Zobrazeno 1 - 10
of 2 874
pro vyhledávání: '"Lochner, P."'
Autor:
Andersson, Alex, Lintott, Chris, Fender, Rob, Lochner, Michelle, Woudt, Patrick, Eijnden, Jakob van den, van der Horst, Alexander, Horesh, Assaf, Saikia, Payaswini, Sivakoff, Gregory R., Tremou, Lilia, Vaccari, Mattia
In this work we explore the applicability of unsupervised machine learning algorithms to the task of finding radio transients. Facilities such as the Square Kilometre Array (SKA) will provide huge volumes of data in which to detect rare transients; t
Externí odkaz:
http://arxiv.org/abs/2410.01034
Automating anomaly detection is an open problem in many scientific fields, particularly in time-domain astronomy, where modern telescopes generate millions of alerts per night. Currently, most anomaly detection algorithms for astronomical time-series
Externí odkaz:
http://arxiv.org/abs/2408.08888
Bees are among the master navigators of the insect world. Despite impressive advances in robot navigation research, the performance of these insects is still unrivaled by any artificial system in terms of training efficiency and generalization capabi
Externí odkaz:
http://arxiv.org/abs/2406.01501
Autor:
Grespan, Margherita, Thuruthipilly, Hareesh, Pollo, Agnieszka, Lochner, Michelle, Biesiada, Marek, Etsebeth, Verlon
Publikováno v:
A&A 688, A34 (2024)
We apply a state-of-the-art transformer algorithm to 221 deg$^2$ of the Kilo Degree Survey (KiDS) to search for new strong gravitational lenses (SGL). We test four transformer encoders trained on simulated data from the Strong Lens Finding Challenge
Externí odkaz:
http://arxiv.org/abs/2405.11992
Automating real-time anomaly detection is essential for identifying rare transients in the era of large-scale astronomical surveys. Modern survey telescopes are generating tens of thousands of alerts per night, and future telescopes, such as the Vera
Externí odkaz:
http://arxiv.org/abs/2403.14742
Autor:
Mohale, Koketso, Lochner, Michelle
Publikováno v:
MNRAS Volume 530, Issue 1, May 2024, Pages 1274--1295
Unsupervised learning, a branch of machine learning that can operate on unlabelled data, has proven to be a powerful tool for data exploration and discovery in astronomy. As large surveys and new telescopes drive a rapid increase in data size and ric
Externí odkaz:
http://arxiv.org/abs/2311.14157
Publikováno v:
MNRAS Volume 529, Issue 1, March 2024, Pages 732--747
Modern astronomical surveys are producing datasets of unprecedented size and richness, increasing the potential for high-impact scientific discovery. This possibility, coupled with the challenge of exploring a large number of sources, has led to the
Externí odkaz:
http://arxiv.org/abs/2309.08660
Autor:
Conor Owens-Walton, Talia M. Nir, Sarah Al-Bachari, Sonia Ambrogi, Tim J. Anderson, Ítalo Karmann Aventurato, Fernando Cendes, Yao-Liang Chen, Valentina Ciullo, Phil Cook, John C. Dalrymple-Alford, Michiel F. Dirkx, Jason Druzgal, Hedley C. A. Emsley, Rachel Guimarães, Hamied A. Haroon, Rick C. Helmich, Michele T. Hu, Martin E. Johansson, Ho Bin Kim, Johannes C. Klein, Max Laansma, Katherine E. Lawrence, Christine Lochner, Clare Mackay, Corey T. McMillan, Tracy R. Melzer, Leila Nabulsi, Ben Newman, Peter Opriessnig, Laura M. Parkes, Clelia Pellicano, Fabrizio Piras, Federica Piras, Lukas Pirpamer, Toni L. Pitcher, Kathleen L. Poston, Annerine Roos, Lucas Scárdua Silva, Reinhold Schmidt, Petra Schwingenschuh, Marian Shahid-Besanti, Gianfranco Spalletta, Dan J. Stein, Sophia I. Thomopoulos, Duygu Tosun, Chih-Chien Tsai, Odile A. van den Heuvel, Eva van Heese, Daniela Vecchio, Julio E. Villalón-Reina, Chris Vriend, Jiun-Jie Wang, Yih-Ru Wu, Clarissa Lin Yasuda, Paul M. Thompson, Neda Jahanshad, Ysbrand van der Werf
Publikováno v:
npj Parkinson's Disease, Vol 10, Iss 1, Pp 1-12 (2024)
Abstract The progression of Parkinson’s disease (PD) is associated with microstructural alterations in neural pathways, contributing to both motor and cognitive decline. However, conflicting findings have emerged due to the use of heterogeneous met
