Identification of 4FGL uncertain sources at Higher Resolutions with
Inverse Discrete Wavelet Transform
- URL: http://arxiv.org/abs/2401.02589v1
- Date: Fri, 5 Jan 2024 01:02:34 GMT
- Title: Identification of 4FGL uncertain sources at Higher Resolutions with
Inverse Discrete Wavelet Transform
- Authors: Haitao Cao, Hubing Xiao, Zhijian Luo, Xiangtao Zeng, Junhui Fan
- Abstract summary: In the forthcoming era of big astronomical data, it is a burden to find out target sources from ground-based and space-based telescopes.
In this work, we focused on the task of finding AGN candidates and identifying BL Lac/FSRQ candidates from the 4FGL DR3 uncertain sources.
- Score: 0.562479170374811
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In the forthcoming era of big astronomical data, it is a burden to find out
target sources from ground-based and space-based telescopes. Although Machine
Learning (ML) methods have been extensively utilized to address this issue, the
incorporation of in-depth data analysis can significantly enhance the
efficiency of identifying target sources when dealing with massive volumes of
astronomical data. In this work, we focused on the task of finding AGN
candidates and identifying BL Lac/FSRQ candidates from the 4FGL DR3 uncertain
sources. We studied the correlations among the attributes of the 4FGL DR3
catalogue and proposed a novel method, named FDIDWT, to transform the original
data. The transformed dataset is characterized as low-dimensional and
feature-highlighted, with the estimation of correlation features by Fractal
Dimension (FD) theory and the multi-resolution analysis by Inverse Discrete
Wavelet Transform (IDWT). Combining the FDIDWT method with an improved
lightweight MatchboxConv1D model, we accomplished two missions: (1) to
distinguish the Active Galactic Nuclei (AGNs) from others (Non-AGNs) in the
4FGL DR3 uncertain sources with an accuracy of 96.65%, namely, Mission A; (2)
to classify blazar candidates of uncertain type (BCUs) into BL Lacertae objects
(BL Lacs) or Flat Spectrum Radio Quasars (FSRQs) with an accuracy of 92.03%,
namely, Mission B. There are 1354 AGN candidates in Mission A, 482 BL Lacs
candidates and 128 FSRQ candidates in Mission B were found. The results show a
high consistency of greater than 98% with the results in previous works. In
addition, our method has the advantage of finding less variable and relatively
faint sources than ordinary methods.
Related papers
- TernaryLLM: Ternarized Large Language Model [29.29122031050894]
Large language models (LLMs) have achieved remarkable performance on Natural Language Processing (NLP) tasks.
We introduce Dual Learnable Ternarization (DLT), which enables both scales and shifts to be learnable.
We also propose Outlier-Friendly Feature Knowledge Distillation (OFF) to recover the information lost in extremely low-bit quantization.
arXiv Detail & Related papers (2024-06-11T11:40:12Z) - Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection [59.41026558455904]
We focus on multi-modal anomaly detection. Specifically, we investigate early multi-modal approaches that attempted to utilize models pre-trained on large-scale visual datasets.
We propose a Local-to-global Self-supervised Feature Adaptation (LSFA) method to finetune the adaptors and learn task-oriented representation toward anomaly detection.
arXiv Detail & Related papers (2024-01-06T07:30:41Z) - Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for
Advanced Object Detection [55.2480439325792]
We present an in-depth evaluation of an object detection model that integrates the LSKNet backbone with the DiffusionDet head.
The proposed model achieves a mean average precision (MAP) of approximately 45.7%, which is a significant improvement.
This advancement underscores the effectiveness of the proposed modifications and sets a new benchmark in aerial image analysis.
arXiv Detail & Related papers (2023-11-21T19:49:13Z) - Source-Free Collaborative Domain Adaptation via Multi-Perspective
Feature Enrichment for Functional MRI Analysis [55.03872260158717]
Resting-state MRI functional (rs-fMRI) is increasingly employed in multi-site research to aid neurological disorder analysis.
