20 citations · 48 across the 11 of their papers we have counts for
14 papers
Euclid Quick Data Release (Q1). AstroVink: A vision transformer approach to find strong gravitational lens systems
Euclid Collaboration, S. H. Vincken, K. Rojas +302
We present AstroVink, a vision transformer classifier designed for automated identification of strong lens candidates in Euclid imaging. We build upon the DINOv2 encoder, fine tune…
Euclid: A machine-learning search for dual and lensed AGN at sub-arcsec separations
L. Ulivi, F. Mannucci, M. Scialpi +189
Cosmological models of hierarchical structure formation predict the existence of a widespread population of dual accreting supermassive black holes (SMBHs) on kpc-scale separations…
Euclid Quick Data Release (Q1). The first catalogue of strong-lensing galaxy clusters
Euclid Collaboration, P. Bergamini, M. Meneghetti +375
We present the first catalogue of strong lensing galaxy clusters identified in the Euclid Quick Release 1 observations (covering ). This catalogue is the resu…
Euclid Quick Data Release (Q1). The Strong Lensing Discovery Engine E -- Ensemble classification of strong gravitational lenses: lessons for Data Release 1
Euclid Collaboration, P. Holloway, A. Verma +343
The Euclid Wide Survey (EWS) is expected to identify of order galaxy-galaxy strong lenses across deg. The Euclid Quick Data Release (Q1) of deg Eu…
Euclid Quick Data Release (Q1): The Strong Lensing Discovery Engine A -- System overview and lens catalogue
Euclid Collaboration, M. Walmsley, P. Holloway +372
We present a catalogue of 497 galaxy-galaxy strong lenses in the Euclid Quick Release 1 data (63 deg). In the initial 0.45\% of Euclid's surveys, we double the total number of…
Euclid Quick Data Release (Q1). The Strong Lensing Discovery Engine C: Finding lenses with machine learning
Euclid Collaboration, N. E. P. Lines, T. E. Collett +350
Strong gravitational lensing has the potential to provide a powerful probe of astrophysics and cosmology, but fewer than 1000 strong lenses have been confirmed so far. With a 0.16'…
Reducing false positives in strong lens detection through effective augmentation and ensemble learning
Samira Rezaei, Amirmohammad Chegeni, Bharath Chowdhary Nagam +4
This research studies the impact of high-quality training datasets on the performance of Convolutional Neural Networks (CNNs) in detecting strong gravitational lenses. We stress th…
Euclid: Finding strong gravitational lenses in the Early Release Observations using convolutional neural networks
B. C. Nagam, J. A. Acevedo Barroso, J. Wilde +192
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,…
Automation of finding strong gravitational lenses in the Kilo Degree Survey with U-DenseLens (DenseLens + Segmentation)
Bharath Chowdhary Nagam, Léon V E Koopmans, Edwin A Valentijn +7
In the context of upcoming large-scale surveys like Euclid, the necessity for the automation of strong lens detection is essential. While existing machine learning pipelines heavil…
MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble
Frank N. Mol, Luuk van der Hoek, Baoqiang Ma +6
The superior soft tissue differentiation provided by MRI may enable more accurate tumor segmentation compared to CT and PET, potentially enhancing adaptive radiotherapy treatment p…
Euclid: Searches for strong gravitational lenses using convolutional neural nets in Early Release Observations of the Perseus field
R. Pearce-Casey, B. C. Nagam, J. Wilde +204
The Euclid Wide Survey (EWS) is predicted to find approximately 170 000 galaxy-galaxy strong lenses from its lifetime observation of 14 000 deg^2 of the sky. Detecting this many le…
Euclid: The Early Release Observations Lens Search Experiment
J. A. Acevedo Barroso, C. M. O'Riordan, B. Clément +206
We investigated the ability of the Euclid telescope to detect galaxy-scale gravitational lenses. To do so, we performed a systematic visual inspection of the Eucl…
Object classification with Convolutional Neural Networks: from KiDS to Euclid
G. A. Verdoes Kleijn, C. A. Marocico, Y. Mzayek +11
Large-scale imaging surveys have grown about 1000 times faster than the number of astronomers in the last 3 decades. Using Artificial Intelligence instead of astronomer's brains fo…
DenseLens -- Using DenseNet ensembles and information criteria for finding and rank-ordering strong gravitational lenses,
Bharath Chowdhary Nagam, Léon V. E. Koopmans, Edwin A. Valentijn +5
Convolutional neural networks (CNNs) are the state-of-the-art technique for identifying strong gravitational lenses. Although they are highly successful in recovering genuine lens…