Euclid: Finding strong gravitational lenses in the Early Release Observations using convolutional neural networks
B. C. Nagam, J. A. Acevedo Barroso, J. Wilde, I. T. Andika, A. Manjón-García, R. Pearce-Casey, D. Stern, J. W. Nightingale, L. A. Moustakas, K. McCarthy, E. Mor… 展开作者
B. C. Nagam, J. A. Acevedo Barroso, J. Wilde, I. T. Andika, A. Manjón-García, R. Pearce-Casey, D. Stern, J. W. Nightingale, L. A. Moustakas, K. McCarthy, E. Moravec, L. Leuzzi, K. Rojas, S. Serjeant, T. E. Collett, P. Matavulj, M. Walmsley, B. Clément, C. Tortora, R. Gavazzi, R. B. Metcalf, C. M. O'Riordan, G. Verdoes Kleijn, L. V. E. Koopmans, E. A. Valentijn, V. Busillo, S. Schuldt, F. Courbin, G. Vernardos, M. Meneghetti, A. Díaz-Sánchez, J. M. Diego, L. R. Ecker, T. T. Thai, A. R. Cooray, H. M. Courtois, L. Delchambre, G. Despali, D. Sluse, L. Ulivi, A. Melo, P. Corcho-Caballero, B. Altieri, A. Amara, S. Andreon, N. Auricchio, H. Aussel, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, P. Battaglia, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, A. Caillat, S. Camera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, M. Cropper, A. Da Silva, H. Degaudenzi, G. De Lucia, A. M. Di Giorgio, J. Dinis, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, M. Fabricius, M. Farina, S. Farrens, S. Ferriol, M. Frailis, E. Franceschi, M. Fumana, K. George, W. Gillard, B. Gillis, C. Giocoli, P. Gómez-Alvarez, A. Grazian, F. Grupp, L. Guzzo, S. V. H. Haugan, J. Hoar, W. Holmes, I. Hook, F. Hormuth, A. Hornstrup, P. Hudelot, K. Jahnke, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, B. Kubik, K. Kuijken, M. Kümmel, M. Kunz, H. Kurki-Suonio, R. Laureijs, D. Le Mignant, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, E. Maiorano, O. Mansutti, O. Marggraf, K. Markovic, M. Martinelli, N. Martinet, F. Marulli, R. Massey, E. Medinaceli, M. Melchior, Y. Mellier, E. Merlin, G. Meylan, M. Moresco, L. Moscardini, R. Nakajima, C. Neissner, R. C. Nichol, S. -M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, E. Rossetti, R. Saglia, Z. Sakr, A. G. Sánchez, D. Sapone, B. Sartoris, M. Schirmer, P. Schneider, T. Schrabback, A. Secroun, G. Seidel, S. Serrano, C. Sirignano, G. Sirri, J. Skottfelt, L. Stanco, J. -L. Starck, J. Steinwagner, P. Tallada-Crespí, D. Tavagnacco, A. N. Taylor, H. I. Teplitz, I. Tereno, R. Toledo-Moreo, F. Torradeflot, A. Tsyganov, I. Tutusaus, L. Valenziano, T. Vassallo, A. Veropalumbo, Y. Wang, J. Weller, A. Zacchei, E. Zucca, C. Burigana, A. Mora, M. Pöntinen, V. Scottez
The Early Release Observations (ERO) from Euclid have detected several new galaxy-galaxy strong gravitational lenses, with the all-sky survey expected to find 170,000 new systems, greatly enhancing studies of dark matter, dark energy, and constraints on the cosmological parameters. As a first step, visual inspection of all galaxies in one of the ERO fields (Perseus) was carried out to identify candidate strong lensing systems and compared to the predictions from Convolutional Neural Networks (CNNs). However, the entire ERO data set is too large for expert visual inspection. In this paper, we therefore extend the CNN analysis to the whole ERO data set, using different CNN architectures and methodologies. Using five CNN architectures, we identified 8,469 strong gravitational lens candidates from IE-band cutouts of 13 Euclid ERO fields, narrowing them to 97 through visual inspection, including 14 grade A and 31 grade B candidates. We present the spectroscopic confirmation of a strong gravitational lensing candidate, EUCLJ081705.61+702348.8. The foreground lensing galaxy, an early-type system at redshift z = 0.335, and the background source, a star-forming galaxy at redshift z = 1.475 with [O II] emission, are both identified. Lens modeling using the Euclid strong lens modeling pipeline reveals two distinct arcs in a lensing configuration, with an Einstein radius of 1.18 \pm 0.03 arcseconds, confirming the lensing nature of the system. These findings highlight the importance of a broad CNN search to efficiently reduce candidates, followed by visual inspection to eliminate false positives and achieve a high-purity sample of strong lenses in Euclid.
