Zobrazeno 1 - 10
of 111
pro vyhledávání: '"Pürrer, Michael"'
Autor:
Dax, Maximilian, Green, Stephen R., Gair, Jonathan, Gupte, Nihar, Pürrer, Michael, Raymond, Vivien, Wildberger, Jonas, Macke, Jakob H., Buonanno, Alessandra, Schölkopf, Bernhard
Mergers of binary neutron stars (BNSs) emit signals in both the gravitational-wave (GW) and electromagnetic (EM) spectra. Famously, the 2017 multi-messenger observation of GW170817 led to scientific discoveries across cosmology, nuclear physics, and
Externí odkaz:
http://arxiv.org/abs/2407.09602
Autor:
Gupte, Nihar, Ramos-Buades, Antoni, Buonanno, Alessandra, Gair, Jonathan, Miller, M. Coleman, Dax, Maximilian, Green, Stephen R., Pürrer, Michael, Wildberger, Jonas, Macke, Jakob, Romero-Shaw, Isobel M., Schölkopf, Bernhard
Binary black holes (BBHs) in eccentric orbits produce distinct modulations the emitted gravitational waves (GWs). The measurement of orbital eccentricity can provide robust evidence for dynamical binary formation channels. We analyze 57 GW events fro
Externí odkaz:
http://arxiv.org/abs/2404.14286
Autor:
Agarwal, Manan, Alameda, Jay, Audenaert, Jeroen, Benoit, Will, Beveridge, Damon, Bhattacharya, Meghna, Chatterjee, Chayan, Chatterjee, Deep, Chen, Andy, Cholayil, Muhammed Saleem, Chou, Chia-Jui, Choudhary, Sunil, Coughlin, Michael, Dax, Maximilian, Desai, Aman, Di Luca, Andrea, Duarte, Javier Mauricio, Farrell, Steven, Feng, Yongbin, Goodarzi, Pooyan, Govorkova, Ekaterina, Graham, Matthew, Guiang, Jonathan, Gunny, Alec, Guo, Weichangfeng, Hakenmueller, Janina, Hawks, Ben, Hsu, Shih-Chieh, Jawahar, Pratik, Ju, Xiangyang, Katsavounidis, Erik, Kellis, Manolis, Khoda, Elham E, Lahbabi, Fatima Zahra, Lian, Van Tha Bik, Liu, Mia, Malanchev, Konstantin, Marx, Ethan, McCormack, William Patrick, McLeod, Alistair, Mo, Geoffrey, Moreno, Eric Anton, Muthukrishna, Daniel, Narayan, Gautham, Naylor, Andrew, Neubauer, Mark, Norman, Michael, Omer, Rafia, Pedro, Kevin, Peterson, Joshua, Pürrer, Michael, Raikman, Ryan, Raj, Shivam, Ricker, George, Robbins, Jared, Samani, Batool Safarzadeh, Scholberg, Kate, Schuy, Alex, Skliris, Vasileios, Soni, Siddharth, Sravan, Niharika, Sutton, Patrick, Villar, Victoria Ashley, Wang, Xiwei, Wen, Linqing, Wuerthwein, Frank, Yang, Tingjun, Yeh, Shu-Wei
Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing these datasets requires both sufficient compute power and efficient w
Externí odkaz:
http://arxiv.org/abs/2306.08106
Autor:
Mihaylov, Deyan P., Ossokine, Serguei, Buonanno, Alessandra, Estelles, Hector, Pompili, Lorenzo, Pürrer, Michael, Ramos-Buades, Antoni
We present pySEOBNR, a Python package for gravitational-wave (GW) modeling developed within the effective-one-body (EOB) formalism. The package contains an extensive framework to generate state-of-the-art inspiral-merger-ringdown waveform models for
Externí odkaz:
http://arxiv.org/abs/2303.18203
Autor:
Pompili, Lorenzo, Buonanno, Alessandra, Estellés, Héctor, Khalil, Mohammed, van de Meent, Maarten, Mihaylov, Deyan P., Ossokine, Serguei, Pürrer, Michael, Ramos-Buades, Antoni, Mehta, Ajit Kumar, Cotesta, Roberto, Marsat, Sylvain, Boyle, Michael, Kidder, Lawrence E., Pfeiffer, Harald P., Scheel, Mark A., Rüter, Hannes R., Vu, Nils, Dudi, Reetika, Ma, Sizheng, Mitman, Keefe, Melchor, Denyz, Thomas, Sierra, Sanchez, Jennifer
We present SEOBNRv5HM, a more accurate and faster inspiral-merger-ringdown gravitational waveform model for quasi-circular, spinning, nonprecessing binary black holes within the effective-one-body (EOB) formalism. Compared to its predecessor, SEOBNRv
Externí odkaz:
http://arxiv.org/abs/2303.18039
Autor:
Wildberger, Jonas, Dax, Maximilian, Green, Stephen R., Gair, Jonathan, Pürrer, Michael, Macke, Jakob H., Buonanno, Alessandra, Schölkopf, Bernhard
