3D convolutional GAN for fast simulation

Machine Learning techniques have been used in different applications by the HEP community: in this talk, we discuss the case of detector simulation. The need for simulated events, expected in the future for LHC experiments and their High Luminosity upgrades, is increasing dramatically and requires new fast simulation solutions. We describe an R&D; activity aimed at providing a configurable tool capable of training a neural network to reproduce the detector response and speed-up standard Monte Carlo simulation. This represents a generic approach in the sense that such a network could be designed and trained to simulate any kind of detector and, eventually, the whole data processing chain in order to get, directly in one step, the final reconstructed quantities, in just a small fraction of time. We present the first application of three-dimensional convolutional Generative Adversarial Networks to the simulation of high granularity electromagnetic calorimeters. We describe detailed validation studies comparing our results to Geant4Monte Carlo simulation. Finally we show how this tool could be generalized to describe a whole class of calorimeters, opening the way to a generic machine learning based fast simulation approach

Designing an AI-Based Greenhouse Plant Monitoring System to Detect and Classify Plant Diseases from Leaf Images

Plant diseases can significantly hinder food crop production, leading to substantial economic losses and posing a threat to global food security. Machine learning, particularly deep learning, plays a crucial role in object detection and classification. In this study, we present an AI-based plant monitoring system for detecting and classifying plant diseases using visual images. Our deep learning models are trained on plant images obtained from natural environments. Manual detection and classification are both challenging and labor-intensive, making accurate and timely diagnoses from an automatic system highly beneficial for treating plant diseases. Traditionally, plant disease detection using deep learning has relied on images taken in controlled environments, which do not support in-situ detection for remote monitoring. The Plantdoc dataset, a popular resource consisting of plant images from actual field conditions, is used in our study. We employ the YOLOv5 algorithm from the field of computer vision to the Plantdoc dataset, achieving results that surpass previous work on the same dataset. This success is attributed to our selected model and data augmentation techniques. Our model can classify and detect various diseased and healthy leaf classes with a mean Average Precision (mAP) of 92%. This capability enables farmers and researchers to remotely monitor plant health and diagnose plant diseases, thereby saving time, reducing costs, and minimizing crop loss

Designing an AI-Based Greenhouse Plant Monitoring System to Detect and Classify Plant Diseases from Leaf Images

Plant diseases can significantly hinder food crop production, leading to substantial economic losses and posing a threat to global food security. Machine learning, particularly deep learning, plays a crucial role in object detection and classification. In this study, we present an AI-based plant monitoring system for detecting and classifying plant diseases using visual images. Our deep learning models are trained on plant images obtained from natural environments. Manual detection and classification are both challenging and labor-intensive, making accurate and timely diagnoses from an automatic system highly beneficial for treating plant diseases. Traditionally, plant disease detection using deep learning has relied on images taken in controlled environments, which do not support in-situ detection for remote monitoring. The Plantdoc dataset, a popular resource consisting of plant images from actual field conditions, is used in our study. We employ the YOLOv5 algorithm from the field of computer vision to the Plantdoc dataset, achieving results that surpass previous work on the same dataset. This success is attributed to our selected model and data augmentation techniques. Our model can classify and detect various diseased and healthy leaf classes with a mean Average Precision (mAP) of 92%. This capability enables farmers and researchers to remotely monitor plant health and diagnose plant diseases, thereby saving time, reducing costs, and minimizing crop loss

Fast simulation of electromagnetic particle showers in high granularity calorimeters

The future need of simulated events by the LHC experiments and their High Luminosity upgrades, is expected to increase by one or two orders of magnitude. As a consequence, research on new fast simulation solutions, including deep Generative Models, is very active and initial results look promising. We have previously reported on a prototype that we have developed, based on 3 dimensional convolutional Generative Adversarial Network, to simulate particle showers in high-granularity calorimeters. In this contribution we present improved results on a more realistic simulation. Detailed validation studies show very good agreement with Monte Carlo simulation. In particular, we show how increasing the network representational power, introducing physics-based constraints and using a transfer-learning approach for training improve the level of agreement over a large energy range

Fast simulation of electromagnetic particle showers in high granularity calorimeters

