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1. A Gigaparsec-Scale Hydrodynamic Volume Reconstructed with Deep Learning

Author

Jacobus, Cooper, Chabanier, Solene, Harrington, Peter, Emberson, JD, Lukić, Zarija, and Habib, Salman

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

The next generation of cosmological spectroscopic sky surveys will probe the distribution of matter across several Gigaparsecs (Gpc) or many billion light-years. In order to leverage the rich data in these new maps to gain a better understanding of the physics that shapes the large-scale structure of the cosmos, observed matter distributions must be compared to simulated mock skies. Small mock skies can be produced using precise, physics-driven hydrodynamical simulations. However, the need to capture small, kpc-scale density fluctuations in the intergalactic medium (IGM) places tight restrictions on the necessary minimum resolution of these simulations. Even on the most powerful supercomputers, it is impossible to run simulations of such high resolution in volumes comparable to what will be probed by future surveys, due to the vast quantity of data needed to store such a simulation in computer memory. However, it is possible to represent the essential features of these high-resolution simulations using orders of magnitude less memory. We present a hybrid approach that employs a physics-driven hydrodynamical simulation at a much lower-than-necessary resolution, followed by a data-driven, deep-learning Enhancement. This hybrid approach allows us to produce hydrodynamic mock skies that accurately capture small, kpc-scale features in the IGM but which span hundreds of Megaparsecs. We have produced such a volume which is roughly one Gigaparsec in diameter and examine its relevant large-scale statistical features, emphasizing certain properties that could not be captured by previous smaller simulations. We present this hydrodynamic volume as well as a companion n-body dark matter simulation and halo catalog which we are making publically available to the community for use in calibrating data pipelines for upcoming survey analyses.

Published
2024

2. Searching for Tidal Orbital Decay in Hot Jupiters

Author

Alvarado III, Efrain, Bostow, Kate B., Patra, Kishore C., Jacobus, Cooper H., Baer-Way, Raphael A., Jennings, Connor F., Pichay, Neil R., deGraw, Asia A., Vidal, Edgar P., Chander, Vidhi, Altunin, Ivan A., Brendel, Victoria M., Ehrich, Kingsley E., Sunseri, James D., May, Michael B., Punjabi, Druv H., Gendreau-Distler, Eli A., Risin, Sophia, Brink, Thomas G., Zheng, WeiKang, and Filippenko, Alexei V.

Subjects
Astrophysics - Earth and Planetary Astrophysics
Abstract

We study transits of several ``hot Jupiter'' systems - including WASP-12 b, WASP-43 b, WASP-103 b, HAT-P-23 b, KELT-16 b, WD 1856+534 b, and WTS-2 b - with the goal of detecting tidal orbital decay and extending the baselines of transit times. We find no evidence of orbital decay in any of the observed systems except for that of the extensively studied WASP-12 b. Although the orbit of WASP-12 b is unequivocally decaying, we find no evidence for acceleration of said orbital decay, with measured $\ddot{P} = (-7 \pm 8) \times 10^{-14} \rm ~s^{-1}$, against the expected acceleration decay of $\ddot{P} \approx -10^{-23} \rm ~s^{-1}$. In the case of WD 1856+534 b, there is a tentative detection of orbital growth with $\dot{P} = (5.0 \pm 1.5) \times 10^{-10}$. While statistically significant, we err on the side of caution and wait for longer follow-up observations to consider the measured $\dot{P}$ real. For most systems, we provide a 95\%-confidence lower limit on the tidal quality factor, $Q_\star'$. The possibility of detecting orbital decay in hot Jupiters via long-term radial velocity (RV) measurements is also explored. We find that $\sim 1 \rm ~m~s^{-1}$ precision in RVs will be required to detect orbital decay of WASP-12 b with only 3 yr of observations. Currently available RV measurements and precision are unable to detect orbital decay in any of the systems studied here., Comment: 14 pages, 11 figures; Accepted in MNRAS on 2024 August 30. Received 2024 August 29; in original form 2024 February 13

