Zobrazeno 1 - 10
of 491
pro vyhledávání: '"Scaife, A M M"'
With the growth of data from new radio telescope facilities, machine-learning approaches to the morphological classification of radio galaxies are increasingly being utilised. However, while widely employed deep-learning models using convolutional ne
Externí odkaz:
http://arxiv.org/abs/2406.09024
Autor:
Mohan, Devina, Scaife, Anna M. M.
The radio astronomy community is rapidly adopting deep learning techniques to deal with the huge data volumes expected from the next generation of radio observatories. Bayesian neural networks (BNNs) provide a principled way to model uncertainty in t
Externí odkaz:
http://arxiv.org/abs/2405.18351
Autor:
Walmsley, Mike, Bowles, Micah, Scaife, Anna M. M., Makechemu, Jason Shingirai, Gordon, Alexander J., Ferguson, Annette M. N., Mann, Robert G., Pearson, James, Popp, Jürgen J., Bovy, Jo, Speagle, Josh, Dickinson, Hugh, Fortson, Lucy, Géron, Tobias, Kruk, Sandor, Lintott, Chris J., Mantha, Kameswara, Mohan, Devina, O'Ryan, David, Slijepevic, Inigo V.
We present the first systematic investigation of supervised scaling laws outside of an ImageNet-like context - on images of galaxies. We use 840k galaxy images and over 100M annotations by Galaxy Zoo volunteers, comparable in scale to Imagenet-1K. We
Externí odkaz:
http://arxiv.org/abs/2404.02973
Autor:
Taylor, A. R., Sekhar, S., Heino, L., Scaife, A. M. M., Stil, J., Bowles, M., Jarvis, M., Heywood, I., Collier, J. D.
The MeerKAT International GigaHertz Tiered Extragalactic Exploration (MIGHTEE) is one of the MeerKAT large survey projects, designed to pathfind SKA key science. MIGHTEE is undertaking deep radio imaging of four well observed fields (COSMOS, XMM-LSS,
Externí odkaz:
http://arxiv.org/abs/2312.13230
Autor:
Walmsley, Mike, Scaife, Anna M. M.
We identify rare and visually distinctive galaxy populations by searching for structure within the learned representations of pretrained models. We show that these representations arrange galaxies by appearance in patterns beyond those needed to pred
Externí odkaz:
http://arxiv.org/abs/2312.02910
At present, the field of astronomical machine learning lacks widely-used benchmarking datasets; most research employs custom-made datasets which are often not publicly released, making comparisons between models difficult. In this paper we present CR
Externí odkaz:
http://arxiv.org/abs/2311.10507
Autor:
Walmsley, Mike, Géron, Tobias, Kruk, Sandor, Scaife, Anna M. M., Lintott, Chris, Masters, Karen L., Dawson, James M., Dickinson, Hugh, Fortson, Lucy, Garland, Izzy L., Mantha, Kameswara, O'Ryan, David, Popp, Jürgen, Simmons, Brooke, Baeten, Elisabeth M., Macmillan, Christine
We present detailed morphology measurements for 8.67 million galaxies in the DESI Legacy Imaging Surveys (DECaLS, MzLS, and BASS, plus DES). These are automated measurements made by deep learning models trained on Galaxy Zoo volunteer votes. Our mode
Externí odkaz:
http://arxiv.org/abs/2309.11425
Autor:
Slijepcevic, Inigo V., Scaife, Anna M. M., Walmsley, Mike, Bowles, Micah, Wong, O. Ivy, Shabala, Stanislav S., White, Sarah V.
In this work, we apply self-supervised learning with instance differentiation to learn a robust, multi-purpose representation for image analysis of resolved extragalactic continuum images. We train a multi-use model which compresses our unlabelled da
Externí odkaz:
http://arxiv.org/abs/2305.16127
The volume of data from current and future observatories has motivated the increased development and application of automated machine learning methodologies for astronomy. However, less attention has been given to the production of standardised datas
Externí odkaz:
http://arxiv.org/abs/2305.11108
Autor:
Bowles, Micah, Tang, Hongming, Vardoulaki, Eleni, Alexander, Emma L., Luo, Yan, Rudnick, Lawrence, Walmsley, Mike, Porter, Fiona, Scaife, Anna M. M., Slijepcevic, Inigo Val, Adams, Elizabeth A. K., Drabent, Alexander, Dugdale, Thomas, Gürkan, Gülay, Hopkins, Andrew M., Jimenez-Andrade, Eric F., Leahy, Denis A., Norris, Ray P., Rahman, Syed Faisal ur, Ouyang, Xichang, Segal, Gary, Shabala, Stanislav S., Wong, O. Ivy
We present a novel natural language processing (NLP) approach to deriving plain English descriptors for science cases otherwise restricted by obfuscating technical terminology. We address the limitations of common radio galaxy morphology classificati
Externí odkaz:
http://arxiv.org/abs/2304.07171