The Preliminary Results on Analysis of TAIGA-IACT Images Using
Convolutional Neural Networks
- URL: http://arxiv.org/abs/2112.10168v1
- Date: Sun, 19 Dec 2021 15:17:20 GMT
- Title: The Preliminary Results on Analysis of TAIGA-IACT Images Using
Convolutional Neural Networks
- Authors: Elizaveta Gres and and Alexander Kryukov
- Abstract summary: The aim of the work is to study the possibility of the machine learning application to solve the tasks set for TAIGA-IACT.
The method of Convolutional Neural Networks (CNN) was applied to process and analyze Monte-Carlo events simulated with CORSIKA.
- Score: 68.8204255655161
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The imaging Cherenkov telescopes TAIGA-IACT, located in the Tunka valley of
the republic Buryatia, accumulate a lot of data in a short period of time which
must be efficiently and quickly analyzed. One of the methods of such analysis
is the machine learning, which has proven its effectiveness in many
technological and scientific fields in recent years. The aim of the work is to
study the possibility of the machine learning application to solve the tasks
set for TAIGA-IACT: the identification of the primary particle of cosmic rays
and reconstruction their physical parameters. In the work the method of
Convolutional Neural Networks (CNN) was applied to process and analyze
Monte-Carlo events simulated with CORSIKA. Also various CNN architectures for
the processing were considered. It has been demonstrated that this method gives
good results in the determining the type of primary particles of Extensive Air
Shower (EAS) and the reconstruction of gamma-rays energy. The results are
significantly improved in the case of stereoscopic observations.
Related papers
- Physics-based reward driven image analysis in microscopy [5.581609660066545]
We present a methodology based on the concept of a Reward Function to optimize image analysis dynamically.
The Reward Function is engineered to closely align with the experimental objectives and broader context.
We extend the reward function approach towards the identification of partially-disordered regions, creating a physics-driven reward function and action space of high-dimensional clustering.
arXiv Detail & Related papers (2024-04-22T12:55:04Z) - Enhancing Rock Image Segmentation in Digital Rock Physics: A Fusion of
Generative AI and State-of-the-Art Neural Networks [5.089732183029123]
In digital rock physics, analysing microstructures from CT and SEM scans is crucial for estimating properties like porosity and pore connectivity.
Traditional segmentation methods like thresholding and CNNs often fall short in accurately detailing rock microstructures and are prone to noise.
U-Net improved segmentation accuracy but required many expert-annotated samples, a laborious and error-prone process due to complex pore shapes.
Our study employed an advanced generative AI model, the diffusion model, to overcome these limitations.
TransU-Net sets a new standard in digital rock physics, paving the way for future geoscience and engineering breakthroughs.
arXiv Detail & Related papers (2023-11-10T14:24:50Z) - Orientation-Shared Convolution Representation for CT Metal Artifact
Learning [63.67718355820655]
During X-ray computed tomography (CT) scanning, metallic implants carrying with patients often lead to adverse artifacts.
Existing deep-learning-based methods have gained promising reconstruction performance.
We propose an orientation-shared convolution representation strategy to adapt the physical prior structures of artifacts.
arXiv Detail & Related papers (2022-12-26T13:56:12Z) - Energy Reconstruction in Analysis of Cherenkov Telescopes Images in
TAIGA Experiment Using Deep Learning Methods [0.0]
This paper presents the analysis of simulated Monte Carlo images by several Deep Learning methods for a single telescope (mono-mode) and multiple IACT telescopes (stereo-mode)
The estimation of the quality of energy reconstruction was carried out and their energy spectra were analyzed using several types of neural networks.
arXiv Detail & Related papers (2022-11-16T15:24:32Z) - Multi-Channel Convolutional Analysis Operator Learning for Dual-Energy
CT Reconstruction [108.06731611196291]
We develop a multi-channel convolutional analysis operator learning (MCAOL) method to exploit common spatial features within attenuation images at different energies.
We propose an optimization method which jointly reconstructs the attenuation images at low and high energies with a mixed norm regularization on the sparse features.
arXiv Detail & Related papers (2022-03-10T14:22:54Z) - End-to-end Neuron Instance Segmentation based on Weakly Supervised
Efficient UNet and Morphological Post-processing [0.0]
We present an end-to-end weakly-supervised framework to automatically detect and segment NeuN stained neuronal cells on histological images.
We integrate the state-of-the-art network, EfficientNet, into our U-Net-like architecture.
arXiv Detail & Related papers (2022-02-17T14:35:45Z) - Processing Images from Multiple IACTs in the TAIGA Experiment with
Convolutional Neural Networks [62.997667081978825]
We use convolutional neural networks (CNNs) to analyze Monte Carlo-simulated images from the TAIGA experiment.
The analysis includes selection of the images corresponding to the showers caused by gamma rays and estimating the energy of the gamma rays.
arXiv Detail & Related papers (2021-12-31T10:49:11Z) - Analysis of the HiSCORE Simulated Events in TAIGA Experiment Using
Convolutional Neural Networks [77.34726150561087]
We propose to consider the use of convolution neural networks in task of air shower characteristics determination.
We use CNN to analyze HiSCORE events, treating them like images.
In addition, we present some preliminary results on the determination of the parameters of air showers.
arXiv Detail & Related papers (2021-12-19T15:18:56Z) - A parameter refinement method for Ptychography based on Deep Learning
concepts [55.41644538483948]
coarse parametrisation in propagation distance, position errors and partial coherence frequently menaces the experiment viability.
A modern Deep Learning framework is used to correct autonomously the setup incoherences, thus improving the quality of a ptychography reconstruction.
We tested our system on both synthetic datasets and also on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.
arXiv Detail & Related papers (2021-05-18T10:15:17Z) - The use of Convolutional Neural Networks for signal-background
classification in Particle Physics experiments [0.4301924025274017]
We present an extensive convolutional neural architecture search, achieving high accuracy for signal/background discrimination for a HEP classification use-case.
We demonstrate among other things that we can achieve the same accuracy as complex ResNet architectures with CNNs with less parameters.
arXiv Detail & Related papers (2020-02-13T19:54:46Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.