Search

Your search keyword '"P Huff"' showing total 4,313 results
Start Over Author "P Huff"
4,313 results on '"P Huff"'

1. High-Significance Detection of Correlation Between the Unresolved Gamma-Ray Background and the Large Scale Cosmic Structure

Author

Thakore, B., Negro, M., Regis, M., Camera, S., Gruen, D., Fornengo, N., Roodman, A., Porredon, A., Schutt, T., Cuoco, A., Alarcon, A., Amon, A., Bechtol, K., Becker, M. R., Bernstein, G. M., Campos, A., Rosell, A. Carnero, Kind, M. Carrasco, Cawthon, R., Chang, C., Chen, R., Choi, A., Cordero, J., Davis, C., DeRose, J., Diehl, H. T., Dodelson, S., Doux, C., Drlica-Wagner, A., Eckert, K., Elvin-Poole, J., Everett, S., Ferté, A., Gatti, M., Giannini, G., Gruendl, R. A., Harrison, I., Hartley, W. G., Huff, E. M., Jarvis, M., Kuropatkin, N., Leget, P. -F., MacCrann, N., McCullough, J., Myles, J., Navarro-Alsina, A., Pandey, S., Prat, J., Raveri, M., Rollins, R. P., Ross, A. J., Rykoff, E. S., Sánchez, C., Secco, L. F., Sevilla-Noarbe, I., Sheldon, E., Shin, T., Troxel, M. A., Tutusaus, I., Yanny, B., Yin, B., Zhang, Y., Aguena, M., Brooks, D., Carretero, J., da Costa, L. N., Davis, T. M., De Vicente, J., Desai, S., Doel, P., Flaugher, B., Frieman, J., García-Bellido, J., Gaztanaga, E., Gutierrez, G., Hinton, S. R., Hollowood, D. L., Honscheid, K., James, D. J., Kuehn, K., Lahav, O., Lee, S., Lima, M., Marshall, J. L., Mena-Fernández, J., Miquel, R., Ogando, R. L. C., Palmese, A., Pieres, A., Malagón, A. A. Plazas, Samuroff, S., Sanchez, E., Cid, D. Sanchez, Smith, M., Suchyta, E., Tarle, G., Vikram, V., Walker, A. R., and Weaverdyck, N.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - High Energy Astrophysical Phenomena
Abstract

Our understanding of the $\gamma$-ray sky has improved dramatically in the past decade, however, the unresolved $\gamma$-ray background (UGRB) still has a potential wealth of information about the faintest $\gamma$-ray sources pervading the Universe. Statistical cross-correlations with tracers of cosmic structure can indirectly identify the populations that most characterize the $\gamma$-ray background. In this study, we analyze the angular correlation between the $\gamma$-ray background and the matter distribution in the Universe as traced by gravitational lensing, leveraging more than a decade of observations from the Fermi-Large Area Telescope (LAT) and 3 years of data from the Dark Energy Survey (DES). We detect a correlation at signal-to-noise ratio of 8.9. Most of the statistical significance comes from large scales, demonstrating, for the first time, that a substantial portion of the UGRB aligns with the mass clustering of the Universe as traced by weak lensing. Blazars provide a plausible explanation for this signal, especially if those contributing to the correlation reside in halos of large mass ($\sim 10^{14} M_{\odot}$) and account for approximately 30-40 % of the UGRB above 10 GeV. Additionally, we observe a preference for a curved $\gamma$-ray energy spectrum, with a log-parabolic shape being favored over a power-law. We also discuss the possibility of modifications to the blazar model and the inclusion of additional $gamma$-ray sources, such as star-forming galaxies or particle dark matter., Comment: 34 pages, 15 figures

Published
2025

2. Euclid preparation LX. The use of HST images as input for weak-lensing image simulations

Author

Euclid Collaboration, Scognamiglio, D., Schrabback, T., Tewes, M., Gillis, B., Hoekstra, H., Huff, E. M., Marggraf, O., Kitching, T., Massey, R., Tereno, I., Carvalho, C. S., Robertson, A., Congedo, G., Aghanim, N., Altieri, B., Amara, A., Andreon, S., Auricchio, N., Baccigalupi, C., Baldi, M., Bardelli, S., Battaglia, P., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Cardone, V. F., Carretero, J., Casas, S., Castander, F. J., Castellano, M., Castignani, G., Cavuoti, S., Cimatti, A., Colodro-Conde, C., Conselice, C. J., Conversi, L., Copin, Y., Courbin, F., Courtois, H. M., Cropper, M., Da Silva, A., Degaudenzi, H., De Lucia, G., Di Giorgio, A. M., Dinis, J., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farina, M., Farrens, S., Ferriol, S., Fosalba, P., Frailis, M., Franceschi, E., Galeotta, S., Giocoli, C., Gómez-Alvarez, P., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Holmes, W., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Joachimi, B., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kubik, B., Kümmel, M., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Mainetti, G., Maiorano, E., Mansutti, O., Markovic, K., Martinelli, M., Martinet, N., Marulli, F., Medinaceli, E., Mei, S., Mellier, Y., Meneghetti, M., Meylan, G., Moresco, M., Moscardini, L., Nakajima, R., Niemi, S. -M., Nightingale, J. W., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sakr, Z., Sánchez, A. G., Sapone, D., Sartoris, B., Scaramella, R., Schirmer, M., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Skottfelt, J., Stanco, L., Starck, J. -L., Steinwagner, J., Tallada-Crespí, P., Taylor, A. N., Teplitz, H. I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valenziano, L., Vassallo, T., Kleijn, G. Verdoes, Veropalumbo, A., Wang, Y., Weller, J., Wetzstein, M., Zamorani, G., Zucca, E., Biviano, A., Bolzonella, M., Boucaud, A., Bozzo, E., Burigana, C., Calabrese, M., Vigo, J. A. Escartin, Gracia-Carpio, J., Mauri, N., Pezzotta, A., Pöntinen, M., Porciani, C., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Allevato, V., Anselmi, S., Ballardini, M., Blot, L., Borgani, S., Bruton, S., Cabanac, R., Calabro, A., Cappi, A., Castro, T., Chambers, K. C., Contarini, S., Cooray, A. R., Davini, S., De Caro, B., Desprez, G., Díaz-Sánchez, A., Di Domizio, S., Dole, H., Escoffier, S., Ferrari, A. G., Ferrero, I., Fornari, F., Gabarra, L., Ganga, K., García-Bellido, J., Gaztanaga, E., Giacomini, F., Gianotti, F., Gozaliasl, G., Hall, A., Hemmati, S., Hildebrandt, H., Hjorth, J., Muñoz, A. Jimenez, Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Graet, J. Le, Legrand, L., Loureiro, A., Macias-Perez, J., Maggio, G., Magliocchetti, M., Mannucci, F., Maoli, R., Martins, C. J. A. P., Matthew, S., Maurin, L., Metcalf, R. B., Monaco, P., Moretti, C., Morgante, G., Walton, Nicholas A., Patrizii, L., Popa, V., Potter, D., Reimberg, P., Risso, I., Rocci, P. -F., Rollins, R. P., Sahlén, M., Schneider, A., Sereno, M., Simon, P., Mancini, A. Spurio, Tanidis, K., Tao, C., Testera, G., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., Vergani, D., and Verza, G.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

Data from the Euclid space telescope will enable cosmic shear measurements with very small statistical errors, requiring corresponding systematic error control level. A common approach to correct for shear biases involves calibrating shape measurement methods using image simulations with known input shear. Given their high resolution, Hubble Space Telescope (HST) galaxies can, in principle, be utilised to emulate Euclid observations. In this work, we employ a GalSim-based testing environment to investigate whether uncertainties in the HST point spread function (PSF) model or in data processing techniques introduce significant biases in weak-lensing (WL) shear calibration. We used single S\'ersic galaxy models to simulate both HST and Euclid observations. We then `Euclidised' our HST simulations and compared the results with the directly simulated Euclid-like images. For this comparison, we utilised a moment-based shape measurement algorithm and galaxy model fits. Through the Euclidisation procedure, we effectively reduced the residual multiplicative biases in shear measurements to sub-percent levels. This achievement was made possible by employing either the native pixel scales of the instruments, utilising the Lanczos15 interpolation kernel, correcting for noise correlations, and ensuring consistent galaxy signal-to-noise ratios between simulation branches. However, the Euclidisation procedure requires further analysis on the impact of the correlated noise, to estimate calibration bias. Additionally, we conducted an in-depth analysis of the accuracy of TinyTim HST PSF models using star fields observed in the F606W and F814W filters. We observe that F606W images exhibit a broader scatter in the recovered best-fit focus, compared to those in the F814W filter., Comment: Accepted by A&A. 23 pages, 20 figures, Euclid pre-launch key paper

Published
2025

3. Dark Energy Survey Year 6 Results: Cell-based Coadds and Metadetection Weak Lensing Shape Catalogue

Author

Yamamoto, M., Becker, M. R., Sheldon, E., Jarvis, M., Gruendl, R. A., Menanteau, F., Rykoff, E. S., Mau, S., Schutt, T., Gatti, M., Troxel, M. A., Amon, A., Anbajagane, D., Bernstein, G. M., Gruen, D., Huff, E. M., Tabbutt, M., Tong, A., Yanny, B., Abbott, T. M. C., Aguena, M., Alarcon, A., Andrade-Oliveira, F., Bechtol, K., Blazek, J., Brooks, D., Rosell, A. Carnero, Carretero, J., Chang, C., Choi, A., Costanzi, M., Crocce, M., da Costa, L. N., Davis, T. M., De Vicente, J., Desai, S., Diehl, H. T., Dodelson, S., Doel, P., Doux, C., Drlica-Wagner, A., Ferté, A., Flaugher, B., Frieman, J., García-Bellido, J., Gaztanaga, E., Giannini, G., Gutierrez, G., Hartley, W. G., Herner, K., Hinton, S. R., Hollowood, D. L., Honscheid, K., Huterer, D., Krause, E., Kuehn, K., Lahav, O., Lima, M., Marshall, J. L., Mena-Fernández, J., Miquel, R., Mohr, J. J., Muir, J., Myles, J., Ogando, R. L. C., Pieres, A., Malagón, A. A. Plazas, Porredon, A., Prat, J., Raveri, M., Rodriguez-Monroy, M., Roodman, A., Samuroff, S., Sanchez, E., Cid, D. Sanchez, Scarpine, V., Sevilla-Noarbe, I., Smith, M., Suchyta, E., Tarle, G., Vikram, V., Weaverdyck, N., Wiseman, P., and Zhang, Y.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

We present the Metadetection weak lensing galaxy shape catalogue from the six-year Dark Energy Survey (DES Y6) imaging data. This dataset is the final release from DES, spanning 4422 deg$^2$ of the southern sky. We describe how the catalogue was constructed, including the two new major processing steps, cell-based image coaddition and shear measurements with Metadetection. The DES Y6 Metadetection weak lensing shape catalogue consists of 151,922,791 galaxies detected over riz bands, with an effective number density of $n_{\rm eff}$ =8.22 galaxies per arcmin$^2$ and shape noise of $\sigma_e$ = 0.29. We carry out a suite of validation tests on the catalogue, including testing for PSF leakage, testing for the impact of PSF modeling errors, and testing the correlation of the shear measurements with galaxy, PSF, and survey properties. In addition to demonstrating that our catalogue is robust for weak lensing science, we use the DES Y6 image simulation suite (Mau, Becker et al. 2025) to estimate the overall multiplicative shear bias of our shear measurement pipeline. We find no detectable multiplicative bias at the roughly half-percent level, with m = (3.4 $\pm$ 6.1) x $10^{-3}$, at 3$\sigma$ uncertainty. This is the first time both cell-based coaddition and Metadetection algorithms are applied to observational data, paving the way to the Stage-IV weak lensing surveys., Comment: 30 pages, 22 figures

Published
2025

4. Demonstrating dynamic surface codes

Author

Eickbusch, Alec, McEwen, Matt, Sivak, Volodymyr, Bourassa, Alexandre, Atalaya, Juan, Claes, Jahan, Kafri, Dvir, Gidney, Craig, Warren, Christopher W., Gross, Jonathan, Opremcak, Alex, Miao, Nicholas Zobrist Kevin C., Roberts, Gabrielle, Satzinger, Kevin J., Bengtsson, Andreas, Neeley, Matthew, Livingston, William P., Greene, Alex, Rajeev, Acharya, Beni, Laleh Aghababaie, Aigeldinger, Georg, Alcaraz, Ross, Andersen, Trond I., Ansmann, Markus, Frank, Arute, Arya, Kunal, Asfaw, Abraham, Babbush, Ryan, Ballard, Brian, Bardin, Joseph C., Bilmes, Alexander, Jenna, Bovaird, Bowers, Dylan, Brill, Leon, Broughton, Michael, Browne, David A., Buchea, Brett, Buckley, Bob B., Tim, Burger, Burkett, Brian, Bushnell, Nicholas, Cabrera, Anthony, Campero, Juan, Chang, Hung-Shen, Chiaro, Ben, Chih, Liang-Ying, Cleland, Agnetta Y., Cogan, Josh, Collins, Roberto, Conner, Paul, Courtney, William, Alexander, Crook, L., Curtin, Ben, Das, Sayan, Barba, Alexander Del Toro, Demura, Sean, De Lorenzo, Laura, Di Paolo, Agustin, Donohoe, Paul, Drozdov, Ilya K., Dunsworth, Andrew, Elbag, Aviv Moshe, Elzouka, Mahmoud, Erickson, Catherine, Ferreira, Vinicius S., Burgos, Leslie Flores, Forati, Ebrahim, Fowler, Austin G., Foxen, Brooks, Ganjam, Suhas, Gonzalo, Garcia, Gasca, Robert, Genois, Élie, Giang, William, Gilboa, Dar, Gosula, Raja, Dau, Alejandro Grajales, Dietrich, Graumann, Ha, Tan, Habegger, Steve, Hansen, Monica, Harrigan, Matthew P., Harrington, Sean D., Heslin, Stephen, Heu, Paula, Higgott, Oscar, Hiltermann, Reno, Hilton, Jeremy, Huang, Hsin-Yuan, Huff, Ashley, Huggins, William J., Jeffrey, Evan, Jiang, Zhang, Jin, Xiaoxuan, Jones, Cody, Joshi, Chaitali, Juhas, Pavol, Kabel, Andreas, Kang, Hui, Amir, Karamlou, H., Kechedzhi, Kostyantyn, Khaire, Trupti, Khattar, Tanuj, Khezri, Mostafa, Kim, Seon, Kobrin, Bryce, Korotkov, Alexander N., Kostritsa, Fedor, Kreikebaum, John Mark, Kurilovich, Vladislav D., Landhuis, David, Tiano, Lange-Dei, Langley, Brandon W., Lau, Kim-Ming, Ledford, Justin, Lee, Kenny, Lester, Brian J., Guevel, Loïck Le, Wing, Li, Yan, Lill, Alexander T., Locharla, Aditya, Lucero, Erik, Lundahl, Daniel, Lunt, Aaron, Madhuk, Sid, Maloney, Ashley, Mandrà, Salvatore, Martin, Leigh S., Martin, Orion, Maxfield, Cameron, McClean, Jarrod R., Meeks, Seneca, Anthony, Megrant, Molavi, Reza, Molina, Sebastian, Montazeri, Shirin, Movassagh, Ramis, Newman, Michael, Nguyen, Anthony, Nguyen, Murray, Ni, Chia-Hung, Oas, Logan, Orosco, Raymond, Ottosson, Kristoffer, Pizzuto, Alex, Potter, Rebecca, Pritchard, Orion, Quintana, Chris, Ramachandran, Ganesh, Reagor, Matthew J., Rhodes, David M., Rosenberg, Eliott, Rossi, Elizabeth, Sankaragomathi, Kannan, Schurkus, Henry F., Shearn, Michael J., Shorter, Aaron, Shutty, Noah, Shvarts, Vladimir, Small, Spencer, Smith, W. Clarke, Springer, Sofia, Sterling, George, Suchard, Jordan, Szasz, Aaron, Sztein, Alex, Thor, Douglas, Tomita, Eifu, Torres, Alfredo, Torunbalci, M. Mert, Vaishnav, Abeer, Vargas, Justin, Sergey, Vdovichev, Vidal, Guifre, Heidweiller, Catherine Vollgraff, Waltman, Steven, Waltz, Jonathan, Wang, Shannon X., Ware, Brayden, Weidel, Travis, White, Theodore, Wong, Kristi, Woo, Bryan W. K., Woodson, Maddy, Xing, Cheng, Yao, Z. Jamie, Yeh, Ping, Ying, Bicheng, Yoo, Juhwan, Yosri, Noureldin, Young, Grayson, Zalcman, Adam, Yaxing, Zhang, Zhu, Ningfeng, Boixo, Sergio, Kelly, Julian, Smelyanskiy, Vadim, Neven, Hartmut, Bacon, Dave, Chen, Zijun, Klimov, Paul V., Roushan, Pedram, Neill, Charles, Chen, Yu, and Morvan, Alexis

