and AI software tools. This study can be a valuable resource for guiding the performance optimization of larger-scale deployments of AI4NP applications. [ABSTRACT FROM AUTHOR]

RED-SEA (https://redsea-project.eu/) is a European project funded in the framework of the H2020-JTI-EuroHPC-2019-1 call that started in April 2021. The goal of the project is to evaluate the architectural design of the main elements of the interconnection networks for the next generation of HPC systems supporting hundreds of thousands of computing nodes enabling the Exascale for HPC, HPDA and AI applications while providing preliminary prototypes. The main technological feature is the BXI network, originally designed and produced by ATOS (France). The plan is to integrate in the next release of the network – BXI3 – the architectural solutions and novel IPs developed within the framework of the RED-SEA project. The consortium is composed of 11 well-established research teams across Europe, with extensive experience in interconnects, including network design, deployment and evaluation. Within RED-SEA, INFN is adopting a hardware/software co-design approach to design APEnetX, a scalable interconnect prototyped on latest generation Xilinx FPGAs, adding innovative components for the improvement of the performance and resiliency of the interconnect. APEnetX is an FPGA-based, PCIe Gen3/4 network interface card equipped with RDMA capabilities being the endpoint of a direct multidimensional toroidal network and suitable for integration in the BXI environment. APEnetX design will be benchmarked on project testbeds using real scientific applications like NEST, a spiking neural network simulator. [ABSTRACT FROM AUTHOR]

Recent work has demonstrated that graph neural networks (GNNs) trained for charged particle tracking can match the performance of traditional algorithms while improving scalability to prepare for the High Luminosity LHC experiment. Most approaches are based on the edge classification (EC) paradigm, wherein tracker hits are connected by edges, and a GNN is trained to prune edges, resulting in a collection of connected components representing tracks. These connected components are usually collected by a clustering algorithm and the resulting hit clusters are passed to downstream modules that may assess track quality or fit track parameters. In this work, we consider an alternative approach based on object condensation (OC), a multi-objective learning framework designed to cluster points belonging to an arbitrary number of objects, in this context tracks, and regress the properties of each object. We demonstrate that OC shows very promising results when applied to the pixel detector of the trackML dataset and can, in some cases, recover tracks that are not reconstructable when relying on the output of an EC alone. The results have been obtained with a modular and extensible open-source implementation that allows us to efficiently train and evaluate the performance of various OC architectures and related approaches. [ABSTRACT FROM AUTHOR]

We are studying the use of deep neural networks (DNNs) to identify and locate primary vertices (PVs) in proton-proton collisions at the LHC. Earlier work focused on finding primary vertices in simulated LHCb data using a hybrid approach that started with kernel density estimators (KDEs) derived heuristically from the ensemble of charged track parameters and predicted "target histogram" proxies, from which the actual PV positions are extracted. We have recently demonstrated that using a UNet architecture performs indistinguishably from a "flat" convolutional neural network model. We have developed an "end-to-end" tracks-to-hist DNN that predicts target histograms directly from track parameters using simulated LHCb data that provides better performance (a lower false positive rate for the same high efficiency) than the best KDE-tohists model studied. This DNN also provides better efficiency than the default heuristic algorithm for the same low false positive rate. "Quantization" of this model, using FP16 rather than FP32 arithmetic, degrades its performance minimally. Reducing the number of UNet channels degrades performance more substantially. We have demonstrated that the KDE-to-hists algorithm developed for LHCb data can be adapted to ATLAS and ACTS data using two variations of the UNet architecture. Within ATLAS/ACTS, these algorithms have been validated against the standard vertex finder algorithm. Both variations produce PVfinding efficiencies similar to that of the standard algorithm and vertex-vertex separation resolutions that are significantly better. [ABSTRACT FROM AUTHOR]

The continuous growth in model complexity in high-energy physics (HEP) collider experiments demands increasingly time-consuming model fits. We show first results on the application of conditional invertible networks (cINNs) to this challenge. Specifically, we construct and train a cINN to learn the mapping from signal strength modifiers to observables and its inverse. The resulting network infers the posterior distribution of the signal strength modifiers rapidly and for low computational cost. We present performance indicators of such a setup including the treatment of systematic uncertainties. Additionally, we highlight the features of cINNs estimating the signal strength for a vector boson associated Higgs production analysis of simulated samples of events, which include a simulation of the CMS detector. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm for aerosol typing from multiwavelength lidar data, based on Artificial Neural Networks. The aerosol model used to simulate optical properties for the training of the network is described. The algorithm is tested on real observations from ESA-CALIPSO database. [ABSTRACT FROM AUTHOR]

This paper presents an artificial neural network method for long-term prediction of liquid sodium flow rate through the core of the IBR-2M reactor. The nonlinear autoregressive neural network (NAR) with local feedback connection has been considered as the most appropriate tool for such a prediction. The predicted results were compared with experimental values. NAR model predicts slow changes of liquid sodium flow rate up to two days with an error less than 5%. [ABSTRACT FROM AUTHOR]