Deep learning techniques for gravitational-wave parameter estimation have emerged as a fast alternative to standard samplers $\unicode{x2013}$ producing results of comparable accuracy. These approaches (e.g., DINGO) enable amortized inference by trai
Externí odkaz:
http://arxiv.org/abs/2211.08801
Autor:
Dax, Maximilian, Green, Stephen R., Gair, Jonathan, Pürrer, Michael, Wildberger, Jonas, Macke, Jakob H., Buonanno, Alessandra, Schölkopf, Bernhard
Publikováno v:
Phys. Rev. Lett. 130, 171403 (2023)
We combine amortized neural posterior estimation with importance sampling for fast and accurate gravitational-wave inference. We first generate a rapid proposal for the Bayesian posterior using neural networks, and then attach importance weights base
Externí odkaz:
http://arxiv.org/abs/2210.05686
We present a surrogate model of \texttt{SEOBNRv4PHM}, a fully precessing time-domain effective-one-body waveform model including subdominant modes. We follow an approach similar to that used to build recent numerical relativity surrogate models. Our
Externí odkaz:
http://arxiv.org/abs/2203.00381
Autor:
Kalogera, Vicky, Sathyaprakash, B. S., Bailes, Matthew, Bizouard, Marie-Anne, Buonanno, Alessandra, Burrows, Adam, Colpi, Monica, Evans, Matt, Fairhurst, Stephen, Hild, Stefan, Kasliwal, Mansi M., Lehner, Luis, Mandel, Ilya, Mandic, Vuk, Nissanke, Samaya, Papa, Maria Alessandra, Reddy, Sanjay, Rosswog, Stephan, Broeck, Chris Van Den, Ajith, P., Anand, Shreya, Andreoni, Igor, Arun, K. G., Barausse, Enrico, Baryakhtar, Masha, Belgacem, Enis, Berry, Christopher P. L., Bertacca, Daniele, Brito, Richard, Caprini, Chiara, Chatziioannou, Katerina, Coughlin, Michael, Cusin, Giulia, Dietrich, Tim, Dirian, Yves, East, William E., Fan, Xilong, Figueroa, Daniel, Foffa, Stefano, Ghosh, Archisman, Hall, Evan, Harms, Jan, Harry, Ian, Hinderer, Tanja, Janka, Thomas, Justham, Stephen, Kasen, Dan, Kotake, Kei, Lovelace, Geoffrey, Maggiore, Michele, Mangiagli, Alberto, Mapelli, Michela, Maselli, Andrea, Matas, Andrew, McIver, Jess, Messer, Bronson, Mezzacappa, Tony, Mills, Cameron, Mueller, Bernhard, Müller, Ewald, Pürrer, Michael, Pani, Paolo, Pratten, Geraint, Regimbau, Tania, Sakellariadou, Mairi, Schneider, Raffaella, Sesana, Alberto, Shao, Lijing, Sotiriou, P. Thomas, Tamanini, Nicola, Tauris, Thomas, Thrane, Eric, Valiante, Rosa, van de Meent, Maarten, Varma, Vijay, Vines, Justin, Vitale, Salvatore, Yang, Huan, Yunes, Nicolas, Zumalacarregui, Miguel, Punturo, Michele, Reitze, David, Couvares, Peter, Katsanevas, Stavros, Kajita, Takaaki, Lueck, Harald, McClelland, David, Rowan, Sheila, Sanders, Gary, Shoemaker, David, Brand, Jo van den
The next generation of ground-based gravitational-wave detectors will observe coalescences of black holes and neutron stars throughout the cosmos, thousands of them with exceptional fidelity. The Science Book is the result of a 3-year effort to study
Externí odkaz:
http://arxiv.org/abs/2111.06990
Autor:
Cuoco, Elena, Powell, Jade, Cavaglià, Marco, Ackley, Kendall, Bejger, Michal, Chatterjee, Chayan, Coughlin, Michael, Coughlin, Scott, Easter, Paul, Essick, Reed, Gabbard, Hunter, Gebhard, Timothy, Ghosh, Shaon, Haegel, Leila, Iess, Alberto, Keitel, David, Marka, Zsuzsa, Marka, Szabolcs, Morawski, Filip, Nguyen, Tri, Ormiston, Rich, Puerrer, Michael, Razzano, Massimiliano, Staats, Kai, Vajente, Gabriele, Williams, Daniel
Machine learning has emerged as a popular and powerful approach for solving problems in astrophysics. We review applications of machine learning techniques for the analysis of ground-based gravitational-wave detector data. Examples include techniques
Externí odkaz:
http://arxiv.org/abs/2005.03745