Subjects
Quantum Physics
Abstract

A remarkable characteristic of quantum computing is the potential for reliable computation despite faulty qubits. This can be achieved through quantum error correction, which is typically implemented by repeatedly applying static syndrome checks, permitting correction of logical information. Recently, the development of time-dynamic approaches to error correction has uncovered new codes and new code implementations. In this work, we experimentally demonstrate three time-dynamic implementations of the surface code, each offering a unique solution to hardware design challenges and introducing flexibility in surface code realization. First, we embed the surface code on a hexagonal lattice, reducing the necessary couplings per qubit from four to three. Second, we walk a surface code, swapping the role of data and measure qubits each round, achieving error correction with built-in removal of accumulated non-computational errors. Finally, we realize the surface code using iSWAP gates instead of the traditional CNOT, extending the set of viable gates for error correction without additional overhead. We measure the error suppression factor when scaling from distance-3 to distance-5 codes of $\Lambda_{35,\text{hex}} = 2.15(2)$, $\Lambda_{35,\text{walk}} = 1.69(6)$, and $\Lambda_{35,\text{iSWAP}} = 1.56(2)$, achieving state-of-the-art error suppression for each. With detailed error budgeting, we explore their performance trade-offs and implications for hardware design. This work demonstrates that dynamic circuit approaches satisfy the demands for fault-tolerance and opens new alternative avenues for scalable hardware design., Comment: 11 pages, 5 figures, Supplementary Information

Published
2024

5. Scaling and logic in the color code on a superconducting quantum processor

Author

Lacroix, Nathan, Bourassa, Alexandre, Heras, Francisco J. H., Zhang, Lei M., Bausch, Johannes, Senior, Andrew W., Edlich, Thomas, Shutty, Noah, Sivak, Volodymyr, Bengtsson, Andreas, McEwen, Matt, Higgott, Oscar, Kafri, Dvir, Claes, Jahan, Morvan, Alexis, Chen, Zijun, Zalcman, Adam, Madhuk, Sid, Acharya, Rajeev, Beni, Laleh Aghababaie, Aigeldinger, Georg, Alcaraz, Ross, Andersen, Trond I., Ansmann, Markus, Arute, Frank, Arya, Kunal, Asfaw, Abraham, Atalaya, Juan, Babbush, Ryan, Ballard, Brian, Bardin, Joseph C., Bilmes, Alexander, Blackwell, Sam, Bovaird, Jenna, Bowers, Dylan, Brill, Leon, Broughton, Michael, Browne, David A., Buchea, Brett, Buckley, Bob B., Burger, Tim, Burkett, Brian, Bushnell, Nicholas, Cabrera, Anthony, Campero, Juan, Chang, Hung-Shen, Chiaro, Ben, Chih, Liang-Ying, Cleland, Agnetta Y., Cogan, Josh, Collins, Roberto, Conner, Paul, Courtney, William, Crook, Alexander L., Curtin, Ben, Das, Sayan, Demura, Sean, De Lorenzo, Laura, Di Paolo, Agustin, Donohoe, Paul, Drozdov, Ilya, Dunsworth, Andrew, Eickbusch, Alec, Elbag, Aviv Moshe, Elzouka, Mahmoud, Erickson, Catherine, Ferreira, Vinicius S., Burgos, Leslie Flores, Forati, Ebrahim, Fowler, Austin G., Foxen, Brooks, Ganjam, Suhas, Garcia, Gonzalo, Gasca, Robert, Genois, Élie, Giang, William, Gilboa, Dar, Gosula, Raja, Dau, Alejandro Grajales, Graumann, Dietrich, Greene, Alex, Gross, Jonathan A., Ha, Tan, Habegger, Steve, Hansen, Monica, Harrigan, Matthew P., Harrington, Sean D., Heslin, Stephen, Heu, Paula, Hiltermann, Reno, Hilton, Jeremy, Hong, Sabrina, Huang, Hsin-Yuan, Huff, Ashley, Huggins, William J., Jeffrey, Evan, Jiang, Zhang, Jin, Xiaoxuan, Joshi, Chaitali, Juhas, Pavol, Kabel, Andreas, Kang, Hui, Karamlou, Amir H., Kechedzhi, Kostyantyn, Khaire, Trupti, Khattar, Tanuj, Khezri, Mostafa, Kim, Seon, Klimov, Paul V., Kobrin, Bryce, Korotkov, Alexander N., Kostritsa, Fedor, Kreikebaum, John Mark, Kurilovich, Vladislav D., Landhuis, David, Lange-Dei, Tiano, Langley, Brandon W., Laptev, Pavel, Lau, Kim-Ming, Ledford, Justin, Lee, Kenny, Lester, Brian J., Guevel, Loïck Le, Li, Wing Yan, Li, Yin, Lill, Alexander T., Livingston, William P., Locharla, Aditya, Lucero, Erik, Lundahl, Daniel, Lunt, Aaron, Maloney, Ashley, Mandrà, Salvatore, Martin, Leigh S., Martin, Orion, Maxfield, Cameron, McClean, Jarrod R., Meeks, Seneca, Megrant, Anthony, Miao, Kevin C., Molavi, Reza, Molina, Sebastian, Montazeri, Shirin, Movassagh, Ramis, Neill, Charles, Newman, Michael, Nguyen, Anthony, Nguyen, Murray, Ni, Chia-Hung, Niu, Murphy Y., Oas, Logan, Oliver, William D., Orosco, Raymond, Ottosson, Kristoffer, Pizzuto, Alex, Potter, Rebecca, Pritchard, Orion, Quintana, Chris, Ramachandran, Ganesh, Reagor, Matthew J., Resnick, Rachel, Rhodes, David M., Roberts, Gabrielle, Rosenberg, Eliott, Rosenfeld, Emma, Rossi, Elizabeth, Roushan, Pedram, Sankaragomathi, Kannan, Schurkus, Henry F., Shearn, Michael J., Shorter, Aaron, Shvarts, Vladimir, Small, Spencer, Smith, W. Clarke, Springer, Sofia, Sterling, George, Suchard, Jordan, Szasz, Aaron, Sztein, Alex, Thor, Douglas, Tomita, Eifu, Torres, Alfredo, Torunbalci, M. Mert, Vaishnav, Abeer, Vargas, Justin, Vdovichev, Sergey, Vidal, Guifre, Heidweiller, Catherine Vollgraff, Waltman, Steven, Waltz, Jonathan, Wang, Shannon X., Ware, Brayden, Weidel, Travis, White, Theodore, Wong, Kristi, Woo, Bryan W. K., Woodson, Maddy, Xing, Cheng, Yao, Z. Jamie, Yeh, Ping, Ying, Bicheng, Yoo, Juhwan, Yosri, Noureldin, Young, Grayson, Zhang, Yaxing, Zhu, Ningfeng, Zobrist, Nicholas, Neven, Hartmut, Kohli, Pushmeet, Davies, Alex, Boixo, Sergio, Kelly, Julian, Jones, Cody, Gidney, Craig, and Satzinger, Kevin J.

Subjects
Quantum Physics
Abstract

Quantum error correction is essential for bridging the gap between the error rates of physical devices and the extremely low logical error rates required for quantum algorithms. Recent error-correction demonstrations on superconducting processors have focused primarily on the surface code, which offers a high error threshold but poses limitations for logical operations. In contrast, the color code enables much more efficient logic, although it requires more complex stabilizer measurements and decoding techniques. Measuring these stabilizers in planar architectures such as superconducting qubits is challenging, and so far, realizations of color codes have not addressed performance scaling with code size on any platform. Here, we present a comprehensive demonstration of the color code on a superconducting processor, achieving logical error suppression and performing logical operations. Scaling the code distance from three to five suppresses logical errors by a factor of $\Lambda_{3/5}$ = 1.56(4). Simulations indicate this performance is below the threshold of the color code, and furthermore that the color code may be more efficient than the surface code with modest device improvements. Using logical randomized benchmarking, we find that transversal Clifford gates add an error of only 0.0027(3), which is substantially less than the error of an idling error correction cycle. We inject magic states, a key resource for universal computation, achieving fidelities exceeding 99% with post-selection (retaining about 75% of the data). Finally, we successfully teleport logical states between distance-three color codes using lattice surgery, with teleported state fidelities between 86.5(1)% and 90.7(1)%. This work establishes the color code as a compelling research direction to realize fault-tolerant quantum computation on superconducting processors in the near future.

Published
2024

6. Multiprobe Cosmology from the Abundance of SPT Clusters and DES Galaxy Clustering and Weak Lensing

Author

Bocquet, S., Grandis, S., Krause, E., To, C., Bleem, L. E., Klein, M., Mohr, J. J., Schrabback, T., Alarcon, A., Alves, O., Amon, A., Andrade-Oliveira, F., Baxter, E. J., Bechtol, K., Becker, M. R., Bernstein, G. M., Blazek, J., Camacho, H., Campos, A., Rosell, A. Carnero, Kind, M. Carrasco, Cawthon, R., Chang, C., Chen, R., Choi, A., Cordero, J., Crocce, M., Davis, C., DeRose, J., Diehl, H. T., Dodelson, S., Doux, C., Drlica-Wagner, A., Eckert, K., Eifler, T. F., Elsner, F., Elvin-Poole, J., Everett, S., Fang, X., Ferté, A., Fosalba, P., Friedrich, O., Frieman, J., Gatti, M., Giannini, G., Gruen, D., Gruendl, R. A., Harrison, I., Hartley, W. G., Herner, K., Huang, H., Huff, E. M., Huterer, D., Jarvis, M., Kuropatkin, N., Leget, P. -F., Lemos, P., Liddle, A. R., MacCrann, N., McCullough, J., Muir, J., Myles, J., Navarro-Alsina, A., Pandey, S., Park, Y., Porredon, A., Prat, J., Raveri, M., Rollins, R. P., Roodman, A., Rosenfeld, R., Rykoff, E. S., Sánchez, C., Sanchez, J., Secco, L. F., Sevilla-Noarbe, I., Sheldon, E., Shin, T., Troxel, M. A., Tutusaus, I., Varga, T. N., Weaverdyck, N., Wechsler, R. H., Wu, H. -Y., Yanny, B., Yin, B., Zhang, Y., Zuntz, J., Abbott, T. M. C., Ade, P. A. R., Aguena, M., Allam, S., Allen, S. W., Anderson, A. J., Ansarinejad, B., Austermann, J. E., Bayliss, M., Beall, J. A., Bender, A. N., Benson, B. A., Bianchini, F., Brodwin, M., Brooks, D., Bryant, L., Burke, D. L., Canning, R. E. A., Carlstrom, J. E., Carretero, J., Castander, F. J., Chang, C. L., Chaubal, P., Chiang, H. C., Chou, T-L., Citron, R., Moran, C. Corbett, Costanzi, M., Crawford, T. M., Crites, A. T., da Costa, L. N., Pereira, M. E. S., Davis, T. M., de Haan, T., Dobbs, M. A., Doel, P., Everett, W., Farahi, A., Flaugher, B., Flores, A. M., Floyd, B., Gallicchio, J., Gaztanaga, E., George, E. M., Gladders, M. D., Gupta, N., Gutierrez, G., Halverson, N. W., Hinton, S. R., Hlavacek-Larrondo, J., Holder, G. P., Hollowood, D. L., Holzapfel, W. L., Hrubes, J. D., Huang, N., Hubmayr, J., Irwin, K. D., James, D. J., Kéruzoré, F., Khullar, G., Kim, K., Knox, L., Kraft, R., Kuehn, K., Lahav, O., Lee, A. T., Lee, S., Li, D., Lidman, C., Lima, M., Lowitz, A., Mahler, G., Mantz, A., Marshall, J. L., McDonald, M., McMahon, J. J., Mena-Fernández, J., Meyer, S. S., Miquel, R., Montgomery, J., Natoli, T., Nibarger, J. P., Noble, G. I., Novosad, V., Ogando, R. L. C., Padin, S., Paschos, P., Patil, S., Malagón, A. A. Plazas, Pryke, C., Reichardt, C. L., Roberson, J., Romer, A. K., Romero, C., Ruhl, J. E., Saliwanchik, B. R., Salvati, L., Samuroff, S., Sanchez, E., Santiago, B., Sarkar, A., Saro, A., Schaffer, K. K., Sharon, K., Sievers, C., Smecher, G., Smith, M., Somboonpanyakul, T., Sommer, M., Stalder, B., Stark, A. A., Stephen, J., Strazzullo, V., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., Tucker, C., Tucker, D. L., Veach, T., Vieira, J. D., von der Linden, A., Wang, G., Whitehorn, N., Wu, W. L. K., Yefremenko, V., Young, M., Zebrowski, J. A., Zohren, H., Collaboration, DES, and Collaboration, SPT

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

Cosmic shear, galaxy clustering, and the abundance of massive halos each probe the large-scale structure of the universe in complementary ways. We present cosmological constraints from the joint analysis of the three probes, building on the latest analyses of the lensing-informed abundance of clusters identified by the South Pole Telescope (SPT) and of the auto- and cross-correlation of galaxy position and weak lensing measurements (3$\times$2pt) in the Dark Energy Survey (DES). We consider the cosmological correlation between the different tracers and we account for the systematic uncertainties that are shared between the large-scale lensing correlation functions and the small-scale lensing-based cluster mass calibration. Marginalized over the remaining $\Lambda$CDM parameters (including the sum of neutrino masses) and 52 astrophysical modeling parameters, we measure $\Omega_\mathrm{m}=0.300\pm0.017$ and $\sigma_8=0.797\pm0.026$. Compared to constraints from Planck primary CMB anisotropies, our constraints are only 15% wider with a probability to exceed of 0.22 ($1.2\sigma$) for the two-parameter difference. We further obtain $S_8\equiv\sigma_8(\Omega_\mathrm{m}/0.3)^{0.5}=0.796\pm0.013$ which is lower than the Planck measurement at the $1.6\sigma$ level. The combined SPT cluster, DES 3$\times$2pt, and Planck datasets mildly prefer a non-zero positive neutrino mass, with a 95% upper limit $\sum m_\nu<0.25~\mathrm{eV}$ on the sum of neutrino masses. Assuming a $w$CDM model, we constrain the dark energy equation of state parameter $w=-1.15^{+0.23}_{-0.17}$ and when combining with Planck primary CMB anisotropies, we recover $w=-1.20^{+0.15}_{-0.09}$, a $1.7\sigma$ difference with a cosmological constant. The precision of our results highlights the benefits of multiwavelength multiprobe cosmology., Comment: Submitted to Phys. Rev. D

Published
2024

7. COVID-19 Pandemic's Impact on CPA Examination Test Taking and Pass Rates of Hispanic Serving Institution Graduates

Author

Kendra Huff, Thomas M. Krueger, and Genevieve Scalan

Abstract

The COVID-19 pandemic has amplified concerns regarding the lagging proportion of Hispanic Certified Public Accountants (CPAs). This report contrasts the CPA exam success of candidates from the regional branches of one large university system's Hispanic-serving institutions (HSIs) and non-Hispanic-serving institutions. Examination statistics during the first 27 months of the pandemic are compared to the prior 5 years. We find a statistically significant negative impact on candidates from HSIs, which will likely lead to continued underrepresentation of Hispanics within the accounting profession.