Published
2024

3. Reconstructing Lyman-$\alpha$ Fields from Low-Resolution Hydrodynamical Simulations with Deep Learning

Author

Jacobus, Cooper, Harrington, Peter, and Lukić, Zarija

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

Hydrodynamical cosmological simulations are a powerful tool for accurately predicting the properties of the intergalactic medium (IGM) and for producing mock skies that can be compared against observational data. However, the need to resolve density fluctuation in the IGM puts a stringent requirement on the resolution of such simulations which in turn limits the volumes which can be modelled, even on most powerful supercomputers. In this work, we present a novel modeling method which combines physics-driven simulations with data-driven generative neural networks to produce outputs that are qualitatively and statistically close to the outputs of hydrodynamical simulations employing 8 times higher resolution. We show that the Ly-$\alpha$ flux field, as well as the underlying hydrodynamic fields, have greatly improved statistical fidelity over a low-resolution simulation. Importantly, the design of our neural network allows for sampling multiple realizations from a given input, enabling us to quantify the model uncertainty. Using test data, we demonstrate that this model uncertainty correlates well with the true error of the Ly-$\alpha$ flux prediction. Ultimately, our approach allows for training on small simulation volumes and applying it to much larger ones, opening the door to producing accurate Ly-$\alpha$ mock skies in volumes of Hubble size, as will be probed with DESI and future spectroscopic sky surveys., Comment: 13 pages, 11 figures