The future need of simulated events by the LHC experiments and their High Luminosity upgrades, is expected to increase by one or two orders of magnitude. As a consequence, research on new fast simulation solutions, including deep Generative Models, is very active and initial results look promising. We have previously reported on a prototype that we have developed, based on 3 dimensional convolutional Generative Adversarial Network, to simulate particle showers in high-granularity calorimeters. In this contribution we present improved results on a more realistic simulation. Detailed validation studies show very good agreement with Monte Carlo simulation. In particular, we show how increasing the network representational power, introducing physics-based constraints and using a transfer-learning approach for training improve the level of agreement over a large energy range

Evaluating Mixed-Precision Arithmetic for 3D Generative Adversarial Networks to Simulate High Energy Physics Detectors

Several hardware companies are proposing native Brain Float 16-bit (BF16) support for neural network training. The usage of Mixed Precision (MP) arithmetic with floating-point 32-bit (FP32) and 16-bit half-precision aims at improving memory and floating-point operations throughput, allowing faster training of bigger models. This paper proposes a binary analysis tool enabling the emulation of lower precision numerical formats in Neural Network implementation without the need for hardware support. This tool is used to analyze BF16 usage in the training phase of a 3D Generative Adversarial Network (3DGAN) simulating High Energy Physics detectors. The binary tool allows us to confirm that BF16 can provide results with similar accuracy as the full-precision 3DGAN version and the costly reference numerical simulation using double precision arithmetic

Evaluating mixed-precision arithmetic for 3D generative adversarial networks to simulate high energy physics detectors

Several hardware companies are proposing native Brain Float 16-bit (BF16) support for neural network training. The usage of Mixed Precision (MP) arithmetic with floating-point 32-bit (FP32) and 16-bit half-precision aims at improving memory and floating-point operations throughput, allowing faster training of bigger models. This paper proposes a binary analysis tool enabling the emulation of lower precision numerical formats in Neural Network implementation without the need for hardware support. This tool is used to analyze BF16 usage in the training phase of a 3D Generative Adversarial Network (3DGAN) simulating High Energy Physics detectors. The binary tool allows us to confirm that BF16 can provide results with similar accuracy as the full-precision 3DGAN version and the costly reference numerical simulation using double precision arithmetic.Marc Casas has been partially supported by the Spanish Ministry of Economy, Industry and Competitiveness under Ramon y Cajal fellowship number RYC-2017-23269. Adrià Armejach has been partially supported by the Spanish Ministry of Economy, Industry and Competitiveness under Juan de la Cierva postdoctoral fellowship number IJCI-2017-33945. John Osorio has been partially supported by the Spanish Government by FPI pre-doctoral scholarship number PRE2019- 090406 under project SEV-2015-0493-19-4. This work has been partially supported by Intel under the BSC-Intel collaboration.Peer ReviewedPostprint (author's final draft

Calorimetry with deep learning : particle simulation and reconstruction for collider physics

Using detailed simulations of calorimeter showers as training data, we investigate the use of deep learning algorithms for the simulation and reconstruction of single isolated particles produced in high-energy physics collisions. We train neural networks on single-particle shower data at the calorimeter-cell level, and show significant improvements for simulation and reconstruction when using these networks compared to methods which rely on currently-used state-of-the-art algorithms. We define two models: an end-to-end reconstruction network which performs simultaneous particle identification and energy regression of particles when given calorimeter shower data, and a generative network which can provide reasonable modeling of calorimeter showers for different particle types at specified angles and energies. We investigate the optimization of our models with hyperparameter scans. Furthermore, we demonstrate the applicability of the reconstruction model to shower inputs from other detector geometries, specifically ATLAS-like and CMS-like geometries. These networks can serve as fast and computationally light methods for particle shower simulation and reconstruction for current and future experiments at particle colliders.Peer reviewe

Generative Adversarial Networks for fast simulation

Deep Learning techniques are being studied for different applications by the HEP community: in this talk, we discuss the case of detector simulation. The need for simulated events, expected in the future for LHC experiments and their High Luminosity upgrades, is increasing dramatically and requires new fast simulation solutions. Here we present updated results on the development of 3DGAN, one of the first examples using three-dimensional convolutional Generative Adversarial Networks to simulate high granularity electromagnetic calorimeters. In particular, we report on two main aspects: results on the simulation of a more general, realistic physics use case and on data parallel strategies to distribute the training process across multiple nodes on public cloud resources