Published
2025
Full Text
View/download PDF

8. Dark Energy Survey Year 3: Blue Shear

Author

McCullough, J., Amon, A., Legnani, E., Gruen, D., Roodman, A., Friedrich, O., MacCrann, N., Becker, M. R., Myles, J., Dodelson, S., Samuroff, S., Blazek, J., Prat, J., Honscheid, K., Pieres, A., Ferté, A., Alarcon, A., Drlica-Wagner, A., Choi, A., Navarro-Alsina, A., Campos, A., Malagón, A. A. Plazas, Porredon, A., Farahi, A., Ross, A. J., Rosell, A. Carnero, Yin, B., Flaugher, B., Yanny, B., Sánchez, C., Chang, C., Davis, C., To, C., Doux, C., Brooks, D., James, D. J., Cid, D. Sanchez, Hollowood, D. L., Huterer, D., Rykoff, E. S., Gaztanaga, E., Huff, E. M., Suchyta, E., Sheldon, E., Sanchez, E., Tarsitano, F., Andrade-Oliveira, F., Castander, F. J., Bernstein, G. M., Gutierrez, G., Giannini, G., Tarle, G., Diehl, H. T., Huang, H., Harrison, I., Sevilla-Noarbe, I., Tutusaus, I., Ferrero, I., Elvin-Poole, J., Marshall, J. L., Muir, J., Weller, J., Zuntz, J., Carretero, J., DeRose, J., Frieman, J., Cordero, J., De Vicente, J., García-Bellido, J., Mena-Fernández, J., Eckert, K., Romer, A. K., Bechtol, K., Herner, K., Kuehn, K., Secco, L. F., da Costa, L. N., Paterno, M., Soares-Santos, 21 M., Gatti, M., Raveri, M., Yamamoto, M., Smith, M., Kind, M. Carrasco, Troxel, M. A., Aguena, M., Jarvis, M., Swanson, M. E. C., Weaverdyck, N., Lahav, O., Doel, P., Wiseman, P., Miquel, R., Gruendl, R. A., Cawthon, R., Allam, S., Hinton, S. R., Bridle, S. L., Bocquet, S., Desai, S., Pandey, S., Everett, S., Lee, S., Shin, T., Palmese, A., Conselice, C., Burke, D. L., Buckley-Geer, E., Lima, M., Vincenzi, M., Pereira, M. E. S., Crocce, M., Schubnell, M., Jeffrey, N., Alves, O., Vikram, V., Zhang, Y., and Collaboration, DES

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

Modeling the intrinsic alignment (IA) of galaxies poses a challenge to weak lensing analyses. The Dark Energy Survey is expected to be less impacted by IA when limited to blue, star-forming galaxies. The cosmological parameter constraints from this blue cosmic shear sample are stable to IA model choice, unlike passive galaxies in the full DES Y3 sample, the goodness-of-fit is improved and the $\Omega_{m}$ and $S_8$ better agree with the cosmic microwave background. Mitigating IA with sample selection, instead of flexible model choices, can reduce uncertainty in $S_8$ by a factor of 1.5., Comment: Data access available at https://jamiemccullough.github.io/data/blueshear/

Published
2024

9. Judgment of Learning: A Human Ability Beyond Generative Artificial Intelligence

Author

Huff, Markus and Ulakçı, Elanur

Subjects
Computer Science - Computation and Language
Abstract

Large language models (LLMs) increasingly mimic human cognition in various language-based tasks. However, their capacity for metacognition - particularly in predicting memory performance - remains unexplored. Here, we introduce a cross-agent prediction model to assess whether ChatGPT-based LLMs align with human judgments of learning (JOL), a metacognitive measure where individuals predict their own future memory performance. We tested humans and LLMs on pairs of sentences, one of which was a garden-path sentence - a sentence that initially misleads the reader toward an incorrect interpretation before requiring reanalysis. By manipulating contextual fit (fitting vs. unfitting sentences), we probed how intrinsic cues (i.e., relatedness) affect both LLM and human JOL. Our results revealed that while human JOL reliably predicted actual memory performance, none of the tested LLMs (GPT-3.5-turbo, GPT-4-turbo, and GPT-4o) demonstrated comparable predictive accuracy. This discrepancy emerged regardless of whether sentences appeared in fitting or unfitting contexts. These findings indicate that, despite LLMs' demonstrated capacity to model human cognition at the object-level, they struggle at the meta-level, failing to capture the variability in individual memory predictions. By identifying this shortcoming, our study underscores the need for further refinements in LLMs' self-monitoring abilities, which could enhance their utility in educational settings, personalized learning, and human-AI interactions. Strengthening LLMs' metacognitive performance may reduce the reliance on human oversight, paving the way for more autonomous and seamless integration of AI into tasks requiring deeper cognitive awareness., Comment: 31 pages, 2 figures. Note: The appendix is similar to arXiv:2403.05152 because the same stimulus material was used

Published
2024

10. Observation of disorder-free localization and efficient disorder averaging on a quantum processor

Author

Gyawali, Gaurav, Cochran, Tyler, Lensky, Yuri, Rosenberg, Eliott, Karamlou, Amir H., Kechedzhi, Kostyantyn, Berndtsson, Julia, Westerhout, Tom, Asfaw, Abraham, Abanin, Dmitry, Acharya, Rajeev, Beni, Laleh Aghababaie, Andersen, Trond I., Ansmann, Markus, Arute, Frank, Arya, Kunal, Astrakhantsev, Nikita, Atalaya, Juan, Babbush, Ryan, Ballard, Brian, Bardin, Joseph C., Bengtsson, Andreas, Bilmes, Alexander, Bortoli, Gina, Bourassa, Alexandre, Bovaird, Jenna, Brill, Leon, Broughton, Michael, Browne, David A., Buchea, Brett, Buckley, Bob B., Buell, David A., Burger, Tim, Burkett, Brian, Bushnell, Nicholas, Cabrera, Anthony, Campero, Juan, Chang, Hung-Shen, Chen, Zijun, Chiaro, Ben, Claes, Jahan, Cleland, Agnetta Y., Cogan, Josh, Collins, Roberto, Conner, Paul, Courtney, William, Crook, Alexander L., Das, Sayan, Debroy, Dripto M., De Lorenzo, Laura, Barba, Alexander Del Toro, Demura, Sean, Di Paolo, Agustin, Donohoe, Paul, Drozdov, Ilya, Dunsworth, Andrew, Earle, Clint, Eickbusch, Alec, Elbag, Aviv Moshe, Elzouka, Mahmoud, Erickson, Catherine, Faoro, Lara, Fatemi, Reza, Ferreira, Vinicius S., Burgos, Leslie Flores, Forati, Ebrahim, Fowler, Austin G., Foxen, Brooks, Ganjam, Suhas, Gasca, Robert, Giang, William, Gidney, Craig, Gilboa, Dar, Gosula, Raja, Dau, Alejandro Grajales, Graumann, Dietrich, Greene, Alex, Gross, Jonathan A., Habegger, Steve, Hamilton, Michael C., Hansen, Monica, Harrigan, Matthew P., Harrington, Sean D., Heslin, Stephen, Heu, Paula, Hill, Gordon, Hilton, Jeremy, Hoffmann, Markus R., Huang, Hsin-Yuan, Huff, Ashley, Huggins, William J., Ioffe, Lev B., Isakov, Sergei V., Jeffrey, Evan, Jiang, Zhang, Jones, Cody, Jordan, Stephen, Joshi, Chaitali, Juhas, Pavol, Kafri, Dvir, Kang, Hui, Khaire, Trupti, Khattar, Tanuj, Khezri, Mostafa, Kieferová, Mária, Kim, Seon, Klimov, Paul V., Klots, Andrey R., Kobrin, Bryce, Korotkov, Alexander N., Kostritsa, Fedor, Kreikebaum, John Mark, Kurilovich, Vladislav D., Landhuis, David, Lange-Dei, Tiano, Langley, Brandon W., Laptev, Pavel, Lau, Kim-Ming, Guevel, Loïck Le, Ledford, Justin, Lee, Joonho, Lee, Kenny, Lester, Brian J., Li, Wing Yan, Lill, Alexander T., Liu, Wayne, Livingston, William P., Locharla, Aditya, Lundahl, Daniel, Lunt, Aaron, Madhuk, Sid, Maloney, Ashley, Mandrà, Salvatore, Martin, Leigh S., Martin, Steven, Martin, Orion, Maxfield, Cameron, McClean, Jarrod R., McEwen, Matt, Meeks, Seneca, Megrant, Anthony, Mi, Xiao, Miao, Kevin C., Mieszala, Amanda, Molina, Sebastian, Montazeri, Shirin, Morvan, Alexis, Movassagh, Ramis, Neill, Charles, Nersisyan, Ani, Newman, Michael, Nguyen, Anthony, Nguyen, Murray, Ni, Chia-Hung, Niu, Murphy Yuezhen, Oliver, William D., Ottosson, Kristoffer, Pizzuto, Alex, Potter, Rebecca, Pritchard, Orion, Pryadko, Leonid P., Quintana, Chris, Reagor, Matthew J., Rhodes, David M., Roberts, Gabrielle, Rocque, Charles, Rubin, Nicholas C., Saei, Negar, Sankaragomathi, Kannan, Satzinger, Kevin J., Schurkus, Henry F., Schuster, Christopher, Shearn, Michael J., Shorter, Aaron, Shutty, Noah, Shvarts, Vladimir, Sivak, Volodymyr, Skruzny, Jindra, Small, Spencer, Smith, W. Clarke, Springer, Sofia, Sterling, George, Suchard, Jordan, Szalay, Marco, Szasz, Aaron, Sztein, Alex, Thor, Douglas, Torunbalci, M. Mert, Vaishnav, Abeer, Vdovichev, Sergey, Vidal, Guifré, Heidweiller, Catherine Vollgraff, Waltman, Steven, Wang, Shannon X., White, Theodore, Wong, Kristi, Woo, Bryan W. K., Xing, Cheng, Yao, Z. Jamie, Yeh, Ping, Ying, Bicheng, Yoo, Juhwan, Yosri, Noureldin, Young, Grayson, Zalcman, Adam, Zhang, Yaxing, Zhu, Ningfeng, Zobrist, Nicholas, Boixo, Sergio, Kelly, Julian, Lucero, Erik, Chen, Yu, Smelyanskiy, Vadim, Neven, Hartmut, Kovrizhin, Dmitry, Knolle, Johannes, Halimeh, Jad C., Aleiner, Igor, Moessner, Roderich, and Roushan, Pedram

Subjects
Quantum Physics, Condensed Matter - Disordered Systems and Neural Networks, Condensed Matter - Strongly Correlated Electrons, High Energy Physics - Lattice
Abstract

One of the most challenging problems in the computational study of localization in quantum manybody systems is to capture the effects of rare events, which requires sampling over exponentially many disorder realizations. We implement an efficient procedure on a quantum processor, leveraging quantum parallelism, to efficiently sample over all disorder realizations. We observe localization without disorder in quantum many-body dynamics in one and two dimensions: perturbations do not diffuse even though both the generator of evolution and the initial states are fully translationally invariant. The disorder strength as well as its density can be readily tuned using the initial state. Furthermore, we demonstrate the versatility of our platform by measuring Renyi entropies. Our method could also be extended to higher moments of the physical observables and disorder learning.

Published
2024

11. Visualizing Dynamics of Charges and Strings in (2+1)D Lattice Gauge Theories

Author

Cochran, Tyler A., Jobst, Bernhard, Rosenberg, Eliott, Lensky, Yuri D., Gyawali, Gaurav, Eassa, Norhan, Will, Melissa, Abanin, Dmitry, Acharya, Rajeev, Beni, Laleh Aghababaie, Andersen, Trond I., Ansmann, Markus, Arute, Frank, Arya, Kunal, Asfaw, Abraham, Atalaya, Juan, Babbush, Ryan, Ballard, Brian, Bardin, Joseph C., Bengtsson, Andreas, Bilmes, Alexander, Bourassa, Alexandre, Bovaird, Jenna, Broughton, Michael, Browne, David A., Buchea, Brett, Buckley, Bob B., Burger, Tim, Burkett, Brian, Bushnell, Nicholas, Cabrera, Anthony, Campero, Juan, Chang, Hung-Shen, Chen, Zijun, Chiaro, Ben, Claes, Jahan, Cleland, Agnetta Y., Cogan, Josh, Collins, Roberto, Conner, Paul, Courtney, William, Crook, Alexander L., Curtin, Ben, Das, Sayan, Demura, Sean, De Lorenzo, Laura, Di Paolo, Agustin, Donohoe, Paul, Drozdov, Ilya, Dunsworth, Andrew, Eickbusch, Alec, Elbag, Aviv Moshe, Elzouka, Mahmoud, Erickson, Catherine, Ferreira, Vinicius S., Burgos, Leslie Flores, Forati, Ebrahim, Fowler, Austin G., Foxen, Brooks, Ganjam, Suhas, Gasca, Robert, Genois, Élie, Giang, William, Gilboa, Dar, Gosula, Raja, Dau, Alejandro Grajales, Graumann, Dietrich, Greene, Alex, Gross, Jonathan A., Habegger, Steve, Hansen, Monica, Harrigan, Matthew P., Harrington, Sean D., Heu, Paula, Higgott, Oscar, Hilton, Jeremy, Huang, Hsin-Yuan, Huff, Ashley, Huggins, William J., Jeffrey, Evan, Jiang, Zhang, Jones, Cody, Joshi, Chaitali, Juhas, Pavol, Kafri, Dvir, Kang, Hui, Karamlou, Amir H., Kechedzhi, Kostyantyn, Khaire, Trupti, Khattar, Tanuj, Khezri, Mostafa, Kim, Seon, Klimov, Paul V., Kobrin, Bryce, Korotkov, Alexander N., Kostritsa, Fedor, Kreikebaum, John Mark, Kurilovich, Vladislav D., Landhuis, David, Lange-Dei, Tiano, Langley, Brandon W., Lau, Kim-Ming, Ledford, Justin, Lee, Kenny, Lester, Brian J., Guevel, Loïck Le, Li, Wing Yan, Lill, Alexander T., Livingston, William P., Locharla, Aditya, Lundahl, Daniel, Lunt, Aaron, Madhuk, Sid, Maloney, Ashley, Mandrà, Salvatore, Martin, Leigh S., Martin, Orion, Maxfield, Cameron, McClean, Jarrod R., McEwen, Matt, Meeks, Seneca, Megrant, Anthony, Miao, Kevin C., Molavi, Reza, Molina, Sebastian, Montazeri, Shirin, Movassagh, Ramis, Neill, Charles, Newman, Michael, Nguyen, Anthony, Nguyen, Murray, Ni, Chia-Hung, Niu, Murphy Yuezhen, Oliver, William D., Ottosson, Kristoffer, Pizzuto, Alex, Potter, Rebecca, Pritchard, Orion, Quintana, Chris, Ramachandran, Ganesh, Reagor, Matthew J., Rhodes, David M., Roberts, Gabrielle, Sankaragomathi, Kannan, Satzinger, Kevin J., Schurkus, Henry F., Shearn, Michael J., Shorter, Aaron, Shutty, Noah, Shvarts, Vladimir, Sivak, Volodymyr, Small, Spencer, Smith, W. Clarke, Springer, Sofia, Sterling, George, Suchard, Jordan, Szasz, Aaron, Sztein, Alex, Thor, Douglas, Torunbalci, M. Mert, Vaishnav, Abeer, Vargas, Justin, Vdovichev, Sergey, Vidal, Guifre, Heidweiller, Catherine Vollgraff, Waltman, Steven, Wang, Shannon X., Ware, Brayden, White, Theodore, Wong, Kristi, Woo, Bryan W. K., Xing, Cheng, Yao, Z. Jamie, Yeh, Ping, Ying, Bicheng, Yoo, Juhwan, Yosri, Noureldin, Young, Grayson, Zalcman, Adam, Zhang, Yaxing, Zhu, Ningfeng, Zobris, Nicholas, Boixo, Sergio, Kelly, Julian, Lucero, Erik, Chen, Yu, Smelyanskiy, Vadim, Neven, Hartmut, Gammon-Smith, Adam, Pollmann, Frank, Knap, Michael, and Roushan, Pedram

Subjects
Quantum Physics, Condensed Matter - Strongly Correlated Electrons, High Energy Physics - Lattice
Abstract

Lattice gauge theories (LGTs) can be employed to understand a wide range of phenomena, from elementary particle scattering in high-energy physics to effective descriptions of many-body interactions in materials. Studying dynamical properties of emergent phases can be challenging as it requires solving many-body problems that are generally beyond perturbative limits. We investigate the dynamics of local excitations in a $\mathbb{Z}_2$ LGT using a two-dimensional lattice of superconducting qubits. We first construct a simple variational circuit which prepares low-energy states that have a large overlap with the ground state; then we create particles with local gates and simulate their quantum dynamics via a discretized time evolution. As the effective magnetic field is increased, our measurements show signatures of transitioning from deconfined to confined dynamics. For confined excitations, the magnetic field induces a tension in the string connecting them. Our method allows us to experimentally image string dynamics in a (2+1)D LGT from which we uncover two distinct regimes inside the confining phase: for weak confinement the string fluctuates strongly in the transverse direction, while for strong confinement transverse fluctuations are effectively frozen. In addition, we demonstrate a resonance condition at which dynamical string breaking is facilitated. Our LGT implementation on a quantum processor presents a novel set of techniques for investigating emergent particle and string dynamics.