Initial studies have suggested generative adversarial networks (GANs) have promise as fast simulations within HEP. These studies, while promising, have been insufficiently precise and also, like GANs in general, suffer from stability issues.We apply GANs to to generate full particle physics events (not individual physics objects), explore conditioning of generated events based on physics theory parameters and evaluate the precision and generalization of the produced datasets. We apply this to SUSY mass parameter interpolation and pileup generation. We also discuss recent developments in convergence and representations that match the structure of the detector better than images.In addition we describe on-going work making use of large-scale distributed resources on the Cori supercomputer at NERSC, and developments to control distributed training via interactive jupyter notebook sessions. This will allow tackling high-resolution detector data; model selection and hyper-parameter tuning in a productive yet scalable deep learning environment. [ABSTRACT FROM AUTHOR]

The crucial role played by the underlying symmetries of high energy physics and lattice field theories calls for the implementation of such symmetries in the neural network architectures that are applied to the physical system under consideration. In these proceedings, we focus on the consequences of incorporating translational equivariance among the network properties, particularly in terms of performance and generalization. The benefits of equivariant networks are exemplified by studying a complex scalar field theory, on which various regression and classification tasks are examined. For a meaningful comparison, promising equivariant and non-equivariant architectures are identified by means of a systematic search. The results indicate that in most of the tasks our best equivariant architectures can perform and generalize significantly better than their non-equivariant counterparts, which applies not only to physical parameters beyond those represented in the training set, but also to different lattice sizes. [ABSTRACT FROM AUTHOR]

In this study artificial neural networks (ANN) are used to simulate the monotonic and cyclic behaviour of sands observed in laboratory tests on Karlsruhe sand under drained and undrained conditions. A genetic algorithm (GA) is used to obtain an optimal framework for the ANN. The results show that the proposed genetic adaptive neural network (GANN) can effectively simulate drained and undrained monotonic triaxial behaviour of saturated sand under isotropic or anisotropic consolidation. The GANN is also able to predict satisfactorily the cyclic behaviour of sands under undrained triaxial test with strain and stress cycles. In addition, GANN is able to distinguish between monotonic drained and undrained conditions by delivering a good prediction when trained with the combined database. [ABSTRACT FROM AUTHOR]

A new concept using deep learning in neural networks is investigated to characterize the underlying uncertainty of nuclear data. Analysis is performed on multi-group neutron cross-sections (56 energy groups) for the GODIVA U-235 sphere. A deep model is trained with cross-validation using 1000 nuclear data random samples to fit 336 nuclear data parameters. Although of the very limited sample size (1000 samples) available in this study, the trained models demonstrate promising performance, where a prediction error of about 166 pcm is found for keff in the test set. In addition, the deep model's sensitivity and uncertainty are validated. The comparison of importance ranking of the principal fast fission energy groups with adjoint methods shows fair agreement, while a very good agreement is observed when comparing the global keff uncertainty with sampling methods. The findings of this work shall motivate additional efforts on using machine learning to unravel complexities in nuclear data research. [ABSTRACT FROM AUTHOR]

An important area of research required for fusion reactor design is the study of materials under high energy neutron irradiation. Deuterium-Tritium (D-T) reactions release 14.1 MeV neutrons and material studies of such high energy neutrons focusing on transmutation and activation are paramount for fusion tokamak devices such as ITER and DEMO. In order to understand neutron damage and transmutation-induced radioactivity in fusion regime energies, a series of experimental campaigns were performed at the ASP facility based at Aldermaston in the UK, which uses a deuteron accelerator to bombard a tritiumloaded target and generate 14 MeV-neutron emission rates of up to 2.5 × 1011 s−1. In this work, a holistic treatment of the 11,000 gamma spectra (time series data) collected over five experimental campaigns is applied to identify radioisotopes and validate nuclear data and the inventory code, FISPACT-II. Whilst previous analysis has examined single spectra and foil irradiation's using traditional, human-driven methods, this work applies novel methods using Artificial Neural Networks (ANN) and classification algorithms to allow a fully automated approach. Using such methods we show good broad agreement with FISPACT-II inventory simulations, and an overview of results are given as C/E values. [ABSTRACT FROM AUTHOR]

In this work, we evaluate the impact of simulating the thermal-hydraulics feedback in criticality risk assessment in a benchmark configuration corresponding to a simplified debris bed. We have recently discussed the coupling of the multi-phase thermal-hydraulics code MC3D with a reactivity evaluation scheme based on multi-point kinetics, which paves the way towards the assessment of the system behavior in terms of energy release. In this work, we analyze the system reactivity as a function of the thermal-hydraulics state of the system. For our investigation, we have considered a simplified cylindrical region and assumed that the fuel particles in the mixture consist of spheres of small diameter. The power has been adjusted to be representative of nuclear decay heat. For most of the examined configurations, the maximum reactivity lies below zero, which shows that the occurrence of a criticality event is likely to be excluded, thanks to the contribution of the decay heat of the fuel. [ABSTRACT FROM AUTHOR]

