Identification of Binary Neutron Star Mergers in Gravitational-Wave Data Using YOLO One-Shot Object Detection

• URL: http://arxiv.org/abs/2207.00591v1
• Date: Fri, 1 Jul 2022 10:11:44 GMT
• Title: Identification of Binary Neutron Star Mergers in Gravitational-Wave Data Using YOLO One-Shot Object Detection
• Authors: Jo\~ao Aveiro, Felipe F. Freitas, M\'arcio Ferreira, Antonio Onofre, Constan\c{c}a Provid\^encia, Gon\c{c}alo Gon\c{c}alves, and Jos\'e A. Font
• Abstract summary: We demonstrate the application of the YOLOv5 model, a general purpose convolution-based single-shot object detection model, in the task of detecting binary neutron star (BNS) coalescence events from gravitational-wave data of current generation interferometer detectors. We achieve mean average precision ($textmAP_[0.50]$) values of 0.945 for a single class validation dataset and as high as 0.978 for test datasets.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We demonstrate the application of the YOLOv5 model, a general purpose convolution-based single-shot object detection model, in the task of detecting binary neutron star (BNS) coalescence events from gravitational-wave data of current generation interferometer detectors. We also present a thorough explanation of the synthetic data generation and preparation tasks based on approximant waveform models used for the model training, validation and testing steps. Using this approach, we achieve mean average precision ($\text{mAP}_{[0.50]}$) values of 0.945 for a single class validation dataset and as high as 0.978 for test datasets. Moreover, the trained model is successful in identifying the GW170817 event in the LIGO H1 detector data. The identification of this event is also possible for the LIGO L1 detector data with an additional pre-processing step, without the need of removing the large glitch in the final stages of the inspiral. The detection of the GW190425 event is less successful, which attests to performance degradation with the signal-to-noise ratio. Our study indicates that the YOLOv5 model is an interesting approach for first-stage detection alarm pipelines and, when integrated in more complex pipelines, for real-time inference of physical source parameters.

Related papers

• SIRST-5K: Exploring Massive Negatives Synthesis with Self-supervised Learning for Robust Infrared Small Target Detection [53.19618419772467]
  Single-frame infrared small target (SIRST) detection aims to recognize small targets from clutter backgrounds. With the development of Transformer, the scale of SIRST models is constantly increasing. With a rich diversity of infrared small target data, our algorithm significantly improves the model performance and convergence speed.
  arXiv  Detail & Related papers  (2024-03-08T16:14:54Z)
• Deep Generative Models for Ultra-High Granularity Particle Physics Detector Simulation: A Voyage From Emulation to Extrapolation [0.0]
  This thesis aims to overcome this challenge for the Pixel Vertex Detector (PXD) at the Belle II experiment. This study introduces, for the first time, the results of using deep generative models for ultra-high granularity detector simulation in Particle Physics.
  arXiv  Detail & Related papers  (2024-03-05T23:12:47Z)
• DiffusionEngine: Diffusion Model is Scalable Data Engine for Object Detection [41.436817746749384]
  Diffusion Model is a scalable data engine for object detection. DiffusionEngine (DE) provides high-quality detection-oriented training pairs in a single stage.
  arXiv  Detail & Related papers  (2023-09-07T17:55:01Z)
• Exploring the Effectiveness of Dataset Synthesis: An application of Apple Detection in Orchards [68.95806641664713]
  We explore the usability of Stable Diffusion 2.1-base for generating synthetic datasets of apple trees for object detection. We train a YOLOv5m object detection model to predict apples in a real-world apple detection dataset. Results demonstrate that the model trained on generated data is slightly underperforming compared to a baseline model trained on real-world images.
  arXiv  Detail & Related papers  (2023-06-20T09:46:01Z)
• Convolutional Neural Networks for the classification of glitches in gravitational-wave data streams [52.77024349608834]
  We classify transient noise signals (i.e.glitches) and gravitational waves in data from the Advanced LIGO detectors. We use models with a supervised learning approach, both trained from scratch using the Gravity Spy dataset. We also explore a self-supervised approach, pre-training models with automatically generated pseudo-labels.
  arXiv  Detail & Related papers  (2023-03-24T11:12:37Z)
• DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature [143.5381108333212]
  We show that text sampled from an large language model tends to occupy negative curvature regions of the model's log probability function. We then define a new curvature-based criterion for judging if a passage is generated from a given LLM. We find DetectGPT is more discriminative than existing zero-shot methods for model sample detection.
  arXiv  Detail & Related papers  (2023-01-26T18:44:06Z)
• DEGAN: Time Series Anomaly Detection using Generative Adversarial Network Discriminators and Density Estimation [0.0]
  We have proposed an unsupervised Generative Adversarial Network (GAN)-based anomaly detection framework, DEGAN. It relies solely on normal time series data as input to train a well-configured discriminator (D) into a standalone anomaly predictor.
  arXiv  Detail & Related papers  (2022-10-05T04:32:12Z)
• Event Data Association via Robust Model Fitting for Event-based Object Tracking [66.05728523166755]
  We propose a novel Event Data Association (called EDA) approach to explicitly address the event association and fusion problem. The proposed EDA seeks for event trajectories that best fit the event data, in order to perform unifying data association and information fusion. The experimental results show the effectiveness of EDA under challenging scenarios, such as high speed, motion blur, and high dynamic range conditions.
  arXiv  Detail & Related papers  (2021-10-25T13:56:00Z)
• Complete parameter inference for GW150914 using deep learning [0.0]
  LIGO and Virgo gravitational-wave observatories have detected many exciting events over the past five years. As the rate of detections grows with detector sensitivity, this poses a growing computational challenge for data analysis. We apply deep learning techniques to perform fast likelihood-free Bayesian inference for gravitational waves.
  arXiv  Detail & Related papers  (2020-08-07T18:00:02Z)
• Condensing Two-stage Detection with Automatic Object Key Part Discovery [87.1034745775229]
  Two-stage object detectors generally require excessively large models for their detection heads to achieve high accuracy. We propose that the model parameters of two-stage detection heads can be condensed and reduced by concentrating on object key parts. Our proposed technique consistently maintains original performance while waiving around 50% of the model parameters of common two-stage detection heads.
  arXiv  Detail & Related papers  (2020-06-10T01:20:47Z)

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.