Published
2024

12. Kinematic Lensing Inference II: Cluster Lensing with $\mathcal{O}$(1) Galaxies

Author

S., Pranjal R., Huff, Eric, Krause, Elisabeth, Eifler, Tim, Everett, Spencer, Huang, Yu-Hsiu, and Xu, Jiachuan

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We present the first detection of a cluster lensing signal with `Kinematic Lensing' (KL), a novel weak lensing method that combines photometry, spectroscopy, and the Tully-Fisher relation to enable shear measurements with individual source galaxies. This is the second paper in a two-part series aimed at measuring a KL signal from data. The first paper, arXiv:2209.11811, describes the inference pipeline, which jointly forward models galaxy imaging and spectroscopy, and demonstrates unbiased shear inference with simulated data. This paper presents measurements of the lensing signal from the galaxy cluster Abell 2261. We obtain spectroscopic observations of background disk galaxies in the cluster field selected from the CLASH Subaru catalog. The final sample consists of three source galaxies while the remaining are rejected due to insufficient signal-to-noise, spectroscopic failures, and inadequately sampled rotation curves. We apply the KL inference pipeline to the three sources and find the shear estimates to be in broad agreement with traditional weak lensing measurements. The typical shear measurement uncertainty for our sources is $\sigma(g_+)\approx 0.026$, which represents approximately a ten-fold improvement over the weak lensing shape noise. We identify target selection and observing strategy as the key avenues of improvement for future KL programs., Comment: v1: To be submitted to MNRAS. Comments are welcome!

Published
2024

13. 'Beautiful Chaos': Clinicians' Learning Experience in Collaborative Theater Programming

Author

Hannah Huff and Karen Whisenhunt Saar

Abstract

This study provides insights into speech-language pathology graduate student learning experiences during a community-based clinical rotation that targets skill development within an interdisciplinary theater program. Theater-based programming has shown some utility toward promoting pragmatic language skill development for children and teens outside of traditional clinical practice. In addition, the interdisciplinary nature of the program provides opportunities for student clinicians to engage in interprofessional collaboration with arts professionals. The purpose of this study was to explore SLP graduate students' experiences within a collaborative inclusion theater program. Semi-structured interviews collected from three graduate student clinicians were transcribed and analyzed according to Interpretive Phenomenological Analysis (IPA) methods to yield insights from student participant perspectives of this assigned clinical rotation. Analysis of interview data suggests participants found opportunities to problem-solve collaborative roles and balances while forming concrete ideas related to how to a therapeutic effect. The most salient concepts present across student accounts of experience included openness to the ongoing nature of the learning process, appreciation for the collaborative teaming involved in program activities, and the establishment of a set of ideas and tools to use for clinical utility. This study provides helpful information for clinical educators considering the benefit of embedding potential interprofessional collaborations with performing arts professionals in clinical education.

Published
2024

14. Emotions in Engineering Education: A Configurative Meta-Synthesis Systematic Review

Author

Johanna Lönngren, Alberto Bellocchi, Maria Berge, Pia Bøgelund, Inês Direito, James L. Huff, Khairiyah Mohd-Yusof, Homero Murzi, Nor Farahwahidah Abdul Rahman, and Roland Tormey

Abstract

Background: The study of emotions in engineering education (EEE) has increased in recent years, but this emerging, multidisciplinary body of research is dispersed and not well consolidated. This paper reports on the first systematic review of EEE research and scholarship. Purpose: The review aimed to critically assess how researchers and scholars in engineering education have conceptualized emotions and how those conceptualizations have been used to frame and conduct EEE research and scholarship. Scope/Method: The systematic review followed the procedures of a configurative meta-synthesis, mapping emotion theories and concepts, research purposes and methods, and citation patterns in the EEE literature. The review proceeded through five stages: (i) scoping and database searching; (ii) abstract screening, full text sifting, and full text review; (iii) pearling; (iv) scoping review, and (v) in-depth analysis for the meta-synthesis review. Two hundred and thirteen publications were included in the final analysis. Results: The results show that the EEE literature has not extensively engaged with the wide range of conceptualizations of emotion available in the educational, psychological, and sociological literature. Further, the focus on emotion often seems to have been unintentional and of secondary importance in studies whose primary goals were to study other phenomena. Conclusions: More research adopting intentional, theorized approaches to emotions will be crucial in further developing the field. To do justice to complex emotional phenomena in teaching and learning, future EEE research will also need to engage a broader range of conceptualizations of emotion and research methods, drawing on diverse disciplinary traditions.

Published
2024
Full Text
View/download PDF

15. Quantum error correction below the surface code threshold

Author

Acharya, Rajeev, Aghababaie-Beni, Laleh, Aleiner, Igor, Andersen, Trond I., Ansmann, Markus, Arute, Frank, Arya, Kunal, Asfaw, Abraham, Astrakhantsev, Nikita, Atalaya, Juan, Babbush, Ryan, Bacon, Dave, Ballard, Brian, Bardin, Joseph C., Bausch, Johannes, Bengtsson, Andreas, Bilmes, Alexander, Blackwell, Sam, Boixo, Sergio, Bortoli, Gina, Bourassa, Alexandre, Bovaird, Jenna, Brill, Leon, Broughton, Michael, Browne, David A., Buchea, Brett, Buckley, Bob B., Buell, David A., Burger, Tim, Burkett, Brian, Bushnell, Nicholas, Cabrera, Anthony, Campero, Juan, Chang, Hung-Shen, Chen, Yu, Chen, Zijun, Chiaro, Ben, Chik, Desmond, Chou, Charina, Claes, Jahan, Cleland, Agnetta Y., Cogan, Josh, Collins, Roberto, Conner, Paul, Courtney, William, Crook, Alexander L., Curtin, Ben, Das, Sayan, Davies, Alex, De Lorenzo, Laura, Debroy, Dripto M., Demura, Sean, Devoret, Michel, Di Paolo, Agustin, Donohoe, Paul, Drozdov, Ilya, Dunsworth, Andrew, Earle, Clint, Edlich, Thomas, Eickbusch, Alec, Elbag, Aviv Moshe, Elzouka, Mahmoud, Erickson, Catherine, Faoro, Lara, Farhi, Edward, Ferreira, Vinicius S., Burgos, Leslie Flores, Forati, Ebrahim, Fowler, Austin G., Foxen, Brooks, Ganjam, Suhas, Garcia, Gonzalo, Gasca, Robert, Genois, Élie, Giang, William, Gidney, Craig, Gilboa, Dar, Gosula, Raja, Dau, Alejandro Grajales, Graumann, Dietrich, Greene, Alex, Gross, Jonathan A., Habegger, Steve, Hall, John, Hamilton, Michael C., Hansen, Monica, Harrigan, Matthew P., Harrington, Sean D., Heras, Francisco J. H., Heslin, Stephen, Heu, Paula, Higgott, Oscar, Hill, Gordon, Hilton, Jeremy, Holland, George, Hong, Sabrina, Huang, Hsin-Yuan, Huff, Ashley, Huggins, William J., Ioffe, Lev B., Isakov, Sergei V., Iveland, Justin, Jeffrey, Evan, Jiang, Zhang, Jones, Cody, Jordan, Stephen, Joshi, Chaitali, Juhas, Pavol, Kafri, Dvir, Kang, Hui, Karamlou, Amir H., Kechedzhi, Kostyantyn, Kelly, Julian, Khaire, Trupti, Khattar, Tanuj, Khezri, Mostafa, Kim, Seon, Klimov, Paul V., Klots, Andrey R., Kobrin, Bryce, Kohli, Pushmeet, Korotkov, Alexander N., Kostritsa, Fedor, Kothari, Robin, Kozlovskii, Borislav, Kreikebaum, John Mark, Kurilovich, Vladislav D., Lacroix, Nathan, Landhuis, David, Lange-Dei, Tiano, Langley, Brandon W., Laptev, Pavel, Lau, Kim-Ming, Guevel, Loïck Le, Ledford, Justin, Lee, Kenny, Lensky, Yuri D., Leon, Shannon, Lester, Brian J., Li, Wing Yan, Li, Yin, Lill, Alexander T., Liu, Wayne, Livingston, William P., Locharla, Aditya, Lucero, Erik, Lundahl, Daniel, Lunt, Aaron, Madhuk, Sid, Malone, Fionn D., Maloney, Ashley, Mandrá, Salvatore, Martin, Leigh S., Martin, Steven, Martin, Orion, Maxfield, Cameron, McClean, Jarrod R., McEwen, Matt, Meeks, Seneca, Megrant, Anthony, Mi, Xiao, Miao, Kevin C., Mieszala, Amanda, Molavi, Reza, Molina, Sebastian, Montazeri, Shirin, Morvan, Alexis, Movassagh, Ramis, Mruczkiewicz, Wojciech, Naaman, Ofer, Neeley, Matthew, Neill, Charles, Nersisyan, Ani, Neven, Hartmut, Newman, Michael, Ng, Jiun How, Nguyen, Anthony, Nguyen, Murray, Ni, Chia-Hung, O'Brien, Thomas E., Oliver, William D., Opremcak, Alex, Ottosson, Kristoffer, Petukhov, Andre, Pizzuto, Alex, Platt, John, Potter, Rebecca, Pritchard, Orion, Pryadko, Leonid P., Quintana, Chris, Ramachandran, Ganesh, Reagor, Matthew J., Rhodes, David M., Roberts, Gabrielle, Rosenberg, Eliott, Rosenfeld, Emma, Roushan, Pedram, Rubin, Nicholas C., Saei, Negar, Sank, Daniel, Sankaragomathi, Kannan, Satzinger, Kevin J., Schurkus, Henry F., Schuster, Christopher, Senior, Andrew W., Shearn, Michael J., Shorter, Aaron, Shutty, Noah, Shvarts, Vladimir, Singh, Shraddha, Sivak, Volodymyr, Skruzny, Jindra, Small, Spencer, Smelyanskiy, Vadim, Smith, W. Clarke, Somma, Rolando D., Springer, Sofia, Sterling, George, Strain, Doug, Suchard, Jordan, Szasz, Aaron, Sztein, Alex, Thor, Douglas, Torres, Alfredo, Torunbalci, M. Mert, Vaishnav, Abeer, Vargas, Justin, Vdovichev, Sergey, Vidal, Guifre, Villalonga, Benjamin, Heidweiller, Catherine Vollgraff, Waltman, Steven, Wang, Shannon X., Ware, Brayden, Weber, Kate, White, Theodore, Wong, Kristi, Woo, Bryan W. K., Xing, Cheng, Yao, Z. Jamie, Yeh, Ping, Ying, Bicheng, Yoo, Juhwan, Yosri, Noureldin, Young, Grayson, Zalcman, Adam, Zhang, Yaxing, Zhu, Ningfeng, and Zobrist, Nicholas

Subjects
Quantum Physics
Abstract

Quantum error correction provides a path to reach practical quantum computing by combining multiple physical qubits into a logical qubit, where the logical error rate is suppressed exponentially as more qubits are added. However, this exponential suppression only occurs if the physical error rate is below a critical threshold. In this work, we present two surface code memories operating below this threshold: a distance-7 code and a distance-5 code integrated with a real-time decoder. The logical error rate of our larger quantum memory is suppressed by a factor of $\Lambda$ = 2.14 $\pm$ 0.02 when increasing the code distance by two, culminating in a 101-qubit distance-7 code with 0.143% $\pm$ 0.003% error per cycle of error correction. This logical memory is also beyond break-even, exceeding its best physical qubit's lifetime by a factor of 2.4 $\pm$ 0.3. We maintain below-threshold performance when decoding in real time, achieving an average decoder latency of 63 $\mu$s at distance-5 up to a million cycles, with a cycle time of 1.1 $\mu$s. To probe the limits of our error-correction performance, we run repetition codes up to distance-29 and find that logical performance is limited by rare correlated error events occurring approximately once every hour, or 3 $\times$ 10$^9$ cycles. Our results present device performance that, if scaled, could realize the operational requirements of large scale fault-tolerant quantum algorithms., Comment: 10 pages, 4 figures, Supplementary Information

Published
2024

16. SuperBIT Superpressure Flight Instrument Overview and Performance: Near diffraction-limited Astronomical Imaging from the Stratosphere

Author

Gill, Ajay S., Benton, Steven J., Damaren, Christopher J., Everett, Spencer W., Fraisse, Aurelien A., Hartley, John W., Harvey, David, Holder, Bradley, Huff, Eric M., Jauzac, Mathilde, Jones, William C., Lagattuta, David, Leung, Jason S. -Y., Li, Lun, Luu, Thuy Vy T., Massey, Richard, McCleary, Jacqueline E., Nagy, Johanna M., Netterfield, C. Barth, Paracha, Emaad, Redmond, Susan F., Rhodes, Jason D., Robertson, Andrew, Romualdez, L. Javier, Schmoll, Jürgen, Shaaban, Mohamed M., Sirks, Ellen L., Vassilakis, Georgios N., and Vitorelliand, André Z.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics, Physics - Instrumentation and Detectors
Abstract

SuperBIT was a 0.5-meter near-ultraviolet to near-infrared wide-field telescope that launched on a NASA superpressure balloon into the stratosphere from New Zealand for a 45-night flight. SuperBIT acquired multi-band images of galaxy clusters to study the properties of dark matter using weak gravitational lensing. We provide an overview of the instrument and its various subsystems. We then present the instrument performance from the flight, including the telescope and image stabilization system, the optical system, the power system, and the thermal system. SuperBIT successfully met the instrument's technical requirements, achieving a telescope pointing stability of 0.34 +/- 0.10 arcseconds, a focal plane image stability of 0.055 +/- 0.027 arcseconds, and a PSF FWHM of ~ 0.35 arcseconds over 5-minute exposures throughout the 45-night flight. The telescope achieved a near-diffraction limited point-spread function in all three science bands (u, b, and g). SuperBIT served as a pathfinder to the GigaBIT observatory, which will be a 1.34-meter near-ultraviolet to near-infrared balloon-borne telescope., Comment: 17 pages, 25 pages, published in the Astronomical Journal