Published
2023

4. Snowmass 2021 CMB-S4 White Paper

Author

Abazajian, Kevork, Abdulghafour, Arwa, Addison, Graeme E., Adshead, Peter, Ahmed, Zeeshan, Ajello, Marco, Akerib, Daniel, Allen, Steven W., Alonso, David, Alvarez, Marcelo, Amin, Mustafa A., Amiri, Mandana, Anderson, Adam, Ansarinejad, Behzad, Archipley, Melanie, Arnold, Kam S., Ashby, Matt, Aung, Han, Baccigalupi, Carlo, Baker, Carina, Bakshi, Abhishek, Bard, Debbie, Barkats, Denis, Barron, Darcy, Barry, Peter S., Bartlett, James G., Barton, Paul, Thakur, Ritoban Basu, Battaglia, Nicholas, Beall, Jim, Bean, Rachel, Beck, Dominic, Belkner, Sebastian, Benabed, Karim, Bender, Amy N., Benson, Bradford A., Besuner, Bobby, Bethermin, Matthieu, Bhimani, Sanah, Bianchini, Federico, Biquard, Simon, Birdwell, Ian, Bischoff, Colin A., Bleem, Lindsey, Bocaz, Paulina, Bock, James J., Bocquet, Sebastian, Boddy, Kimberly K., Bond, J. Richard, Borrill, Julian, Bouchet, Francois R., Brinckmann, Thejs, Brown, Michael L., Bryan, Sean, Buza, Victor, Byrum, Karen, Calabrese, Erminia, Calafut, Victoria, Caldwell, Robert, Carlstrom, John E., Carron, Julien, Cecil, Thomas, Challinor, Anthony, Chan, Victor, Chang, Clarence L., Chapman, Scott, Charles, Eric, Chauvin, Eric, Cheng, Cheng, Chesmore, Grace, Cheung, Kolen, Chinone, Yuji, Chluba, Jens, Cho, Hsiao-Mei Sherry, Choi, Steve, Clancy, Justin, Clark, Susan, Cooray, Asantha, Coppi, Gabriele, Corlett, John, Coulton, Will, Crawford, Thomas M., Crites, Abigail, Cukierman, Ari, Cyr-Racine, Francis-Yan, Dai, Wei-Ming, Daley, Cail, Dart, Eli, Daues, Gregorg, de Haan, Tijmen, Deaconu, Cosmin, Delabrouille, Jacques, Derylo, Greg, Devlin, Mark, Di Valentino, Eleonora, Dierickx, Marion, Dober, Brad, Doriese, Randy, Duff, Shannon, Dutcher, Daniel, Dvorkin, Cora, Dünner, Rolando, Eftekhari, Tarraneh, Eimer, Joseph, Bouhargani, Hamza El, Elleflot, Tucker, Emerson, Nick, Errard, Josquin, Essinger-Hileman, Thomas, Fabbian, Giulio, Fanfani, Valentina, Fasano, Alessandro, Feng, Chang, Ferraro, Simone, Filippini, Jeffrey P., Flauger, Raphael, Flaugher, Brenna, Fraisse, Aurelien A., Frisch, Josef, Frolov, Andrei, Galitzki, Nicholas, Gallardo, Patricio A., Galli, Silvia, Ganga, Ken, Gerbino, Martina, Giannakopoulos, Christos, Gilchriese, Murdock, Gluscevic, Vera, Goeckner-Wald, Neil, Goldfinger, David, Green, Daniel, Grimes, Paul, Grin, Daniel, Grohs, Evan, Gualtieri, Riccardo, Guarino, Vic, Gudmundsson, Jon E., Gullett, Ian, Guns, Sam, Habib, Salman, Haller, Gunther, Halpern, Mark, Halverson, Nils W., Hanany, Shaul, Hand, Emma, Harrington, Kathleen, Hasegawa, Masaya, Hasselfield, Matthew, Hazumi, Masashi, Heitmann, Katrin, Henderson, Shawn, Hensley, Brandon, Herbst, Ryan, Hervias-Caimapo, Carlos, Hill, J. Colin, Hills, Richard, Hivon, Eric, Hlozek, Renée, Ho, Anna, Holder, Gil, Hollister, Matt, Holzapfel, William, Hood, John, Hotinli, Selim, Hryciuk, Alec, Hubmayr, Johannes, Huffenberger, Kevin M., Hui, Howard, nez, Roberto Ibá, Ibitoye, Ayodeji, Ikape, Margaret, Irwin, Kent, Jacobus, Cooper, Jeong, Oliver, Johnson, Bradley R., Johnstone, Doug, Jones, William C., Joseph, John, Jost, Baptiste, Kang, Jae Hwan, Kaplan, Ari, Karkare, Kirit S., Katayama, Nobuhiko, Keskitalo, Reijo, King, Cesiley, Kisner, Theodore, Klein, Matthias, Knox, Lloyd, Koopman, Brian J., Kosowsky, Arthur, Kovac, John, Kovetz, Ely D., Krolewski, Alex, Kubik, Donna, Kuhlmann, Steve, Kuo, Chao-Lin, Kusaka, Akito, Lähteenmäki, Anne, Lau, Kenny, Lawrence, Charles R., Lee, Adrian T., Legrand, Louis, Leitner, Matthaeus, Leloup, Clément, Lewis, Antony, Li, Dale, Linder, Eric, Liodakis, Ioannis, Liu, Jia, Long, Kevin, Louis, Thibaut, Loverde, Marilena, Lowry, Lindsay, Lu, Chunyu, Lubin, Phil, Ma, Yin-Zhe, Maccarone, Thomas, Madhavacheril, Mathew S., Maldonado, Felipe, Mantz, Adam, Marques, Gabriela, Matsuda, Frederick, Mauskopf, Philip, May, Jared, McCarrick, Heather, McCracken, Ken, McMahon, Jeffrey, Meerburg, P. Daniel, Melin, Jean-Baptiste, Menanteau, Felipe, Meyers, Joel, Millea, Marius, Miranda, Vivian, Mitchell, Don, Mohr, Joseph, Moncelsi, Lorenzo, Monzani, Maria Elena, Moshed, Magdy, Mroczkowski, Tony, Mukherjee, Suvodip, Münchmeyer, Moritz, Nagai, Daisuke, Nagarajappa, Chandan, Nagy, Johanna, Namikawa, Toshiya, Nati, Federico, Natoli, Tyler, Nerval, Simran, Newburgh, Laura, Nguyen, Hogan, Nichols, Erik, Nicola, Andrina, Niemack, Michael D., Nord, Brian, Norton, Tim, Novosad, Valentine, O'Brient, Roger, Omori, Yuuki, Orlando, Giorgio, Osherson, Benjamin, Osten, Rachel, Padin, Stephen, Paine, Scott, Partridge, Bruce, Patil, Sanjaykumar, Petravick, Don, Petroff, Matthew, Pierpaoli, Elena, Pilleux, Mauricio, Pogosian, Levon, Prabhu, Karthik, Pryke, Clement, Puglisi, Giuseppe, Racine, Benjamin, Raghunathan, Srinivasan, Rahlin, Alexandra, Raveri, Marco, Reese, Ben, Reichardt, Christian L., Remazeilles, Mathieu, Rizzieri, Arianna, Rocha, Graca, Roe, Natalie A., Rotermund, Kaja, Roy, Anirban, Ruhl, John E., Saba, Joe, Sailer, Noah, Salatino, Maria, Saliwanchik, Benjamin, Sapozhnikov, Leonid, Rao, Mayuri Sathyanarayana, Saunders, Lauren, Schaan, Emmanuel, Schillaci, Alessandro, Schmitt, Benjamin, Scott, Douglas, Sehgal, Neelima, Shandera, Sarah, Sherwin, Blake D., Shirokoff, Erik, Shiu, Corwin, Simon, Sara M., Singari, Baibhav, Slosar, Anze, Spergel, David, Germaine, Tyler St., Staggs, Suzanne T., Stark, Antony A., Starkman, Glenn D., Steinbach, Bryan, Stompor, Radek, Stoughton, Chris, Suzuki, Aritoki, Tajima, Osamu, Tandoi, Chris, Teply, Grant P., Thayer, Gregg, Thompson, Keith, Thorne, Ben, Timbie, Peter, Tomasi, Maurizio, Trendafilova, Cynthia, Tristram, Matthieu, Tucker, Carole, Tucker, Gregory, Umiltà, Caterina, van Engelen, Alexander, van Marrewijk, Joshiwa, Vavagiakis, Eve M., Vergès, Clara, Vieira, Joaquin D., Vieregg, Abigail G., Wagoner, Kasey, Wallisch, Benjamin, Wang, Gensheng, Wang, Guo-Jian, Watson, Scott, Watts, Duncan, Weaver, Chris, Wenzl, Lukas, Westbrook, Ben, White, Martin, Whitehorn, Nathan, Wiedlea, Andrew, Williams, Paul, Wilson, Robert, Winch, Harrison, Wollack, Edward J., Wu, W. L. Kimmy, Xu, Zhilei, Yefremenko, Volodymyr G., Yu, Cyndia, Zegeye, David, Zivick, Jeff, and Zonca, Andrea

