Zobrazeno 1 - 10
of 92 782
pro vyhledávání: '"Bird, A"'
The origin of the binary black hole mergers observed by LIGO-Virgo-KAGRA (LVK) remains an open question. We calculate the merger rate from primordial black holes (PBHs) within the density spike around supermassive black holes (SMBHs) at the center of
Externí odkaz:
http://arxiv.org/abs/2411.05065
The Milky Way Radial Metallicity Gradient as an Equilibrium Phenomenon: Why Old Stars are Metal-Rich
Autor:
Johnson, James W., Weinberg, David H., Blanc, Guillermo A., Bonaca, Ana, Rudie, Gwen C., Yuxi, Lu, Chu, Bronwyn Reichardt, Griffith, Emily J., Sit, Tawny, Johnson, Jennifer A., Dubay, Liam O., Weller, Miqaela K., Boyea, Daniel A., Bird, Jonathan C.
Metallicities of both gas and stars decline toward large radii in spiral galaxies, a trend known as the radial metallicity gradient. We quantify the evolution of the metallicity gradient in the Milky Way as traced by APOGEE red giants with age estima
Externí odkaz:
http://arxiv.org/abs/2410.13256
Autor:
Nahar, Nadia, Kästner, Christian, Butler, Jenna, Parnin, Chris, Zimmermann, Thomas, Bird, Christian
Large Language Models (LLMs) are increasingly embedded into software products across diverse industries, enhancing user experiences, but at the same time introducing numerous challenges for developers. Unique characteristics of LLMs force developers,
Externí odkaz:
http://arxiv.org/abs/2410.12071
Autor:
Bird, Samuel, Devescovi, Chiara, Engeler, Pascal, Valenti, Agnes, Gökmen, Doruk Efe, Worreby, Robin, Peri, Valerio, Huber, Sebastian D.
Designing topological materials with specific topological indices is a complex inverse problem, traditionally tackled through manual, intuition-driven methods that are neither scalable nor efficient for exploring the vast space of possible material c
Externí odkaz:
http://arxiv.org/abs/2410.10484
Autor:
Tillman, Megan Taylor, Burkhart, Blakesley, Tonnesen, Stephanie, Bird, Simeon, Bryan, Greg L.
We study the effects of AGN feedback on the Lyman-$\alpha$ forest 1D flux power spectrum (P1D). Using the Simba cosmological-hydrodynamic simulations, we examine the impact that adding different AGN feedback modes has on the predicted P1D. We find th
Externí odkaz:
http://arxiv.org/abs/2410.05383
Autor:
Cheng, Ti-Chung, Badea, Carmen, Bird, Christian, Zimmermann, Thomas, DeLine, Robert, Forsgren, Nicole, Ford, Denae
Across domains, metrics and measurements are fundamental to identifying challenges, informing decisions, and resolving conflicts. Despite the abundance of data available in this information age, not only can it be challenging for a single expert to w
Externí odkaz:
http://arxiv.org/abs/2410.00880
Autor:
Zhou, Yihao, Mukherjee, Diptajyoti, Chen, Nianyi, Di Matteo, Tiziana, Johansson, Peter H., Rantala, Antti, Partmann, Christian, Carlo, Ugo NiccolDi, Bird, Simeon, Ni, Yueying
MBH seed mergers are expected to be among the loudest sources of gravitational waves detected by the Laser Interferometer Space Antenna (LISA), providing a unique window into the birth and early growth of SMBH. We present the MAGICS-II simulation sui
Externí odkaz:
http://arxiv.org/abs/2409.19914
Autor:
Ni, Yueying, Chen, Nianyi, Zhou, Yihao, Park, Minjung, Yang, Yanhui, DiMatteo, Tiziana, Bird, Simeon, Croft, Rupert
We present new results from the ASTRID simulation from $z=3$ to $z=0.5$, covering the epoch of cosmic noon. The galaxy stellar mass function, as well as the black hole mass and luminosity functions in ASTRID, exhibit good agreement with recent observ
Externí odkaz:
http://arxiv.org/abs/2409.10666
Autor:
Kays, Roland, Snider, Matthew H., Hess, George, Cove, Michael V., Jensen, Alex, Shamon, Hila, McShea, William J., Rooney, Brigit, Allen, Maximilian L., Pekins, Charles E., Wilmers, Christopher C., Pendergast, Mary E., Green, Austin M., Suraci, Justin, Leslie, Matthew S., Nasrallah, Sophie, Farkas, Dan, Jordan, Mark, Grigione, Melissa, LaScaleia, Michael C., Davis, Miranda L., Hansen, Chris, Millspaugh, Josh, Lewis, Jesse S., Havrda, Michael, Long, Robert, Remine, Kathryn R., Jaspers, Kodi J., Lafferty, Diana J. R., Hubbard, Tru, Studds, Colin E., Barthelmess, Erika L., Andy, Katherine, Romero, Andrea, O'Neill, Brian J., Hawkins, Melissa T. R., Lombardi, Jason V., Sergeyev, Maksim, Fisher-Reid, M. Caitlin, Rentz, Michael S., Nagy, Christopher, Davenport, Jon M., Rega-Brodsky, Christine C., Appel, Cara L., Lesmeister, Damon B., Giery, Sean T., Whittier, Christopher A., Alston, Jesse M., Sutherland, Chris, Rota, Christopher, Murphy, Thomas, Lee, Thomas E., Mortelliti, Alessio, Bergman, Dylan L., Compton, Justin A., Gerber, Brian D., Burr, Jess, Rezendes, Kylie, DeGregorio, Brett A., Wehr, Nathaniel H., Benson, John F., O’Mara, M. Teague, Jachowski, David S., Gray, Morgan, Beyer, Dean E., Belant, Jerrold L., Horan, Robert V., Lonsinger, Robert C., Kuhn, Kellie M., Hasstedt, Steven C. M., Zimova, Marketa, Moore, Sophie M., Herrera, Daniel J., Fritts, Sarah, Edelman, Andrew J., Flaherty, Elizabeth A., Petroelje, Tyler R., Neiswenter, Sean A., Risch, Derek R., Iannarilli, Fabiola, van der Merwe, Marius, Maher, Sean P., Farris, Zach J., Webb, Stephen L., Mason, David S., Lashley, Marcus A., Wilson, Andrew M., Vanek, John P., Wehr, Samuel R., Conner, L. Mike, Beasley, James C., Bontrager, Helen L., Baruzzi, Carolina, Ellis-Felege, Susan N., Proctor, Mike D., Schipper, Jan, Weiss, Katherine C. B., Darracq, Andrea K., Barr, Evan G., Alexander, Peter D., Şekercioğlu, Çağan H., Bogan, Daniel A., Schalk, Christopher M., Fantle-Lepczyk, Jean E., Lepczyk, Christopher A., LaPoint, Scott, Whipple, Laura S., Rowe, Helen Ivy, Mullen, Kayleigh, Bird, Tori, Zorn, Adam, Brandt, LaRoy, Lathrop, Richard G., McCain, Craig, Crupi, Anthony P., Clark, James, Parsons, Arielle
Publikováno v:
Diversity and Distributions, 2024 Sep 01. 30(9), 1-16.
Externí odkaz:
https://www.jstor.org/stable/48784956
AI-assisted super-resolution cosmological simulations IV: An emulator for deterministic realizations
Autor:
Zhang, Xiaowen, Lachance, Patrick, Dasgupta, Ankita, Croft, Rupert A. C., Di Matteo, Tiziana, Ni, Yueying, Bird, Simeon, Li, Yin
Super-resolution (SR) models in cosmological simulations use deep learning (DL) to rapidly supplement low-resolution (LR) runs with statistically correct, fine details. The SR technique preserves large-scale structures by conditioning on a low-resolu
Externí odkaz:
http://arxiv.org/abs/2408.09051