Published
2024
Full Text
View/download PDF

17. Enhancing weak lensing redshift distribution characterization by optimizing the Dark Energy Survey Self-Organizing Map Photo-z method

Author

Campos, A., Yin, B., Dodelson, S., Amon, A., Alarcon, A., Sánchez, C., Bernstein, G. M., Giannini, G., Myles, J., Samuroff, S., Alves, O., Andrade-Oliveira, F., Bechtol, K., Becker, M. R., Blazek, J., Camacho, H., Rosell, A. Carnero, Kind, M. Carrasco, Cawthon, R., Chang, C., Chen, R., Choi, A., Cordero, J., Davis, C., DeRose, J., Diehl, H. T., Doux, C., Drlica-Wagner, A., Eckert, K., Eifler, T. F., Elvin-Poole, J., Everett, S., Fang, X., Ferté, A., Friedrich, O., Gatti, M., Gruen, D., Gruendl, R. A., Harrison, I., Hartley, W. G., Herner, K., Huang, H., Huff, E. M., Jarvis, M., Krause, E., Kuropatkin, N., Leget, P. -F., MacCrann, N., McCullough, J., Navarro-Alsina, A., Pandey, S., Prat, J., Raveri, M., Rollins, R. P., Roodman, A., Rosenfeld, R., Ross, A. J., Rykoff, E. S., Sanchez, J., Secco, L. F., Sevilla-Noarbe, I., Sheldon, E., Shin, T., Troxel, M. A., Tutusaus, I., Varga, T. N., Wechsler, R. H., Yanny, B., Zhang, Y., Zuntz, J., Aguena, M., Annis, J., Bacon, D., Bocquet, S., Brooks, D., Burke, D. L., Carretero, J., Castander, F. J., Costanzi, M., da Costa, L. N., De Vicente, J., Doel, P., Ferrero, I., Flaugher, B., Frieman, J., García-Bellido, J., Gaztanaga, E., Gutierrez, G., Hinton, S. R., Hollowood, D. L., Honscheid, K., James, D. J., Kuehn, K., Lima, M., Lin, H., Marshall, J. L., Mena-Fernández, J., Menanteau, F., Miquel, R., Ogando, R. L. C., Paterno, M., Pereira, M. E. S., Pieres, A., Malagón, A. A. Plazas, Porredon, A., Sanchez, E., Cid, D. Sanchez, Smith, M., Suchyta, E., Swanson, M. E. C., Tarle, G., To, C., Vikram, V., and Weaverdyck, N.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

Characterization of the redshift distribution of ensembles of galaxies is pivotal for large scale structure cosmological studies. In this work, we focus on improving the Self-Organizing Map (SOM) methodology for photometric redshift estimation (SOMPZ), specifically in anticipation of the Dark Energy Survey Year 6 (DES Y6) data. This data set, featuring deeper and fainter galaxies than DES Year 3 (DES Y3), demands adapted techniques to ensure accurate recovery of the underlying redshift distribution. We investigate three strategies for enhancing the existing SOM-based approach used in DES Y3: 1) Replacing the Y3 SOM algorithm with one tailored for redshift estimation challenges; 2) Incorporating $\textit{g}$-band flux information to refine redshift estimates (i.e. using $\textit{griz}$ fluxes as opposed to only $\textit{riz}$); 3) Augmenting redshift data for galaxies where available. These methods are applied to DES Y3 data, and results are compared to the Y3 fiducial ones. Our analysis indicates significant improvements with the first two strategies, notably reducing the overlap between redshift bins. By combining strategies 1 and 2, we have successfully managed to reduce redshift bin overlap in DES Y3 by up to 66$\%$. Conversely, the third strategy, involving the addition of redshift data for selected galaxies as an additional feature in the method, yields inferior results and is abandoned. Our findings contribute to the advancement of weak lensing redshift characterization and lay the groundwork for better redshift characterization in DES Year 6 and future stage IV surveys, like the Rubin Observatory.

Published
2024

18. Kinematic Lensing with the Dark Energy Spectroscopic Instrument -- Probing structure formation at very low redshift

Author

Xu, Jiachuan, Eifler, Tim, Wang, Eason, Krause, Elisabeth, Everett, Spencer, Huff, Eric, S., Pranjal R., and Huang, Yu-Hsiu

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We explore the science prospects of a 14,000 deg$^2$ Kinematic Lensing (KL) survey with the Dark Energy Spectroscopic Instrument (DESI) and overlapping imaging surveys. KL infers the cosmic shear signal by jointly forward modeling the observed photometric image and velocity field of a disk galaxy. The latter can be constrained by placing multiple DESI fibers along the galaxy's major and minor axis, a concept similar to the DESI Peculiar Velocity Survey. We study multiple subset galaxy samples of the DESI Legacy Survey Data Release 9 catalog and quantify the residual shape noise, $\sigma_\epsilon$, of each sample as a function of cuts in $r$-band magnitude using mock observations. We conduct simulated likelihood analyses for these galaxy samples and find that a DESI-KL program can place highly interesting constraints on structure formation at very low redshifts, i.e. $\sigma_8(z<0.15)$. We conclude that if the $S_8$ tension consolidates as a phenomenon, a DESI-KL survey can provide unique insights into this phenomenon in the very late-time Universe. Given the different footprints of DESI and Rubin Observatory's Legacy Survey of Space and Time (LSST), lensing results from both surveys are highly complementary and can be combined into a joint lensing survey. We further note that DESI-KL benefits multiple additional science cases, e.g. studies of modified gravity models when combined with peculiar velocity surveys, and dark matter studies that are based on galaxy-galaxy lensing of dwarf galaxies.

Published
2024

19. From SuperBIT to GigaBIT: Informing next-generation balloon-borne telescope design with Fine Guidance System flight data

Author

Voyer, Philippe, Benton, Steven J., Damaren, Christopher J., Everett, Spencer W., Fraisse, Aurelien A., Gill, Ajay S., Hartley, John W., Harvey, David, Henderson, Michael, Holder, Bradley, Huff, Eric M., Jauzac, Mathilde, Jones, William C., Lagattuta, David, Leung, Jason S. -Y., Li, Lun, Luu, Thuy Vy T., Massey, Richard, McCleary, Jacqueline E., Nagy, Johanna M., Netterfield, C. Barth, Paracha, Emaad, Redmond, Susan F., Rhodes, Jason D., Robertson, Andrew, Romualdez, L. Javier, Schmoll, Jürgen, Shaaban, Mohamed M., Sirks, Ellen L., Vassilakis, Georgios N., and Vitorelli, André Z.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The Super-pressure Balloon-borne Imaging Telescope (SuperBIT) is a near-diffraction-limited 0.5m telescope that launched via NASA's super-pressure balloon technology on April 16, 2023. SuperBIT achieved precise pointing control through the use of three nested frames in conjunction with an optical Fine Guidance System (FGS), resulting in an average image stability of 0.055" over 300-second exposures. The SuperBIT FGS includes a tip-tilt fast-steering mirror that corrects for jitter on a pair of focal plane star cameras. In this paper, we leverage the empirical data from SuperBIT's successful 45-night stratospheric mission to inform the FGS design for the next-generation balloon-borne telescope. The Gigapixel Balloon-borne Imaging Telescope (GigaBIT) is designed to be a 1.35m wide-field, high resolution imaging telescope, with specifications to extend the scale and capabilities beyond those of its predecessor SuperBIT. A description and analysis of the SuperBIT FGS will be presented along with methodologies for extrapolating this data to enhance GigaBIT's FGS design and fine pointing control algorithm. We employ a systems engineering approach to outline and formalize the design constraints and specifications for GigaBIT's FGS. GigaBIT, building on the SuperBIT legacy, is set to enhance high-resolution astronomical imaging, marking a significant advancement in the field of balloon-borne telescopes., Comment: 13 pages, 7 figures, SPIE Astronomical Telescopes + Instrumentation 2024

Published
2024

20. Managing O-RAN Networks: xApp Development from Zero to Hero

Author

Santos, Joao F., Huff, Alexandre, Campos, Daniel, Cardoso, Kleber V., Both, Cristiano B., and DaSilva, Luiz A.

Subjects
Computer Science - Networking and Internet Architecture, Electrical Engineering and Systems Science - Systems and Control
Abstract

The Open Radio Access Network (O-RAN) Alliance proposes an open architecture that disaggregates the RAN and supports executing custom control logic in near-real time from third-party applications, the xApps. Despite O-RAN's efforts, the creation of xApps remains a complex and time-consuming endeavor, aggravated by the sometimes fragmented, outdated, or deprecated documentation from the O-RAN Software Community (OSC). These challenges hinder academia and industry from developing and validating solutions and algorithms on O-RAN networks. This tutorial addresses this gap by providing the first comprehensive guide for developing xApps to manage the O-RAN ecosystem from theory to practice. We provide a thorough theoretical foundation of the O-RAN architecture and detail the functionality offered by Near Real-Time RAN Intelligent Controller (Near-RT RIC) components. We examine the xApp design and configuration. We explore the xApp lifecycle and demonstrate how to deploy and manage xApps on a Near-RT RIC. We address the xApps' interfaces and capabilities, accompanied by practical examples. We provide comprehensive details on how xApps can control the RAN. We discuss debugging strategies and good practices to aid the xApp developers in testing their xApps. Finally, we review the current landscape and open challenges for creating xApps.

Published
2024

21. The little coadd that could: Estimating shear from coadded images

Author

Armstrong, Robert, Sheldon, Erin, Huff, Eric, Bosch, Jim, Rykoff, Eli, Mandelbaum, Rachel, Kannawadi, Arun, Melchior, Peter, Lupton, Robert, Becker, Matthew R., Al-Sayyed, Yusra, and Collaboration, The LSST Dark Energy Science

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

Upcoming wide field surveys will have many overlapping epochs of the same region of sky. The conventional wisdom is that in order to reduce the errors sufficiently for systematics-limited measurements, like weak lensing, we must do simultaneous fitting of all the epochs. Using current algorithms this will require a significant amount of computing time and effort. In this paper, we revisit the potential of using coadds for shear measurements. We show on a set of image simulations that the multiplicative shear bias can be constrained below the 0.1% level on coadds, which is sufficient for future lensing surveys. We see no significant differences between simultaneous fitting and coadded approaches for two independent shear codes: Metacalibration and BFD. One caveat of our approach is the assumption of a principled coadd, i.e. the PSF is mathematically well-defined for all the input images. This requires us to reject CCD images that do not fully cover the coadd region. We estimate that the number of epochs that must be rejected for a survey like LSST is on the order of 20%, resulting in a small loss in depth of less than 0.1 magnitudes. We also put forward a cell-based coaddition scheme that meets the above requirements for unbiased weak lensing shear estimation in the context of LSST.

Published
2024

22. Modelling the redshift-space cluster-galaxy correlation function on Mpc scales with emulation of the pairwise velocity distribution

Author

Robertson, Andrew, Huff, Eric, Markovic, Katarina, and Li, Baojiu

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We present a method for modelling the cluster-galaxy correlation function in redshift-space, down to ~ Mpc scales. The method builds upon the so-called Galaxy Infall Kinematics (GIK) model, a parametric model for the pairwise velocities of galaxies with respect to nearby galaxy clusters. We fit the parameters of the GIK model to a suite of simulations run with different cosmologies, and use Gaussian Processes to emulate how the GIK parameters depend upon cosmology. This emulator can then be combined with knowledge of the real-space clustering of clusters and galaxies, to predict the cluster-galaxy correlation function in redshift space. Fitting this model to an observed correlation function enables the extraction of cosmological parameter constraints, and we present forecasts for a DESI-like survey. We also perform tests of the robustness of our constraints from fitting to mock data extracted from N-body simulations, finding that fitting to scales < 3 Mpc/h leads to a biased inference on cosmology, due to model misspecification on these scales. Finally, we discuss what steps will need to be taken in order to apply our method to real data., Comment: 24 pages, 11 figures, under review at MNRAS

Published
2024

23. Thermalization and criticality on an analogue–digital quantum simulator

Author

Andersen, T. I., Astrakhantsev, N., Karamlou, A. H., Berndtsson, J., Motruk, J., Szasz, A., Gross, J. A., Schuckert, A., Westerhout, T., Zhang, Y., Forati, E., Rossi, D., Kobrin, B., Paolo, A. Di, Klots, A. R., Drozdov, I., Kurilovich, V., Petukhov, A., Ioffe, L. B., Elben, A., Rath, A., Vitale, V., Vermersch, B., Acharya, R., Beni, L. A., Anderson, K., Ansmann, M., Arute, F., Arya, K., Asfaw, A., Atalaya, J., Ballard, B., Bardin, J. C., Bengtsson, A., Bilmes, A., Bortoli, G., Bourassa, A., Bovaird, J., Brill, L., Broughton, M., Browne, D. A., Buchea, B., Buckley, B. B., Buell, D. A., Burger, T., Burkett, B., Bushnell, N., Cabrera, A., Campero, J., Chang, H.-S., Chen, Z., Chiaro, B., Claes, J., Cleland, A. Y., Cogan, J., Collins, R., Conner, P., Courtney, W., Crook, A. L., Das, S., Debroy, D. M., Lorenzo, L. De, Barba, A. Del Toro, Demura, S., Donohoe, P., Dunsworth, A., Earle, C., Eickbusch, A., Elbag, A. M., Elzouka, M., Erickson, C., Faoro, L., Fatemi, R., Ferreira, V. S., Burgos, L. Flores, Fowler, A. G., Foxen, B., Ganjam, S., Gasca, R., Giang, W., Gidney, C., Gilboa, D., Giustina, M., Gosula, R., Dau, A. Grajales, Graumann, D., Greene, A., Habegger, S., Hamilton, M. C., Hansen, M., Harrigan, M. P., Harrington, S. D., Heslin, S., Heu, P., Hill, G., Hoffmann, M. R., Huang, H.-Y., Huang, T., Huff, A., Huggins, W. J., Isakov, S. V., Jeffrey, E., Jiang, Z., Jones, C., Jordan, S., Joshi, C., Juhas, P., Kafri, D., Kang, H., Kechedzhi, K., Khaire, T., Khattar, T., Khezri, M., Kieferová, M., Kim, S., Kitaev, A., Klimov, P., Korotkov, A. N., Kostritsa, F., Kreikebaum, J. M., Landhuis, D., Langley, B. W., Laptev, P., Lau, K.-M., Guevel, L. Le, Ledford, J., Lee, J., Lee, K. W., Lensky, Y. D., Lester, B. J., Li, W. Y., Lill, A. T., Liu, W., Livingston, W. P., Locharla, A., Lundahl, D., Lunt, A., Madhuk, S., Maloney, A., Mandrà, S., Martin, L. S., Martin, O., Martin, S., Maxfield, C., McClean, J. R., McEwen, M., Meeks, S., Miao, K. C., Mieszala, A., Molina, S., Montazeri, S., Morvan, A., Movassagh, R., Neill, C., Nersisyan, A., Newman, M., Nguyen, A., Nguyen, M., Ni, C.-H., Niu, M. Y., Oliver, W. D., Ottosson, K., Pizzuto, A., Potter, R., Pritchard, O., Pryadko, L. P., Quintana, C., Reagor, M. J., Rhodes, D. M., Roberts, G., Rocque, C., Rosenberg, E., Rubin, N. C., Saei, N., Sankaragomathi, K., Satzinger, K. J., Schurkus, H. F., Schuster, C., Shearn, M. J., Shorter, A., Shutty, N., Shvarts, V., Sivak, V., Skruzny, J., Small, S., Smith, W. Clarke, Springer, S., Sterling, G., Suchard, J., Szalay, M., Sztein, A., Thor, D., Torres, A., Torunbalci, M. M., Vaishnav, A., Vdovichev, S., Villalonga, B., Heidweiller, C. Vollgraff, Waltman, S., Wang, S. X., White, T., Wong, K., Woo, B. W. K., Xing, C., Yao, Z. Jamie, Yeh, P., Ying, B., Yoo, J., Yosri, N., Young, G., Zalcman, A., Zhu, N., Zobrist, N., Neven, H., Babbush, R., Boixo, S., Hilton, J., Lucero, E., Megrant, A., Kelly, J., Chen, Y., Smelyanskiy, V., Vidal, G., Roushan, P., Läuchli, A. M., Abanin, D. A., and Mi, X.