A new event data format has been designed and prototyped by the CMS collaboration to satisfy the needs of a large fraction of physics analyses (at least 50%) with a per event size of order 1 kB. This new format is more than a factor of 20 smaller than the MINIAOD format and contains only top level information typically used in the last steps of the analysis. The talk will review the current analysis strategy from the point of view of event format in CMS (both skims and formats such as RECO, AOD, MINIAOD, NANOAOD) and will describe the design guidelines for the new NANOAOD format. [ABSTRACT FROM AUTHOR]

B-decay data from the Belle experiment at the KEKB collider have a substantial background from e+e--h> qq¯ events. To suppress this we employ deep neural network algorithms. These provide improved signal from background discrimination. However, the deep neural network develops a substantial correlation with the ∆E kinematic variable used to distinguish signal from background in the final fit due to its relationship with input variables. The effect of this correlation is reduced by deploying an adversarial neural network. Over-all the adversarial deep neural network performs better than a Boosted Decision Tree algorithimn and a commercial package, NeuroBayes, which employs a neural net with a single hidden layer. [ABSTRACT FROM AUTHOR]

The application of deep learning techniques using convolutional neural networks for the classification of particle collisions in High Energy Physics is explored. An intuitive approach to transform physical variables, like momenta of particles and jets, into a single image that captures the relevant information, is proposed. The idea is tested using a well-known deep learning framework on a simulation dataset, including leptonic ttbar events and the corresponding background at 7 TeV from the CMS experiment at LHC, available as Open Data. This initial test shows competitive results when compared to more classical approaches, like those using feedforward neural networks. [ABSTRACT FROM AUTHOR]

In the two-phase flow measurements a method involving the absorption of gamma radiation can be applied among others. Analysis of the signals from the scintillation probes can be used to determine the number of flow parameters and to recognize flow structure. Three types of flow regimes as plug, bubble, and transitional plug - bubble flows were considered in this work. The article shows how features of the signals in the time and frequency domain can be used to build the artificial neural network (ANN) to recognize the structure of the gas-liquid flow in a horizontal pipeline. In order to reduce the number of signal features the principal component analysis (PCA) was used. It was found that the reduction of signals features allows for building a network with better performance. [ABSTRACT FROM AUTHOR]

The Tunka Radio Extension (Tunka-Rex) is a digital antenna array, which measures radio emission of the cosmic-ray air-showers in the frequency band of 30-80 MHz. Tunka-Rex is co-located with the TAIGA experiment in Siberia and consists of 63 antennas, 57 of them are in a densely instrumented area of about 1 km2. In the present workwe discuss the improvements of the signal reconstruction applied for Tunka-Rex. At the first stage we implemented matched filtering using averaged signals as template. The simulation study has shown that matched filtering allows one to decrease the threshold of signal detection and increase its purity. However, the maximum performanceof matched filtering is achievable only in case of white noise, while in reality the noise is not fully random due to different reasons. To recognize hidden features of the noise and treat them, we decided to use convolutional neural network with autoencoder architecture. Taking the recorded trace as an input, the autoencoder returns denoised traces, i.e. removes all signal-unrelated amplitudes. We present the comparison between the standard method of signal reconstruction, matched filtering and the autoencoder, and discuss the prospects of application of neural networks for lowering the threshold of digital antenna arrays for cosmic-ray detection. [ABSTRACT FROM AUTHOR]

To tackle thorax movement from CT images, we have developed a platform to simulate a customized breathing cycle, where the pulmonary movement has been considered only at the rough border of the whole lung by artificial neural networks (ANN). The goal of this work is to include additional information of the lung lobe. Thus, more ANN will be used and future simulation will be able to take into consideration the impact of tumor on lobe movement. We present a new automatic segmentation algorithm that enables the extraction of lobar contour data using sliding mask and direction estimation. These improvements enhance the overall system performance in which higher precision and more accurate treatments can be expected. [ABSTRACT FROM AUTHOR]

The Relativistic Mean Field (RMF) model with a small number of adjusted parameters is powerful tool for correct predictions of various ground-state nuclear properties of nuclei. Its success for describing nuclear properties of nuclei is directly related with adjustment of its parameters by using experimental data. In the present study, the Artificial Neural Network (ANN) method which mimics brain functionality has been employed for improvement of the RMF model parameters. In particular, the understanding capability of the ANN method for relations between the RMF model parameters and their predictions for binding energies (BEs) of 58Ni and 208Pb have been found in agreement with the literature values. [ABSTRACT FROM AUTHOR]

In the proposed work we aim at modelling building lighting energy consumption. We compared several classical methods to the latest Artificial Intelligence modelling technique : Artificial Neural Networks Ensembling (ANNE). Therefore, in this study we show how we built the ANNE and a new hybrid model based on the statistical-ANNE combination. Experimentation has been carried out over a three months data set coming from a real office building located in the ENEA 'Casaccia' Research Centre. Experimental results show that the proposed hybrid statistical-ANNE approach can get a remarkable improvement with respect to the best classical method (the statistical one). [ABSTRACT FROM AUTHOR]