Externí odkaz:
https://doaj.org/article/abdb13d4596d46599f5bc49288648bf9
Autor:
Euclid Collaboration, Leuzzi, L., Meneghetti, M., Angora, G., Metcalf, R. B., Moscardini, L., Rosati, P., Bergamini, P., Calura, F., Clément, B., Gavazzi, R., Gentile, F., Lochner, M., Grillo, C., Vernardos, G., Aghanim, N., Amara, A., Amendola, L., Andreon, S., Auricchio, N., Bardelli, S., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Courtois, H. M., Cropper, M., Da Silva, A., Degaudenzi, H., Dinis, J., Dubath, F., Dupac, X., Dusini, S., Farina, M., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Joachimi, B., Kümmel, M., Keihänen, E., Kermiche, S., Kiessling, A., Kitching, T., Kunz, M., Kurki-Suonio, H., Lilje, P. B., Lindholm, V., Lloro, I., Maino, D., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinet, N., Marulli, F., Massey, R., Medinaceli, E., Mei, S., Melchior, M., Mellier, Y., Merlin, E., Meylan, G., Moresco, M., Munari, E., Niemi, S. -M., Nightingale, J. W., Nutma, T., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schirmer, M., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valenziano, L., Vassallo, T., Veropalumbo, A., Wang, Y., Weller, J., Zamorani, G., Zoubian, J., Zucca, E., Boucaud, A., Bozzo, E., Colodro-Conde, C., Di Ferdinando, D., Farinelli, R., Graciá-Carpio, J., Mauri, N., Neissner, C., Scottez, V., Tenti, M., Tramacere, A., Akrami, Y., Allevato, V., Baccigalupi, C., Ballardini, M., Bernardeau, F., Biviano, A., Borgani, S., Borlaff, A. S., Bretonnière, H., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Castignani, G., Castro, T., Chambers, K. C., Cooray, A. R., Coupon, J., Davini, S., de la Torre, S., De Lucia, G., Desprez, G., Di Domizio, S., Dole, H., Vigo, J. A. Escartin, Escoffier, S., Ferrero, I., Gabarra, L., Ganga, K., Garcia-Bellido, J., Gaztanaga, E., George, K., Gozaliasl, G., Hildebrandt, H., Huertas-Company, M., Kajava, J. J. E., Kansal, V., Kirkpatrick, C. C., Legrand, L., Loureiro, A., Magliocchetti, M., Mainetti, G., Maoli, R., Martinelli, M., Martins, C. J. A. P., Matthew, S., Maurin, L., Monaco, P., Morgante, G., Nadathur, S., Nucita, A. A., Pöntinen, M., Patrizii, L., Popa, V., Porciani, C., Potter, D., Reimberg, P., Sánchez, A. G., Sakr, Z., Schneider, A., Sereno, M., Simon, P., Mancini, A. Spurio, Stadel, J., Steinwagner, J., Teyssier, R., Valiviita, J., Viel, M., Zinchenko, I. A., Sánchez, H. Domínguez
Publikováno v:
Astronomy & Astrophysics,2024, 681, A68
Forthcoming imaging surveys will potentially increase the number of known galaxy-scale strong lenses by several orders of magnitude. For this to happen, images of tens of millions of galaxies will have to be inspected to identify potential candidates
Externí odkaz:
http://arxiv.org/abs/2307.08736
Autor:
Lo, Kyle, Chang, Joseph Chee, Head, Andrew, Bragg, Jonathan, Zhang, Amy X., Trier, Cassidy, Anastasiades, Chloe, August, Tal, Authur, Russell, Bragg, Danielle, Bransom, Erin, Cachola, Isabel, Candra, Stefan, Chandrasekhar, Yoganand, Chen, Yen-Sung, Cheng, Evie Yu-Yen, Chou, Yvonne, Downey, Doug, Evans, Rob, Fok, Raymond, Hu, Fangzhou, Huff, Regan, Kang, Dongyeop, Kim, Tae Soo, Kinney, Rodney, Kittur, Aniket, Kang, Hyeonsu, Klevak, Egor, Kuehl, Bailey, Langan, Michael, Latzke, Matt, Lochner, Jaron, MacMillan, Kelsey, Marsh, Eric, Murray, Tyler, Naik, Aakanksha, Nguyen, Ngoc-Uyen, Palani, Srishti, Park, Soya, Paulic, Caroline, Rachatasumrit, Napol, Rao, Smita, Sayre, Paul, Shen, Zejiang, Siangliulue, Pao, Soldaini, Luca, Tran, Huy, van Zuylen, Madeleine, Wang, Lucy Lu, Wilhelm, Christopher, Wu, Caroline, Yang, Jiangjiang, Zamarron, Angele, Hearst, Marti A., Weld, Daniel S.
Scholarly publications are key to the transfer of knowledge from scholars to others. However, research papers are information-dense, and as the volume of the scientific literature grows, the need for new technology to support the reading process grow
Externí odkaz:
http://arxiv.org/abs/2303.14334