Published
2025
Full Text
View/download PDF

24. Trust in scientists and their role in society across 68 countries

Author

Cologna, Viktoria, Mede, Niels G., Berger, Sebastian, Besley, John, Brick, Cameron, Joubert, Marina, Maibach, Edward W., Mihelj, Sabina, Oreskes, Naomi, Schäfer, Mike S., van der Linden, Sander, Abdul Aziz, Nor Izzatina, Abdulsalam, Suleiman, Shamsi, Nurulaini Abu, Aczel, Balazs, Adinugroho, Indro, Alabrese, Eleonora, Aldoh, Alaa, Alfano, Mark, Ali, Innocent Mbulli, Alsobay, Mohammed, Altenmüller, Marlene, Alvarez, R. Michael, Amoako, Richard, Amollo, Tabitha, Ansah, Patrick, Apriliawati, Denisa, Azevedo, Flavio, Bajrami, Ani, Bardhan, Ronita, Bati, Keagile, Bertsou, Eri, Betsch, Cornelia, Bhatiya, Apurav Yash, Bhui, Rahul, Białobrzeska, Olga, Bilewicz, Michał, Bouguettaya, Ayoub, Breeden, Katherine, Bret, Amélie, Buchel, Ondrej, Cabrera-Álvarez, Pablo, Cagnoli, Federica, Calero Valdez, André, Callaghan, Timothy, Cases, Rizza Kaye, Çoksan, Sami, Czarnek, Gabriela, De Peuter, Steven, Debnath, Ramit, Delouvée, Sylvain, Di Stefano, Lucia, Díaz-Catalán, Celia, Doell, Kimberly C., Dohle, Simone, Douglas, Karen M., Dries, Charlotte, Dubrov, Dmitrii, Dzimińska, Małgorzata, Ecker, Ullrich K. H., Elbaek, Christian T., Elsherif, Mahmoud, Enke, Benjamin, Etienne, Tom W., Facciani, Matthew, Fage-Butler, Antoinette, Faisal, Md. Zaki, Fan, Xiaoli, Farhart, Christina, Feldhaus, Christoph, Ferreira, Marinus, Feuerriegel, Stefan, Fischer, Helen, Freundt, Jana, Friese, Malte, Fuglsang, Simon, Gallyamova, Albina, Garrido-Vásquez, Patricia, Garrido Vásquez, Mauricio E., Gatua, Winfred, Genschow, Oliver, Ghasemi, Omid, Gkinopoulos, Theofilos, Gloor, Jamie L., Goddard, Ellen, Gollwitzer, Mario, González-Brambila, Claudia, Gordon, Hazel, Grigoryev, Dmitry, Grimshaw, Gina M., Guenther, Lars, Haarstad, Håvard, Harari, Dana, Hawkins, Lelia N., Hensel, Przemysław, Hernández-Mondragón, Alma Cristal, Herziger, Atar, Huang, Guanxiong, Huff, Markus, Hurley, Mairéad, Ibadildin, Nygmet, Ishibashi, Maho, Islam, Mohammad Tarikul, Jeddi, Younes, Jin, Tao, Jones, Charlotte A., Jungkunz, Sebastian, Jurgiel, Dominika, Kabdulkair, Zhangir, Kao, Jo-Ju, Kavassalis, Sarah, Kerr, John R., Kitsa, Mariana, Klabíková Rábová, Tereza, Klein, Olivier, Koh, Hoyoun, Koivula, Aki, Kojan, Lilian, Komyaginskaya, Elizaveta, König, Laura, Koppel, Lina, Koren Nobre Cavalcante, Kochav, Kosachenko, Alexandra, Kotcher, John, Kranz, Laura S., Krishnan, Pradeep, Kristiansen, Silje, Krouwel, André, Kuppens, Toon, Kyza, Eleni A., Lamm, Claus, Lantian, Anthony, Lazić, Aleksandra, Lecuona, Oscar, Légal, Jean-Baptiste, Leviston, Zoe, Levy, Neil, Lindkvist, Amanda M., Lits, Grégoire, Löschel, Andreas, López Ortega, Alberto, Lopez-Villavicencio, Carlos, Lou, Nigel Mantou, Lucas, Chloe H., Lunz-Trujillo, Kristin, Marques, Mathew D., Mayer, Sabrina J., McKay, Ryan, Mercier, Hugo, Metag, Julia, Milfont, Taciano L., Miller, Joanne M., Mitkidis, Panagiotis, Monge-Rodríguez, Fredy, Motta, Matt, Mudra, Iryna, Muršič, Zarja, Namutebi, Jennifer, Newman, Eryn J., Nitschke, Jonas P., Ntui, Ntui-Njock Vincent, Nwogwugwu, Daniel, Ostermann, Thomas, Otterbring, Tobias, Palmer-Hague, Jaime, Pantazi, Myrto, Pärnamets, Philip, Parra Saiani, Paolo, Paruzel-Czachura, Mariola, Parzuchowski, Michal, Pavlov, Yuri G., Pearson, Adam R., Penner, Myron A., Pennington, Charlotte R., Petkanopoulou, Katerina, Petrović, Marija B., Pfänder, Jan, Pisareva, Dinara, Ploszaj, Adam, Poliaková, Karolína, Pronizius, Ekaterina, Pypno-Blajda, Katarzyna, Quiñones, Diwa Malaya A., Räsänen, Pekka, Rauchfleisch, Adrian, Rebitschek, Felix G., Refojo Seronero, Cintia, Rêgo, Gabriel, Reynolds, James P., Roche, Joseph, Rödder, Simone, Röer, Jan Philipp, Ross, Robert M., Ruin, Isabelle, Santos, Osvaldo, Santos, Ricardo R., Schmid, Philipp, Schulreich, Stefan, Scoggins, Bermond, Sharaf, Amena, Sheria Nfundiko, Justin, Shuckburgh, Emily, Six, Johan, Solak, Nevin, Späth, Leonhard, Spruyt, Bram, Standaert, Olivier, Stanley, Samantha K., Storms, Gert, Strahm, Noel, Syropoulos, Stylianos, Szaszi, Barnabas, Szumowska, Ewa, Tanaka, Mikihito, Teran-Escobar, Claudia, Todorova, Boryana, Toko, Abdoul Kafid, Tokrri, Renata, Toribio-Florez, Daniel, Tsakiris, Manos, Tyrala, Michael, Uluğ, Özden Melis, Uzoma, Ijeoma Chinwe, van Noord, Jochem, Varda, Christiana, Verheyen, Steven, Vilares, Iris, Vlasceanu, Madalina, von Bubnoff, Andreas, Walker, Iain, Warwas, Izabela, Weber, Marcel, Weninger, Tim, Westfal, Mareike, Wintterlin, Florian, Wojcik, Adrian Dominik, Xia, Ziqian, Xie, Jinliang, Zegler-Poleska, Ewa, Zenklusen, Amber, and Zwaan, Rolf A.

Published
2025
Full Text
View/download PDF

25. Initial surgery for spontaneous intestinal perforation in extremely low birth weight infants is not associated with mortality or in-hospital morbidities

Author

Hair, Amy B., Sullivan, Kevin M., Ahmad, Irfan, Zaniletti, Isabella, Acker, Shannon N., Premkumar, Muralidhar H., Reber, Kristina, Huff, Katie A., Nayak, Sujir Pritha, DiGeronimo, Robert, Kim, Jae, Roberts, Jessica, Markel, Troy A., Brozanski, Beverly, Sharma, Jotishna, Piazza, Anthony J., and Yanowitz, Toby D.

Published
2024
Full Text
View/download PDF

27. Affordance norms for 2825 concrete nouns

Author

Maxwell, Nicholas P., Huff, Mark J., Hajnal, Alen, Namias, Jacob M., Blau, Julia J. C., Day, Brian, Marsh, Kerry L., Meagher, Benjamin R., Shelley-Tremblay, John F., Thomas, Gray F., and Wagman, Jeffrey B.

Published
2024
Full Text
View/download PDF

28. Thermalization and Criticality on an Analog-Digital Quantum Simulator

Author

Andersen, Trond I., Astrakhantsev, Nikita, Karamlou, Amir H., Berndtsson, Julia, Motruk, Johannes, Szasz, Aaron, Gross, Jonathan A., Schuckert, Alexander, Westerhout, Tom, Zhang, Yaxing, Forati, Ebrahim, Rossi, Dario, Kobrin, Bryce, Di Paolo, Agustin, Klots, Andrey R., Drozdov, Ilya, Kurilovich, Vladislav D., Petukhov, Andre, Ioffe, Lev B., Elben, Andreas, Rath, Aniket, Vitale, Vittorio, Vermersch, Benoit, Acharya, Rajeev, Beni, Laleh Aghababaie, Anderson, Kyle, Ansmann, Markus, Arute, Frank, Arya, Kunal, Asfaw, Abraham, Atalaya, Juan, Ballard, Brian, Bardin, Joseph C., Bengtsson, Andreas, Bilmes, Alexander, Bortoli, Gina, Bourassa, Alexandre, Bovaird, Jenna, Brill, Leon, Broughton, Michael, Browne, David A., Buchea, Brett, Buckley, Bob B., Buell, David A., Burger, Tim, Burkett, Brian, Bushnell, Nicholas, Cabrera, Anthony, Campero, Juan, Chang, Hung-Shen, Chen, Zijun, Chiaro, Ben, Claes, Jahan, Cleland, Agnetta Y., Cogan, Josh, Collins, Roberto, Conner, Paul, Courtney, William, Crook, Alexander L., Das, Sayan, Debroy, Dripto M., De Lorenzo, Laura, Barba, Alexander Del Toro, Demura, Sean, Donohoe, Paul, Dunsworth, Andrew, Earle, Clint, Eickbusch, Alec, Elbag, Aviv Moshe, Elzouka, Mahmoud, Erickson, Catherine, Faoro, Lara, Fatemi, Reza, Ferreira, Vinicius S., Burgos, Leslie Flores, Fowler, Austin G., Foxen, Brooks, Ganjam, Suhas, Gasca, Robert, Giang, William, Gidney, Craig, Gilboa, Dar, Giustina, Marissa, Gosula, Raja, Dau, Alejandro Grajales, Graumann, Dietrich, Greene, Alex, Habegger, Steve, Hamilton, Michael C., Hansen, Monica, Harrigan, Matthew P., Harrington, Sean D., Heslin, Stephen, Heu, Paula, Hill, Gordon, Hoffmann, Markus R., Huang, Hsin-Yuan, Huang, Trent, Huff, Ashley, Huggins, William J., Isakov, Sergei V., Jeffrey, Evan, Jiang, Zhang, Jones, Cody, Jordan, Stephen, Joshi, Chaitali, Juhas, Pavol, Kafri, Dvir, Kang, Hui, Kechedzhi, Kostyantyn, Khaire, Trupti, Khattar, Tanuj, Khezri, Mostafa, Kieferová, Mária, Kim, Seon, Kitaev, Alexei, Klimov, Paul V., Korotkov, Alexander N., Kostritsa, Fedor, Kreikebaum, John Mark, Landhuis, David, Langley, Brandon W., Laptev, Pavel, Lau, Kim-Ming, Guevel, Loïck Le, Ledford, Justin, Lee, Joonho, Lee, Kenny, Lensky, Yuri D., Lester, Brian J., Li, Wing Yan, Lill, Alexander T., Liu, Wayne, Livingston, William P., Locharla, Aditya, Lundahl, Daniel, Lunt, Aaron, Madhuk, Sid, Maloney, Ashley, Mandrà, Salvatore, Martin, Leigh S., Martin, Orion, Martin, Steven, Maxfield, Cameron, McClean, Jarrod R., McEwen, Matt, Meeks, Seneca, Miao, Kevin C., Mieszala, Amanda, Molina, Sebastian, Montazeri, Shirin, Morvan, Alexis, Movassagh, Ramis, Neill, Charles, Nersisyan, Ani, Newman, Michael, Nguyen, Anthony, Nguyen, Murray, Ni, Chia-Hung, Niu, Murphy Yuezhen, Oliver, William D., Ottosson, Kristoffer, Pizzuto, Alex, Potter, Rebecca, Pritchard, Orion, Pryadko, Leonid P., Quintana, Chris, Reagor, Matthew J., Rhodes, David M., Roberts, Gabrielle, Rocque, Charles, Rosenberg, Eliott, Rubin, Nicholas C., Saei, Negar, Sankaragomathi, Kannan, Satzinger, Kevin J., Schurkus, Henry F., Schuster, Christopher, Shearn, Michael J., Shorter, Aaron, Shutty, Noah, Shvarts, Vladimir, Sivak, Volodymyr, Skruzny, Jindra, Small, Spencer, Smith, W. Clarke, Springer, Sofia, Sterling, George, Suchard, Jordan, Szalay, Marco, Sztein, Alex, Thor, Douglas, Torres, Alfredo, Torunbalci, M. Mert, Vaishnav, Abeer, Vdovichev, Sergey, Villalonga, Benjamin, Heidweiller, Catherine Vollgraff, Waltman, Steven, Wang, Shannon X., White, Theodore, Wong, Kristi, Woo, Bryan W., Xing, Cheng, Yao, Z. Jamie, Yeh, Ping, Ying, Bicheng, Yoo, Juhwan, Yosri, Noureldin, Young, Grayson, Zalcman, Adam, Zhu, Ningfeng, Zobrist, Nicholas, Neven, Hartmut, Babbush, Ryan, Boixo, Sergio, Hilton, Jeremy, Lucero, Erik, Megrant, Anthony, Kelly, Julian, Chen, Yu, Smelyanskiy, Vadim, Vidal, Guifre, Roushan, Pedram, Lauchli, Andreas M., Abanin, Dmitry A., and Mi, Xiao

Subjects
Quantum Physics, Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Strongly Correlated Electrons
Abstract

Understanding how interacting particles approach thermal equilibrium is a major challenge of quantum simulators. Unlocking the full potential of such systems toward this goal requires flexible initial state preparation, precise time evolution, and extensive probes for final state characterization. We present a quantum simulator comprising 69 superconducting qubits which supports both universal quantum gates and high-fidelity analog evolution, with performance beyond the reach of classical simulation in cross-entropy benchmarking experiments. Emulating a two-dimensional (2D) XY quantum magnet, we leverage a wide range of measurement techniques to study quantum states after ramps from an antiferromagnetic initial state. We observe signatures of the classical Kosterlitz-Thouless phase transition, as well as strong deviations from Kibble-Zurek scaling predictions attributed to the interplay between quantum and classical coarsening of the correlated domains. This interpretation is corroborated by injecting variable energy density into the initial state, which enables studying the effects of the eigenstate thermalization hypothesis (ETH) in targeted parts of the eigenspectrum. Finally, we digitally prepare the system in pairwise-entangled dimer states and image the transport of energy and vorticity during thermalization. These results establish the efficacy of superconducting analog-digital quantum processors for preparing states across many-body spectra and unveiling their thermalization dynamics.

