Neural Network Prediction of Strong Lensing Systems with Domain Adaptation and Uncertainty Quantification

Autor: Agarwal, Shrihan, Ćiprijanović, Aleksandra, Nord, Brian D.

Modeling strong gravitational lenses is computationally expensive for the complex data from modern and next-generation cosmic surveys. Deep learning has emerged as a promising approach for finding lenses and predicting lensing parameters, such as the

Externí odkaz: http://arxiv.org/abs/2411.03334

Movie Gen: A Cast of Media Foundation Models

Autor: Polyak, Adam, Zohar, Amit, Brown, Andrew, Tjandra, Andros, Sinha, Animesh, Lee, Ann, Vyas, Apoorv, Shi, Bowen, Ma, Chih-Yao, Chuang, Ching-Yao, Yan, David, Choudhary, Dhruv, Wang, Dingkang, Sethi, Geet, Pang, Guan, Ma, Haoyu, Misra, Ishan, Hou, Ji, Wang, Jialiang, Jagadeesh, Kiran, Li, Kunpeng, Zhang, Luxin, Singh, Mannat, Williamson, Mary, Le, Matt, Yu, Matthew, Singh, Mitesh Kumar, Zhang, Peizhao, Vajda, Peter, Duval, Quentin, Girdhar, Rohit, Sumbaly, Roshan, Rambhatla, Sai Saketh, Tsai, Sam, Azadi, Samaneh, Datta, Samyak, Chen, Sanyuan, Bell, Sean, Ramaswamy, Sharadh, Sheynin, Shelly, Bhattacharya, Siddharth, Motwani, Simran, Xu, Tao, Li, Tianhe, Hou, Tingbo, Hsu, Wei-Ning, Yin, Xi, Dai, Xiaoliang, Taigman, Yaniv, Luo, Yaqiao, Liu, Yen-Cheng, Wu, Yi-Chiao, Zhao, Yue, Kirstain, Yuval, He, Zecheng, He, Zijian, Pumarola, Albert, Thabet, Ali, Sanakoyeu, Artsiom, Mallya, Arun, Guo, Baishan, Araya, Boris, Kerr, Breena, Wood, Carleigh, Liu, Ce, Peng, Cen, Vengertsev, Dimitry, Schonfeld, Edgar, Blanchard, Elliot, Juefei-Xu, Felix, Nord, Fraylie, Liang, Jeff, Hoffman, John, Kohler, Jonas, Fire, Kaolin, Sivakumar, Karthik, Chen, Lawrence, Yu, Licheng, Gao, Luya, Georgopoulos, Markos, Moritz, Rashel, Sampson, Sara K., Li, Shikai, Parmeggiani, Simone, Fine, Steve, Fowler, Tara, Petrovic, Vladan, Du, Yuming

We present Movie Gen, a cast of foundation models that generates high-quality, 1080p HD videos with different aspect ratios and synchronized audio. We also show additional capabilities such as precise instruction-based video editing and generation of

Externí odkaz: http://arxiv.org/abs/2410.13720

Population-level Dark Energy Constraints from Strong Gravitational Lensing using Simulation-Based Inference

Autor: Jarugula, Sreevani, Nord, Brian, Gandrakota, Abhijith, Ćiprijanović, Aleksandra

In this work, we present a scalable approach for inferring the dark energy equation-of-state parameter ($w$) from a population of strong gravitational lens images using Simulation-Based Inference (SBI). Strong gravitational lensing offers crucial ins

Externí odkaz: http://arxiv.org/abs/2407.17292

DeepSurveySim: Simulation Software and Benchmark Challenges for Astronomical Observation Scheduling

Autor: Voetberg, Maggie, Nord, Brian

Modern astronomical surveys have multiple competing scientific goals. Optimizing the observation schedule for these goals presents significant computational and theoretical challenges, and state-of-the-art methods rely on expensive human inspection o

Externí odkaz: http://arxiv.org/abs/2312.09092

Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers

Autor: Huppenkothen, D., Ntampaka, M., Ho, M., Fouesneau, M., Nord, B., Peek, J. E. G., Walmsley, M., Wu, J. F., Avestruz, C., Buck, T., Brescia, M., Finkbeiner, D. P., Goulding, A. D., Kacprzak, T., Melchior, P., Pasquato, M., Ramachandra, N., Ting, Y. -S., van de Ven, G., Villar, S., Villar, V. A., Zinger, E.

Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of transients to neural network emulators of cosmological simulations, a

Externí odkaz: http://arxiv.org/abs/2310.12528