Gravitational Lenses in UNIONS and Euclid (GLUE) I: A Search for Strong Gravitational Lenses in UNIONS with Subaru, CFHT, and Pan-STARRS Data
University of Hawai‘i（夏威夷大学）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；University of San Francisco（旧金山大学）；University of Hawai‘i at Mānoa（夏威夷大学马诺阿分校）；University of California, Berkeley（加州大学伯克利分校）；Université Paris-Saclay（巴黎萨克雷大学）；Université Paris Cité（巴黎西岱大学）；CEA（法国原子能和替代能源委员会）；CNRS（法国国家科学研究中心）；AIM（天体物理与宇宙微波背景研究所）；Laboratoire d’Astrophysique de Marseille（马赛天体物理学实验室）；Aix-Marseille Université（艾克斯-马赛大学）；CNES（法国国家空间研究中心）；Institut d’Astrophysique de Paris（巴黎天体物理学研究所）；Sorbonne Université（索邦大学）；National Research Council Herzberg Astronomy and Astrophysics（加拿大国家研究委员会赫茨伯格天文学与天体物理学研究所）；University of Waterloo（滑铁卢大学）；Waterloo Centre for Astrophysics（滑铁卢天体物理学中心）；Perimeter Institute for Theoretical Physics（理论物理珀IMETER研究所）；University of Hawaii（夏威夷大学）；University of British Columbia（不列颠哥伦比亚大学）
Measuring the Stellar-to-Halo Mass Relation at $\sim10^{10}$ Solar masses, using forthcoming space-based imaging of galaxy-galaxy strong lenses
National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；School of Physics and Astronomy, Beijing Normal University（北京师范大学物理与天文学院）；School of Astronomy and Space Science, University of Chinese Academy of Sciences（中国科学院大学天文学与空间科学学院）；Institute for Astrophysics, School of Physics, Zhengzhou University（郑州大学物理学院天体物理研究所）；School of Mathematics, Statistics and Physics, Newcastle University（纽卡斯尔大学数学、统计与物理学院）；Institute for Computational Cosmology, Department of Physics, Durham University（杜伦大学物理学院计算宇宙学研究所）
Synergies between SALT and Herschel, Euclid & the SKA: strong gravitational lensing & galaxy evolution
引力透镜在过去的几年里引起了人们极大的兴趣。由于创新的多波长选择，2000年已知的少数几个强透镜系统现在已经被数百个所取代，并且来自Herschel和其他亚毫米测量的数千个透镜的前景迫在眉睫。欧几里德和平方公里阵列承诺数万甚至数十万。引力透镜是极少数能够绘制暗物质晕分布的探测器之一。透镜还提供了独立的宇宙学参数估计，并使研究星系人口，否则太微弱的详细研究。SALT处于非常有利的位置，可以对Herschel、南极望远镜、Atacama宇宙学望远镜、Euclid和Square Kilometer Array等的前景透镜和背景源的后续观测产生巨大影响。本文回顾了高影响SALT科学的前景和星系演化的许多限制，可能导致。
Survey of Gravitationally-lensed Objects in HSC Imaging (SuGOHI). II. Environments and Line-of-Sight Structure of Strong Gravitational Lens Galaxies to z ~ 0.8
Euclid Quick Data Release (Q1). The Strong Lensing Discovery Engine F -- Bright and low-redshift strong lenses
Ludwig-Maximilians-Universität München（慕尼黑大学）；Max Planck Institute for Extraterrestrial Physics（马克斯·普朗克地外物理研究所）；University of Portsmouth（朴茨茅斯大学）；Oxford University（牛津大学）
Lenses In VoicE (LIVE): Searching for strong gravitational lenses in the VOICE@VST survey using Convolutional Neural Networks
我们提出了一个样本的16个可能的强引力透镜中确定的CDFS和ES 1字段（VOICE调查）的VST光学成像使用卷积神经网络（CNN）。我们训练两个不同的CNN在合成图像上，合成图像是通过将模拟的引力弧叠加在VOICE中观察到的真实发光红色星系上产生的。具体来说，第一个CNN是在单波段图像上训练的，更容易识别具有大爱因斯坦半径的系统，而第二个是在复合RGB图像上训练的，在检索具有较小爱因斯坦半径的系统时更准确。我们将这两个网络应用于VOICE调查的实际数据，利用了该深度调查的高极限震级（r波段为26.1）和低PSF FWHM（r波段为0.8”）。我们用$mag_r<21.5$分析了$\sim21,200$图像，确定了257个晶状体候选者。为了检索高置信度的样本并评估我们技术的准确性，九位作者进行了目视检查。大约75%的系统被至少一位作者归类为可能的透镜。最后，我们组装现场样本（Lenses In VoicE）组成的16个系统通过所选择的分级阈值。其中三个候选者在哈勃太空望远镜观察时显示出可能的透镜特征。这项工作进一步证实了CNN检查星系大样本寻找引力透镜的能力。这些算法对于利用欧几里德卫星和维拉鲁宾天文台即将进行的调查的全部科学潜力至关重要
Retrospective Search for Strongly Lensed Supernovae in the DESI Legacy Imaging Surveys
University of California, Los Angeles（加州大学洛杉矶分校）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；University of San Francisco（旧金山大学）；Gemini Observatory / NSF’s NOIRLab（吉米天文台/美国国家光学红外天文实验室）；Kavli Institute for the Physics and Mathematics of the Universe, University of Tokyo（东京大学宇宙物理数学研究所）；University of Hawaii（夏威夷大学）
本文开发了针对强引力透镜系统的瞬变源搜索流水线，应用于 DESI Legacy 巡天数据，发现七个新的透镜超新星候选体，其中一个极可能是 Ia 型，有助于宇宙学参数约束。
Detection of Strongly Lensed Arcs in Galaxy Clusters with Transformers
Taiyuan University of Technology（太原理工大学）；Peng Cheng Lab（鹏城实验室）；Durham University（杜伦大学）；National Astronomical Observatories（国家天文台）；Central South University（中南大学）