Published
2024

29. Euclid. I. Overview of the Euclid mission

Author

Euclid Collaboration, Mellier, Y., Abdurro'uf, Barroso, J. A. Acevedo, Achúcarro, A., Adamek, J., Adam, R., Addison, G. E., Aghanim, N., Aguena, M., Ajani, V., Akrami, Y., Al-Bahlawan, A., Alavi, A., Albuquerque, I. S., Alestas, G., Alguero, G., Allaoui, A., Allen, S. W., Allevato, V., Alonso-Tetilla, A. V., Altieri, B., Alvarez-Candal, A., Alvi, S., Amara, A., Amendola, L., Amiaux, J., Andika, I. T., Andreon, S., Andrews, A., Angora, G., Angulo, R. E., Annibali, F., Anselmi, A., Anselmi, S., Arcari, S., Archidiacono, M., Aricò, G., Arnaud, M., Arnouts, S., Asgari, M., Asorey, J., Atayde, L., Atek, H., Atrio-Barandela, F., Aubert, M., Aubourg, E., Auphan, T., Auricchio, N., Aussel, B., Aussel, H., Avelino, P. P., Avgoustidis, A., Avila, S., Awan, S., Azzollini, R., Baccigalupi, C., Bachelet, E., Bacon, D., Baes, M., Bagley, M. B., Bahr-Kalus, B., Balaguera-Antolinez, A., Balbinot, E., Balcells, M., Baldi, M., Baldry, I., Balestra, A., Ballardini, M., Ballester, O., Balogh, M., Bañados, E., Barbier, R., Bardelli, S., Baron, M., Barreiro, T., Barrena, R., Barriere, J. -C., Barros, B. J., Barthelemy, A., Bartolo, N., Basset, A., Battaglia, P., Battisti, A. J., Baugh, C. M., Baumont, L., Bazzanini, L., Beaulieu, J. -P., Beckmann, V., Belikov, A. N., Bel, J., Bellagamba, F., Bella, M., Bellini, E., Benabed, K., Bender, R., Benevento, G., Bennett, C. L., Benson, K., Bergamini, P., Bermejo-Climent, J. R., Bernardeau, F., Bertacca, D., Berthe, M., Berthier, J., Bethermin, M., Beutler, F., Bevillon, C., Bhargava, S., Bhatawdekar, R., Bianchi, D., Bisigello, L., Biviano, A., Blake, R. P., Blanchard, A., Blazek, J., Blot, L., Bosco, A., Bodendorf, C., Boenke, T., Böhringer, H., Boldrini, P., Bolzonella, M., Bonchi, A., Bonici, M., Bonino, D., Bonino, L., Bonvin, C., Bon, W., Booth, J. T., Borgani, S., Borlaff, A. S., Borsato, E., Bose, B., Botticella, M. T., Boucaud, A., Bouche, F., Boucher, J. S., Boutigny, D., Bouvard, T., Bouwens, R., Bouy, H., Bowler, R. A. A., Bozza, V., Bozzo, E., Branchini, E., Brando, G., Brau-Nogue, S., Brekke, P., Bremer, M. N., Brescia, M., Breton, M. -A., Brinchmann, J., Brinckmann, T., Brockley-Blatt, C., Brodwin, M., Brouard, L., Brown, M. L., Bruton, S., Bucko, J., Buddelmeijer, H., Buenadicha, G., Buitrago, F., Burger, P., Burigana, C., Busillo, V., Busonero, D., Cabanac, R., Cabayol-Garcia, L., Cagliari, M. S., Caillat, A., Caillat, L., Calabrese, M., Calabro, A., Calderone, G., Calura, F., Quevedo, B. Camacho, Camera, S., Campos, L., Canas-Herrera, G., Candini, G. P., Cantiello, M., Capobianco, V., Cappellaro, E., Cappelluti, N., Cappi, A., Caputi, K. I., Cara, C., Carbone, C., Cardone, V. F., Carella, E., Carlberg, R. G., Carle, M., Carminati, L., Caro, F., Carrasco, J. M., Carretero, J., Carrilho, P., Duque, J. Carron, Carry, B., Carvalho, A., Carvalho, C. S., Casas, R., Casas, S., Casenove, P., Casey, C. M., Cassata, P., Castander, F. J., Castelao, D., Castellano, M., Castiblanco, L., Castignani, G., Castro, T., Cavet, C., Cavuoti, S., Chabaud, P. -Y., Chambers, K. C., Charles, Y., Charlot, S., Chartab, N., Chary, R., Chaumeil, F., Cho, H., Chon, G., Ciancetta, E., Ciliegi, P., Cimatti, A., Cimino, M., Cioni, M. -R. L., Claydon, R., Cleland, C., Clément, B., Clements, D. L., Clerc, N., Clesse, S., Codis, S., Cogato, F., Colbert, J., Cole, R. E., Coles, P., Collett, T. E., Collins, R. S., Colodro-Conde, C., Colombo, C., Combes, F., Conforti, V., Congedo, G., Conseil, S., Conselice, C. J., Contarini, S., Contini, T., Conversi, L., Cooray, A. R., Copin, Y., Corasaniti, P. -S., Corcho-Caballero, P., Corcione, L., Cordes, O., Corpace, O., Correnti, M., Costanzi, M., Costille, A., Courbin, F., Mifsud, L. Courcoult, Courtois, H. M., Cousinou, M. -C., Covone, G., Cowell, T., Cragg, C., Cresci, G., Cristiani, S., Crocce, M., Cropper, M., Crouzet, P. E, Csizi, B., Cuby, J. -G., Cucchetti, E., Cucciati, O., Cuillandre, J. -C., Cunha, P. A. C., Cuozzo, V., Daddi, E., D'Addona, M., Dafonte, C., Dagoneau, N., Dalessandro, E., Dalton, G. B., D'Amico, G., Dannerbauer, H., Danto, P., Das, I., Da Silva, A., da Silva, R., Doumerg, W. d'Assignies, Daste, G., Davies, J. E., Davini, S., Dayal, P., de Boer, T., Decarli, R., De Caro, B., Degaudenzi, H., Degni, G., de Jong, J. T. A., de la Bella, L. F., de la Torre, S., Delhaise, F., Delley, D., Delucchi, G., De Lucia, G., Denniston, J., De Paolis, F., De Petris, M., Derosa, A., Desai, S., Desjacques, V., Despali, G., Desprez, G., De Vicente-Albendea, J., Deville, Y., Dias, J. D. F., Díaz-Sánchez, A., Diaz, J. J., Di Domizio, S., Diego, J. M., Di Ferdinando, D., Di Giorgio, A. M., Dimauro, P., Dinis, J., Dolag, K., Dolding, C., Dole, H., Sánchez, H. Domínguez, Doré, O., Dournac, F., Douspis, M., Dreihahn, H., Droge, B., Dryer, B., Dubath, F., Duc, P. -A., Ducret, F., Duffy, C., Dufresne, F., Duncan, C. A. J., Dupac, X., Duret, V., Durrer, R., Durret, F., Dusini, S., Ealet, A., Eggemeier, A., Eisenhardt, P. R. M., Elbaz, D., Elkhashab, M. Y., Ellien, A., Endicott, J., Enia, A., Erben, T., Vigo, J. A. Escartin, Escoffier, S., Sanz, I. Escudero, Essert, J., Ettori, S., Ezziati, M., Fabbian, G., Fabricius, M., Fang, Y., Farina, A., Farina, M., Farinelli, R., Farrens, S., Faustini, F., Feltre, A., Ferguson, A. M. N., Ferrando, P., Ferrari, A. G., Ferré-Mateu, A., Ferreira, P. G., Ferreras, I., Ferrero, I., Ferriol, S., Ferruit, P., Filleul, D., Finelli, F., Finkelstein, S. L., Finoguenov, A., Fiorini, B., Flentge, F., Focardi, P., Fonseca, J., Fontana, A., Fontanot, F., Fornari, F., Fosalba, P., Fossati, M., Fotopoulou, S., Fouchez, D., Fourmanoit, N., Frailis, M., Fraix-Burnet, D., Franceschi, E., Franco, A., Franzetti, P., Freihoefer, J., Frenk, C. . S., Frittoli, G., Frugier, P. -A., Frusciante, N., Fumagalli, A., Fumagalli, M., Fumana, M., Fu, Y., Gabarra, L., Galeotta, S., Galluccio, L., Ganga, K., Gao, H., García-Bellido, J., Garcia, K., Gardner, J. P., Garilli, B., Gaspar-Venancio, L. -M., Gasparetto, T., Gautard, V., Gavazzi, R., Gaztanaga, E., Genolet, L., Santos, R. Genova, Gentile, F., George, K., Gerbino, M., Ghaffari, Z., Giacomini, F., Gianotti, F., Gibb, G. P. S., Gillard, W., Gillis, B., Ginolfi, M., Giocoli, C., Girardi, M., Giri, S. K., Goh, L. W. K., Gómez-Alvarez, P., Gonzalez-Perez, V., Gonzalez, A. H., Gonzalez, E. J., Gonzalez, J. C., Beauchamps, S. Gouyou, Gozaliasl, G., Gracia-Carpio, J., Grandis, S., Granett, B. R., Granvik, M., Grazian, A., Gregorio, A., Grenet, C., Grillo, C., Grupp, F., Gruppioni, C., Gruppuso, A., Guerbuez, C., Guerrini, S., Guidi, M., Guillard, P., Gutierrez, C. M., Guttridge, P., Guzzo, L., Gwyn, S., Haapala, J., Haase, J., Haddow, C. R., Hailey, M., Hall, A., Hall, D., Hamaus, N., Haridasu, B. S., Harnois-Déraps, J., Harper, C., Hartley, W. G., Hasinger, G., Hassani, F., Hatch, N. A., Haugan, S. V. H., Häußler, B., Heavens, A., Heisenberg, L., Helmi, A., Helou, G., Hemmati, S., Henares, K., Herent, O., Hernández-Monteagudo, C., Heuberger, T., Hewett, P. C., Heydenreich, S., Hildebrandt, H., Hirschmann, M., Hjorth, J., Hoar, J., Hoekstra, H., Holland, A. D., Holliman, M. S., Holmes, W., Hook, I., Horeau, B., Hormuth, F., Hornstrup, A., Hosseini, S., Hu, D., Hudelot, P., Hudson, M. J., Huertas-Company, M., Huff, E. M., Hughes, A. C. N., Humphrey, A., Hunt, L. K., Huynh, D. D., Ibata, R., Ichikawa, K., Iglesias-Groth, S., Ilbert, O., Ilić, S., Ingoglia, L., Iodice, E., Israel, H., Israelsson, U. E., Izzo, L., Jablonka, P., Jackson, N., Jacobson, J., Jafariyazani, M., Jahnke, K., Jain, B., Jansen, H., Jarvis, M. J., Jasche, J., Jauzac, M., Jeffrey, N., Jhabvala, M., Jimenez-Teja, Y., Muñoz, A. Jimenez, Joachimi, B., Johansson, P. H., Joudaki, S., Jullo, E., Kajava, J. J. E., Kang, Y., Kannawadi, A., Kansal, V., Karagiannis, D., Kärcher, M., Kashlinsky, A., Kazandjian, M. V., Keck, F., Keihänen, E., Kerins, E., Kermiche, S., Khalil, A., Kiessling, A., Kiiveri, K., Kilbinger, M., Kim, J., King, R., Kirkpatrick, C. C., Kitching, T., Kluge, M., Knabenhans, M., Knapen, J. H., Knebe, A., Kneib, J. -P., Kohley, R., Koopmans, L. V. E., Koskinen, H., Koulouridis, E., Kou, R., Kovács, A., Kovačić, I., Kowalczyk, A., Koyama, K., Kraljic, K., Krause, O., Kruk, S., Kubik, B., Kuchner, U., Kuijken, K., Kümmel, M., Kunz, M., Kurki-Suonio, H., Lacasa, F., Lacey, C. G., La Franca, F., Lagarde, N., Lahav, O., Laigle, C., La Marca, A., La Marle, O., Lamine, B., Lam, M. C., Lançon, A., Landt, H., Langer, M., Lapi, A., Larcheveque, C., Larsen, S. S., Lattanzi, M., Laudisio, F., Laugier, D., Laureijs, R., Laurent, V., Lavaux, G., Lawrenson, A., Lazanu, A., Lazeyras, T., Boulc'h, Q. Le, Brun, A. M. C. Le, Brun, V. Le, Leclercq, F., Lee, S., Graet, J. Le, Legrand, L., Leirvik, K. N., Jeune, M. Le, Lembo, M., Mignant, D. Le, Lepinzan, M. D., Lepori, F., Reun, A. Le, Leroy, G., Lesci, G. F., Lesgourgues, J., Leuzzi, L., Levi, M. E., Liaudat, T. I., Libet, G., Liebing, P., Ligori, S., Lilje, P. B., Lin, C. -C., Linde, D., Linder, E., Lindholm, V., Linke, L., Li, S. -S., Liu, S. J., Lloro, I., Lobo, F. S. N., Lodieu, N., Lombardi, M., Lombriser, L., Lonare, P., Longo, G., López-Caniego, M., Lopez, X. Lopez, Alvarez, J. Lorenzo, Loureiro, A., Loveday, J., Lusso, E., Macias-Perez, J., Maciaszek, T., Maggio, G., Magliocchetti, M., Magnard, F., Magnier, E. A., Magro, A., Mahler, G., Mainetti, G., Maino, D., Maiorano, E., Malavasi, N., Mamon, G. A., Mancini, C., Mandelbaum, R., Manera, M., Manjón-García, A., Mannucci, F., Mansutti, O., Outeiro, M. Manteiga, Maoli, R., Maraston, C., Marcin, S., Marcos-Arenal, P., Margalef-Bentabol, B., Marggraf, O., Marinucci, D., Marinucci, M., Markovic, K., Marleau, F. R., Marpaud, J., Martignac, J., Martín-Fleitas, J., Martin-Moruno, P., Martin, E. L., Martinelli, M., Martinet, N., Martin, H., Martins, C. J. A. P., Marulli, F., Massari, D., Massey, R., Masters, D. C., Matarrese, S., Matsuoka, Y., Matthew, S., Maughan, B. J., Mauri, N., Maurin, L., Maurogordato, S., McCarthy, K., McConnachie, A. W., McCracken, H. J., McDonald, I., McEwen, J. D., McPartland, C. J. R., Medinaceli, E., Mehta, V., Mei, S., Melchior, M., Melin, J. -B., Ménard, B., Mendes, J., Mendez-Abreu, J., Meneghetti, M., Mercurio, A., Merlin, E., Metcalf, R. B., Meylan, G., Migliaccio, M., Mignoli, M., Miller, L., Miluzio, M., Milvang-Jensen, B., Mimoso, J. P., Miquel, R., Miyatake, H., Mobasher, B., Mohr, J. J., Monaco, P., Monguió, M., Montoro, A., Mora, A., Dizgah, A. Moradinezhad, Moresco, M., Moretti, C., Morgante, G., Morisset, N., Moriya, T. J., Morris, P. W., Mortlock, D. J., Moscardini, L., Mota, D. F., Mottet, S., Moustakas, L. A., Moutard, T., Müller, T., Munari, E., Murphree, G., Murray, C., Murray, N., Musi, P., Nadathur, S., Nagam, B. C., Nagao, T., Naidoo, K., Nakajima, R., Nally, C., Natoli, P., Navarro-Alsina, A., Girones, D. Navarro, Neissner, C., Nersesian, A., Nesseris, S., Nguyen-Kim, H. N., Nicastro, L., Nichol, R. C., Nielbock, M., Niemi, S. -M., Nieto, S., Nilsson, K., Noller, J., Norberg, P., Nouri-Zonoz, A., Ntelis, P., Nucita, A. A., Nugent, P., Nunes, N. J., Nutma, T., Ocampo, I., Odier, J., Oesch, P. A., Oguri, M., Oliveira, D. Magalhaes, Onoue, M., Oosterbroek, T., Oppizzi, F., Ordenovic, C., Osato, K., Pacaud, F., Pace, F., Padilla, C., Paech, K., Pagano, L., Page, M. J., Palazzi, E., Paltani, S., Pamuk, S., Pandolfi, S., Paoletti, D., Paolillo, M., Papaderos, P., Pardede, K., Parimbelli, G., Parmar, A., Partmann, C., Pasian, F., Passalacqua, F., Paterson, K., Patrizii, L., Pattison, C., Paulino-Afonso, A., Paviot, R., Peacock, J. A., Pearce, F. R., Pedersen, K., Peel, A., Peletier, R. F., Ibanez, M. Pellejero, Pello, R., Penny, M. T., Percival, W. J., Perez-Garrido, A., Perotto, L., Pettorino, V., Pezzotta, A., Pezzuto, S., Philippon, A., Pierre, M., Piersanti, O., Pietroni, M., Piga, L., Pilo, L., Pires, S., Pisani, A., Pizzella, A., Pizzuti, L., Plana, C., Polenta, G., Pollack, J. E., Poncet, M., Pöntinen, M., Pool, P., Popa, L. A., Popa, V., Popp, J., Porciani, C., Porth, L., Potter, D., Poulain, M., Pourtsidou, A., Pozzetti, L., Prandoni, I., Pratt, G. W., Prezelus, S., Prieto, E., Pugno, A., Quai, S., Quilley, L., Racca, G. D., Raccanelli, A., Rácz, G., Radinović, S., Radovich, M., Ragagnin, A., Ragnit, U., Raison, F., Ramos-Chernenko, N., Ranc, C., Rasera, Y., Raylet, N., Rebolo, R., Refregier, A., Reimberg, P., Reiprich, T. H., Renk, F., Renzi, A., Retre, J., Revaz, Y., Reylé, C., Reynolds, L., Rhodes, J., Ricci, F., Ricci, M., Riccio, G., Ricken, S. O., Rissanen, S., Risso, I., Rix, H. -W., Robin, A. C., Rocca-Volmerange, B., Rocci, P. -F., Rodenhuis, M., Rodighiero, G., Monroy, M. Rodriguez, Rollins, R. P., Romanello, M., Roman, J., Romelli, E., Romero-Gomez, M., Roncarelli, M., Rosati, P., Rosset, C., Rossetti, E., Roster, W., Rottgering, H. J. A., Rozas-Fernández, A., Ruane, K., Rubino-Martin, J. A., Rudolph, A., Ruppin, F., Rusholme, B., Sacquegna, S., Sáez-Casares, I., Saga, S., Saglia, R., Sahlén, M., Saifollahi, T., Sakr, Z., Salvalaggio, J., Salvaterra, R., Salvati, L., Salvato, M., Salvignol, J. -C., Sánchez, A. G., Sanchez, E., Sanders, D. B., Sapone, D., Saponara, M., Sarpa, E., Sarron, F., Sartori, S., Sartoris, B., Sassolas, B., Sauniere, L., Sauvage, M., Sawicki, M., Scaramella, R., Scarlata, C., Scharré, L., Schaye, J., Schewtschenko, J. A., Schindler, J. -T., Schinnerer, E., Schirmer, M., Schmidt, F., Schmidt, M., Schneider, A., Schneider, M., Schneider, P., Schöneberg, N., Schrabback, T., Schultheis, M., Schulz, S., Schuster, N., Schwartz, J., Sciotti, D., Scodeggio, M., Scognamiglio, D., Scott, D., Scottez, V., Secroun, A., Sefusatti, E., Seidel, G., Seiffert, M., Sellentin, E., Selwood, M., Semboloni, E., Sereno, M., Serjeant, S., Serrano, S., Setnikar, G., Shankar, F., Sharples, R. M., Short, A., Shulevski, A., Shuntov, M., Sias, M., Sikkema, G., Silvestri, A., Simon, P., Sirignano, C., Sirri, G., Skottfelt, J., Slezak, E., Sluse, D., Smith, G. P., Smith, L. C., Smith, R. E., Smit, S. J. A., Soldano, F., Solheim, B. G. B., Sorce, J. G., Sorrenti, F., Soubrie, E., Spinoglio, L., Mancini, A. Spurio, Stadel, J., Stagnaro, L., Stanco, L., Stanford, S. A., Starck, J. -L., Stassi, P., Steinwagner, J., Stern, D., Stone, C., Strada, P., Strafella, F., Stramaccioni, D., Surace, C., Sureau, F., Suyu, S. H., Swindells, I., Szafraniec, M., Szapudi, I., Taamoli, S., Talia, M., Tallada-Crespí, P., Tanidis, K., Tao, C., Tarrío, P., Tavagnacco, D., Taylor, A. N., Taylor, J. E., Taylor, P. L., Teixeira, E. M., Tenti, M., Idiago, P. Teodoro, Teplitz, H. I., Tereno, I., Tessore, N., Testa, V., Testera, G., Tewes, M., Teyssier, R., Theret, N., Thizy, C., Thomas, P. D., Toba, Y., Toft, S., Toledo-Moreo, R., Tolstoy, E., Tommasi, E., Torbaniuk, O., Torradeflot, F., Tortora, C., Tosi, S., Tosti, S., Trifoglio, M., Troja, A., Trombetti, T., Tronconi, A., Tsedrik, M., Tsyganov, A., Tucci, M., Tutusaus, I., Uhlemann, C., Ulivi, L., Urbano, M., Vacher, L., Vaillon, L., Valageas, P., Valdes, I., Valentijn, E. A., Valenziano, L., Valieri, C., Valiviita, J., Broeck, M. Van den, Vassallo, T., Vavrek, R., Vega-Ferrero, J., Venemans, B., Venhola, A., Ventura, S., Kleijn, G. Verdoes, Vergani, D., Verma, A., Vernizzi, F., Veropalumbo, A., Verza, G., Vescovi, C., Vibert, D., Viel, M., Vielzeuf, P., Viglione, C., Viitanen, A., Villaescusa-Navarro, F., Vinciguerra, S., Visticot, F., Voggel, K., von Wietersheim-Kramsta, M., Vriend, W. J., Wachter, S., Walmsley, M., Walth, G., Walton, D. M., Walton, N. A., Wander, M., Wang, L., Wang, Y., Weaver, J. R., Weller, J., Wetzstein, M., Whalen, D. J., Whittam, I. H., Widmer, A., Wiesmann, M., Wilde, J., Williams, O. R., Winther, H. -A., Wittje, A., Wong, J. H. W., Wright, A. H., Yankelevich, V., Yeung, H. W., Yoon, M., Youles, S., Yung, L. Y. A., Zacchei, A., Zalesky, L., Zamorani, G., Vitorelli, A. Zamorano, Marc, M. Zanoni, Zennaro, M., Zerbi, F. M., Zinchenko, I. A., Zoubian, J., Zucca, E., and Zumalacarregui, M.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The current standard model of cosmology successfully describes a variety of measurements, but the nature of its main ingredients, dark matter and dark energy, remains unknown. Euclid is a medium-class mission in the Cosmic Vision 2015-2025 programme of the European Space Agency (ESA) that will provide high-resolution optical imaging, as well as near-infrared imaging and spectroscopy, over about 14,000 deg^2 of extragalactic sky. In addition to accurate weak lensing and clustering measurements that probe structure formation over half of the age of the Universe, its primary probes for cosmology, these exquisite data will enable a wide range of science. This paper provides a high-level overview of the mission, summarising the survey characteristics, the various data-processing steps, and data products. We also highlight the main science objectives and expected performance., Comment: Accepted for publication in the A&A special issue`Euclid on Sky'