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, General Relativity and Quantum Cosmology, High Energy Physics - Experiment, High Energy Physics - Phenomenology
Abstract

This Snowmass 2021 White Paper describes the Cosmic Microwave Background Stage 4 project CMB-S4, which is designed to cross critical thresholds in our understanding of the origin and evolution of the Universe, from the highest energies at the dawn of time through the growth of structure to the present day. We provide an overview of the science case, the technical design, and project plan., Comment: Contribution to Snowmass 2021. arXiv admin note: substantial text overlap with arXiv:1908.01062, arXiv:1907.04473

Published
2022

5. Machine-Learning enabled analysis of ELM filament dynamics in KSTAR

Author

Jacobus, Cooper, Choi, Minjun J., and Kube, Ralph

Subjects
Physics - Plasma Physics, Condensed Matter - Disordered Systems and Neural Networks, Computer Science - Artificial Intelligence, Physics - Instrumentation and Detectors
Abstract

The emergence and dynamics of filamentary structures associated with edge-localized modes (ELMs) inside tokamak plasmas during high-confinement mode is regularly studied using Electron Cyclotron Emission Imaging (ECEI) diagnostic systems. ECEI allows inference of electron temperature variations, often across a poloidal cross-section. Previously, detailed analyses of filamentary dynamics and classification of the precursors to ELM crashes have been done manually. We present a machine-learning-based model, capable of automatically identifying the position, spatial extent, and amplitude of ELM filaments. The model is a deep convolutional neural network that has been trained and optimized on an extensive set of manually labeled ECEI data from the KSTAR tokamak. Once trained, the model achieves a 93.7% precision and allows to robustly identify plasma filaments in unseen ECEI data. The trained model is used to characterize ELM filament dynamics in a single H-mode plasma shot. We identify quasi-periodic oscillations of the filaments' size, total heat content, and radial velocity. The detailed dynamics of these quantities appear strongly correlated with each other and appear qualitatively different during the pre-crash and ELM crash phases., Comment: 25 pages, 13 figures