Many methods have been proposed to reduce fMRI heterogeneity between source and target domains.
But acquiring source data is challenging due to concerns and/or data storage burdens in multi-site studies.
We design a source-free collaborative domain adaptation framework for fMRI analysis, where only a pretrained source model and unlabeled target data are accessible.
arXiv Detail & Related papers (2023-08-24T01:30:18Z) - Imbalanced Aircraft Data Anomaly Detection [103.01418862972564]
Anomaly detection in temporal data from sensors under aviation scenarios is a practical but challenging task.
We propose a Graphical Temporal Data Analysis framework.
It consists three modules, named Series-to-Image (S2I), Cluster-based Resampling Approach using Euclidean Distance (CRD) and Variance-Based Loss (VBL)
arXiv Detail & Related papers (2023-05-17T09:37:07Z) - Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary
Data [100.33096338195723]
We focus on Few-shot Learning with Auxiliary Data (FLAD)
FLAD assumes access to auxiliary data during few-shot learning in hopes of improving generalization.
We propose two algorithms -- EXP3-FLAD and UCB1-FLAD -- and compare them with prior FLAD methods that either explore or exploit.
arXiv Detail & Related papers (2023-02-01T18:59:36Z) - A comparative study of source-finding techniques in HI emission line
cubes using SoFiA, MTObjects, and supervised deep learning [0.0]
The 21 cm spectral line emission of atomic neutral hydrogen (HI) is one of the primary wavelengths observed in radio astronomy.
This study aimed to find the optimal pipeline for finding and masking the most sources with the best mask quality and the fewest artefacts in 3D neutral hydrogen cubes.
Two traditional source-finding methods were tested, SoFiA and MTObjects, as well as a new supervised deep learning approach, in which a 3D convolutional neural network architecture, known as V-Net was used.
The pipelines were tested on HI data cubes from the Westerbork Synthesis Radio Telescope with additional inserted mock galaxies.
arXiv Detail & Related papers (2022-11-23T09:45:07Z) - Anchor-free Oriented Proposal Generator for Object Detection [59.54125119453818]
Oriented object detection is a practical and challenging task in remote sensing image interpretation.
Nowadays, oriented detectors mostly use horizontal boxes as intermedium to derive oriented boxes from them.
We propose a novel Anchor-free Oriented Proposal Generator (AOPG) that abandons the horizontal boxes-related operations from the network architecture.
arXiv Detail & Related papers (2021-10-05T10:45:51Z) - Primordial non-Gaussianity from the Completed SDSS-IV extended Baryon
Oscillation Spectroscopic Survey I: Catalogue Preparation and Systematic
Mitigation [3.2855185490071444]
We investigate the large-scale clustering of the final spectroscopic sample of quasars from the recently completed extended Baryon Oscillation Spectroscopic Survey (eBOSS)
We develop a neural network-based approach to mitigate spurious fluctuations in the density field caused by spatial variations in the quality of the imaging data used to select targets for follow-up spectroscopy.
arXiv Detail & Related papers (2021-06-25T16:01:19Z) - Weakly Supervised Instance Attention for Multisource Fine-Grained Object
Recognition with an Application to Tree Species Classification [9.668407688201361]
We propose a multisource method to classify relatively small objects.
The proposed method uses a single-source deep instance attention model with parallel branches for joint localization and classification of objects.
We show that all levels of fusion provide higher accuracies compared to the state-of-the-art, with the best performing method of feature-level fusion resulting in 53% accuracy for the recognition of 40 different types of trees.
arXiv Detail & Related papers (2021-05-23T17:51:14Z) - Constraining the recent star formation history of galaxies : an
Approximate Bayesian Computation approach [0.0]
We present a method to identify galaxies undergoing a strong variation of star formation activity in the last tens to hundreds Myr.
We analyze a sample of COSMOS galaxies using high signal-to-noise ratio broad band photometry.
arXiv Detail & Related papers (2020-02-18T19:00:01Z)
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.