Streamlined Lensed Quasar Identification in Multiband Images via Ensemble Networks

• URL: http://arxiv.org/abs/2307.01090v2
• Date: Fri, 18 Aug 2023 08:31:30 GMT
• Title: Streamlined Lensed Quasar Identification in Multiband Images via Ensemble Networks
• Authors: Irham Taufik Andika, Sherry H. Suyu, Raoul Ca\~nameras, Alejandra Melo, Stefan Schuldt, Yiping Shu, Anna-Christina Eilers, Anton Timur Jaelani, Minghao Yue
• Abstract summary: Quasars experiencing strong lensing offer unique viewpoints on subjects related to cosmic expansion rate, dark matter, and quasar host galaxies. We have developed a novel approach by ensembling cutting-edge convolutional networks (CNNs) trained on realistic galaxy-quasar lens simulations. We retrieve approximately 60 million sources as parent samples and reduce this to 892,609 after employing a photometry preselection to discover quasars with Einstein radii of $theta_mathrmE5$ arcsec.
• Score: 34.82692226532414
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quasars experiencing strong lensing offer unique viewpoints on subjects related to the cosmic expansion rate, the dark matter profile within the foreground deflectors, and the quasar host galaxies. Unfortunately, identifying them in astronomical images is challenging since they are overwhelmed by the abundance of non-lenses. To address this, we have developed a novel approach by ensembling cutting-edge convolutional networks (CNNs) -- for instance, ResNet, Inception, NASNet, MobileNet, EfficientNet, and RegNet -- along with vision transformers (ViTs) trained on realistic galaxy-quasar lens simulations based on the Hyper Suprime-Cam (HSC) multiband images. While the individual model exhibits remarkable performance when evaluated against the test dataset, achieving an area under the receiver operating characteristic curve of $>$97.3% and a median false positive rate of 3.6%, it struggles to generalize in real data, indicated by numerous spurious sources picked by each classifier. A significant improvement is achieved by averaging these CNNs and ViTs, resulting in the impurities being downsized by factors up to 50. Subsequently, combining the HSC images with the UKIRT, VISTA, and unWISE data, we retrieve approximately 60 million sources as parent samples and reduce this to 892,609 after employing a photometry preselection to discover $z>1.5$ lensed quasars with Einstein radii of $\theta_\mathrm{E}<5$ arcsec. Afterward, the ensemble classifier indicates 3080 sources with a high probability of being lenses, for which we visually inspect, yielding 210 prevailing candidates awaiting spectroscopic confirmation. These outcomes suggest that automated deep learning pipelines hold great potential in effectively detecting strong lenses in vast datasets with minimal manual visual inspection involved.

Related papers

• A Versatile Framework for Analyzing Galaxy Image Data by Implanting Human-in-the-loop on a Large Vision Model [14.609681101463334]
  We present a framework for general analysis of galaxy images based on a large vision model (LVM) plus downstream tasks (DST) Considering the low signal-to-noise ratio of galaxy images, we have incorporated a Human-in-the-loop (HITL) module into our large vision model. For object detection, trained by 1000 data points, our DST upon the LVM achieves an accuracy of 96.7%, while ResNet50 plus Mask R-CNN gives an accuracy of 93.1%.
  arXiv  Detail & Related papers  (2024-05-17T16:29:27Z)
• CSST Strong Lensing Preparation: a Framework for Detecting Strong Lenses in the Multi-color Imaging Survey by the China Survey Space Telescope (CSST) [25.468504540327498]
  Strong gravitational lensing is a powerful tool for investigating dark matter and dark energy properties. We have developed a framework based on a hierarchical visual Transformer with a sliding window technique to search for strong lensing systems within entire images. Our framework achieves precision and recall rates of 0.98 and 0.90, respectively.
  arXiv  Detail & Related papers  (2024-04-02T09:44:30Z)
• Fewer is More: Efficient Object Detection in Large Aerial Images [59.683235514193505]
  This paper presents an Objectness Activation Network (OAN) to help detectors focus on fewer patches but achieve more efficient inference and more accurate results. Using OAN, all five detectors acquire more than 30.0% speed-up on three large-scale aerial image datasets. We extend our OAN to driving-scene object detection and 4K video object detection, boosting the detection speed by 112.1% and 75.0%, respectively.
  arXiv  Detail & Related papers  (2022-12-26T12:49:47Z)
• When Spectral Modeling Meets Convolutional Networks: A Method for Discovering Reionization-era Lensed Quasars in Multi-band Imaging Data [0.0]
  We introduce a new spatial geometry veto criterion, implemented via image-based deep learning. We make the first application of this approach in a systematic search for reionization-era lensed quasars. The training datasets are constructed by painting deflected point-source lights over actual galaxy images to generate realistic galaxy-quasar lens models.
  arXiv  Detail & Related papers  (2022-11-26T11:27:13Z)
• Detection of Strongly Lensed Arcs in Galaxy Clusters with Transformers [11.051750815556748]
  We propose a framework to detect cluster-scale strongly lensed arcs, which contains a transformer-based detection algorithm and an image simulation algorithm. Results show that our approach could achieve 99.63 % accuracy rate, 90.32 % recall rate, 85.37 % precision rate and 0.23 % false positive rate in detection of strongly lensed arcs from simulated images.
  arXiv  Detail & Related papers  (2022-11-11T02:33:34Z)
• A machine learning based approach to gravitational lens identification with the International LOFAR Telescope [0.0]
  We present a novel machine learning based approach for detecting galaxy-scale gravitational lenses from interferometric data. We develop and test several Convolutional Neural Networks to determine the probability and uncertainty of a given sample being classified as a lensed or non-lensed event. We expect to discover the vast majority of galaxy-scale gravitational lens systems contained within the LOFAR Two Metre Sky Survey.
  arXiv  Detail & Related papers  (2022-07-21T18:18:55Z)
• RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Images [82.1679766706423]
  Salient object detection (SOD) for optical remote sensing images (RSIs) aims at locating and extracting visually distinctive objects/regions from the optical RSIs. We propose a relational reasoning network with parallel multi-scale attention for SOD in optical RSIs. Our proposed RRNet outperforms the existing state-of-the-art SOD competitors both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2021-10-27T07:18:32Z)
• DeepShadows: Separating Low Surface Brightness Galaxies from Artifacts using Deep Learning [70.80563014913676]
  We investigate the use of convolutional neural networks (CNNs) for the problem of separating low-surface-brightness galaxies from artifacts in survey images. We show that CNNs offer a very promising path in the quest to study the low-surface-brightness universe.
  arXiv  Detail & Related papers  (2020-11-24T22:51:08Z)
• TJU-DHD: A Diverse High-Resolution Dataset for Object Detection [48.94731638729273]
  Large-scale, rich-diversity, and high-resolution datasets play an important role in developing better object detection methods. We build a diverse high-resolution dataset (called TJU-DHD) The dataset contains 115,354 high-resolution images and 709,330 labeled objects with a large variance in scale and appearance.
  arXiv  Detail & Related papers  (2020-11-18T09:32:24Z)
• Anchor-free Small-scale Multispectral Pedestrian Detection [88.7497134369344]
  We propose a method for effective and efficient multispectral fusion of the two modalities in an adapted single-stage anchor-free base architecture. We aim at learning pedestrian representations based on object center and scale rather than direct bounding box predictions. Results show our method's effectiveness in detecting small-scaled pedestrians.
  arXiv  Detail & Related papers  (2020-08-19T13:13:01Z)

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.