Published
2022

6. Searching for tidal orbital decay in hot Jupiters.

Author

Alvarado, Efrain, Bostow, Kate B, Patra, Kishore C, Jacobus, Cooper H, Baer-Way, Raphael A, Jennings, Connor F, Pichay, Neil R, deGraw, Asia A, Vidal, Edgar P, Chander, Vidhi, Altunin, Ivan A, Brendel, Victoria M, Ehrich, Kingsley E, Sunseri, James D, May, Michael B, Punjabi, Druv H, Gendreau-Distler, Eli A, Risin, Sophia, Brink, Thomas G, and Zheng, WeiKang

Subjects
HOT Jupiters, CONFIDENCE intervals, QUALITY factor, NATURAL satellites, ORBITS (Astronomy)
Abstract

We study transits of several 'hot Jupiter' systems, including WASP-12 b, WASP-43 b, WASP-103 b, HAT-P-23 b, KELT-16 b, WD 1856+534 b, and WTS-2 b, with the goal of detecting tidal orbital decay and extending the baselines of transit times. We find no evidence of orbital decay in any of the observed systems except for that of the extensively studied WASP-12 b. Although the orbit of WASP-12 b is unequivocally decaying, we find no evidence for acceleration of said orbital decay, with measured |$\ddot{P} = (-7 \pm 8) \times 10^{-14} \, \rm s^{-1}$|⁠ , against the expected acceleration decay of |$\ddot{P} \approx -10^{-23} \, \rm s^{-1}$|⁠. In the case of WD 1856+534 b, there is a tentative detection of orbital growth with |$\dot{P} = (5.0 \pm 1.5) \times 10^{-10}$|⁠. While statistically significant, we err on the side of caution and wait for longer follow-up observations to consider the measured |$\dot{P}$| real. For most systems, we provide a 95 per cent confidence lower limit on the tidal quality factor, |$Q_\star ^{\prime }$|⁠. The possibility of detecting orbital decay in hot Jupiters via long-term radial velocity (RV) measurements is also explored. We find that |${\sim} 1 \rm \, m \, s^{-1}$| precision in RVs will be required to detect orbital decay of WASP-12 b with only 3 yr of observations. Currently available RV measurements and precision are unable to detect orbital decay in any of the systems studied here. [ABSTRACT FROM AUTHOR]

Published
2024
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8. Optical Observations of the Type Ia Supernova 2022hrs

Author

Risin, Sophia, primary, Jacobus, Cooper, additional, Altunin, Ivan, additional, Brink, Thomas G., additional, Patra, Kishore C., additional, Zheng, WeiKang, additional, Yang, Yi, additional, Pichay, Neil, additional, Fitzgerald, Michael, additional, Boyce, Grady, additional, Boyce, Pat, additional, and Filippenko, Alexei V., additional

Published
2023
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9. Reconstructing Ly α Fields from Low-resolution Hydrodynamical Simulations with Deep Learning.

Author

Jacobus, Cooper, Harrington, Peter, and Lukić, Zarija

Subjects
*DEEP learning, *SUPERCOMPUTERS, *INTERSTELLAR medium, *ASTRONOMICAL surveys, *LARGE scale structure (Astronomy), *CONVOLUTIONAL neural networks
Abstract

Hydrodynamical cosmological simulations are a powerful tool for accurately predicting the properties of the intergalactic medium (IGM) and for producing mock skies that can be compared against observational data. However, the need to resolve density fluctuation in the IGM puts a stringent requirement on the resolution of such simulations, which in turn limits the volumes that can be modeled, even on the most powerful supercomputers. In this work, we present a novel modeling method that combines physics-driven simulations with data-driven generative neural networks to produce outputs that are qualitatively and statistically close to the outputs of hydrodynamical simulations employing eight times higher resolution. We show that the Ly α flux field, as well as the underlying hydrodynamic fields, have greatly improved statistical fidelity over a low-resolution simulation. Importantly, the design of our neural network allows for sampling multiple realizations from a given input, enabling us to quantify the model uncertainty. Using test data, we demonstrate that this model uncertainty correlates well with the true error of the Ly α flux prediction. Ultimately, our approach allows for training on small simulation volumes and applying it to much larger ones, opening the door to producing accurate Ly α mock skies in volumes of Hubble size, as will be probed with DESI and future spectroscopic sky surveys. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
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