Published
2024

30. Dark Energy Survey Year 3 results: simulation-based cosmological inference with wavelet harmonics, scattering transforms, and moments of weak lensing mass maps II. Cosmological results

Author

Gatti, M., Campailla, G., Jeffrey, N., Whiteway, L., Porredon, A., Prat, J., Williamson, J., Raveri, M., Jain, B., Ajani, V., Giannini, G., Yamamoto, M., Zhou, C., Blazek, J., Anbajagane, D., Samuroff, S., Kacprzak, T., Alarcon, A., Amon, A., Bechtol, K., Becker, M., Bernstein, G., Campos, A., Chang, C., Chen, R., Choi, A., Davis, C., Derose, J., Diehl, H. T., Dodelson, S., Doux, C., Eckert, K., Elvin-Poole, J., Everett, S., Ferte, A., Gruen, D., Gruendl, R., Harrison, I., Hartley, W. G., Herner, K., Huff, E. M., Jarvis, M., Kuropatkin, N., Leget, P. F., MacCrann, N., McCullough, J., Myles, J., Navarro-Alsina, A., Pandey, S., Rollins, R. P., Roodman, A., Sanchez, C., Secco, L. F., Sevilla-Noarbe, I., Sheldon, E., Shin, T., Troxel, M., Tutusaus, I., Varga, T. N., Yanny, B., Yin, B., Zhang, Y., Zuntz, J., Abbott, T. M. C., Aguena, M., Allam, S. S., Alves, O., Andrade-Oliveira, F., Bacon, D., Bocquet, S., Brooks, D., Rosell, A. Carnero, Carretero, J., da Costa, L. N., Pereira, M. E. S., De Vicente, J., Ferrero, I., Frieman, J., García-Bellido, J., Gaztanaga, E., Gutierrez, G., Hinton, S. R., Hollowood, D. L., Honscheid, K., James, D. J., Kuehn, K., Lahav, O., Lee, S., Marshall, J. L., Mena-Fernández, J., Miquel, R., Pieres, A., Malagón, A. A. Plazas, Sanchez, E., Cid, D. Sanchez, Schubnell, M., Smith, M., Suchyta, E., Tarle, G., Weaverdyck, N., Weller, J., and Wiseman, P.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We present a simulation-based cosmological analysis using a combination of Gaussian and non-Gaussian statistics of the weak lensing mass (convergence) maps from the first three years (Y3) of the Dark Energy Survey (DES). We implement: 1) second and third moments; 2) wavelet phase harmonics; 3) the scattering transform. Our analysis is fully based on simulations, spans a space of seven $\nu w$CDM cosmological parameters, and forward models the most relevant sources of systematics inherent in the data: masks, noise variations, clustering of the sources, intrinsic alignments, and shear and redshift calibration. We implement a neural network compression of the summary statistics, and we estimate the parameter posteriors using a simulation-based inference approach. Including and combining different non-Gaussian statistics is a powerful tool that strongly improves constraints over Gaussian statistics (in our case, the second moments); in particular, the Figure of Merit $\textrm{FoM}(S_8, \Omega_{\textrm{m}})$ is improved by 70 percent ($\Lambda$CDM) and 90 percent ($w$CDM). When all the summary statistics are combined, we achieve a 2 percent constraint on the amplitude of fluctuations parameter $S_8 \equiv \sigma_8 (\Omega_{\textrm{m}}/0.3)^{0.5}$, obtaining $S_8 = 0.794 \pm 0.017$ ($\Lambda$CDM) and $S_8 = 0.817 \pm 0.021$ ($w$CDM). The constraints from different statistics are shown to be internally consistent (with a $p$-value>0.1 for all combinations of statistics examined). We compare our results to other weak lensing results from the DES Y3 data, finding good consistency; we also compare with results from external datasets, such as \planck{} constraints from the Cosmic Microwave Background, finding statistical agreement, with discrepancies no greater than $<2.2\sigma$., Comment: 24 pages, 13 figures, to be submitted to PRD. Comments welcome!

Published
2024

31. Diagnosing and Predicting Autonomous Vehicle Operational Safety Using Multiple Simulation Modalities and a Virtual Environment

Author

Beck, Joe, Huff, Shean, and Chakraborty, Subhadeep

Subjects
Computer Science - Robotics, Electrical Engineering and Systems Science - Systems and Control
Abstract

Even as technology and performance gains are made in the sphere of automated driving, safety concerns remain. Vehicle simulation has long been seen as a tool to overcome the cost associated with a massive amount of on-road testing for development and discovery of safety critical "edge-cases". However, purely software-based vehicle models may leave a large realism gap between their real-world counterparts in terms of dynamic response, and highly realistic vehicle-in-the-loop (VIL) simulations that encapsulate a virtual world around a physical vehicle may still be quite expensive to produce and similarly time intensive as on-road testing. In this work, we demonstrate an AV simulation test bed that combines the realism of vehicle-in-the-loop (VIL) simulation with the ease of implementation of model-in-the-loop (MIL) simulation. The setup demonstrated in this work allows for response diagnosis for the VIL simulations. By observing causal links between virtual weather and lighting conditions that surround the virtual depiction of our vehicle, the vision-based perception model and controller of Openpilot, and the dynamic response of our physical vehicle under test, we can draw conclusions regarding how the perceived environment contributed to vehicle response. Conversely, we also demonstrate response prediction for the MIL setup, where the need for a physical vehicle is not required to draw richer conclusions around the impact of environmental conditions on AV performance than could be obtained with VIL simulation alone. These combine for a simulation setup with accurate real-world implications for edge-case discovery that is both cost effective and time efficient to implement., Comment: Preprint. Under Review

Published
2024

32. Influence of Solution Efficiency and Valence of Instruction on Additive and Subtractive Solution Strategies in Humans and GPT-4

Author

Uhler, Lydia, Jordan, Verena, Buder, Jürgen, Huff, Markus, and Papenmeier, Frank

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Generative artificial intelligences, particularly large language models (LLMs), play an increasingly prominent role in human decision-making contexts, necessitating transparency about their capabilities. While prior studies have shown addition biases in humans (Adams et al., 2021) and OpenAI's GPT-3 (Winter et al., 2023), this study extends the research by comparing human and GPT-4 problem-solving across both spatial and linguistic tasks, with variations in solution efficiency and valence of task instruction. Four preregistered experiments with 588 participants from the U.S. and 680 GPT-4 iterations revealed a stronger tendency towards additive transformations in GPT-4 than in humans. Human participants were less likely to use additive strategies when subtraction was relatively more efficient than when addition and subtraction were equally efficient. GPT-4 exhibited the opposite behavior, with a strong addition bias when subtraction was more efficient. In terms of valence of task instruction, GPT-4's use of additive strategies increased when instructed to "improve" (positive) rather than "edit" (neutral). These findings demonstrate that biases in human problem-solving are amplified in GPT-4, and that LLM behavior differs from human efficiency-based strategies. This highlights the limitations of LLMs and the need for caution when using them in real-world applications., Comment: 29 pages, 4 figures, 2 tables

Published
2024

33. Weak lensing combined with the kinetic Sunyaev Zel'dovich effect: A study of baryonic feedback

Author

Bigwood, L., Amon, A., Schneider, A., Salcido, J., McCarthy, I. G., Preston, C., Sanchez, D., Sijacki, D., Schaan, E., Ferraro, S., Battaglia, N., Chen, A., Dodelson, S., Roodman, A., Pieres, A., Ferte, A., Alarcon, A., Drlica-Wagner, A., Choi, A., Navarro-Alsina, A., Campos, A., Ross, A. J., Rosell, A. Carnero, Yin, B., Yanny, B., Sanchez, C., Chang, C., Davis, C., Doux, C., Gruen, D., Rykoff, E. S., Huff, E. M., Sheldon, E., Tarsitano, F., Andrade-Oliveira, F., Bernstein, G. M., Giannini, G., Diehl, H. T., Huang, H., Harrison, I., Sevilla-Noarbe, I., Tutusaus, I., Elvin-Poole, J., McCullough, J., Zuntz, J., Blazek, J., DeRose, J., Cordero, J., Prat, J., Myles, J., Eckert, K., Bechtol, K., Herner, K., Secco, L. F., Gatti, M., Raveri, M., Kind, M. Carrasco, Becker, M. R., Troxel, M. A., Jarvis, M., MacCrann, N., Friedrich, O., Alves, O., Leget, P. -F., Chen, R., Rollins, R. P., Wechsler, R. H., Gruendl, R. A., Cawthon, R., Allam, S., Bridle, S. L., Pandey, S., Everett, S., Shin, T., Hartley, W. G., Fang, X., Zhang, Y., Aguena, M., Annis, J., Bacon, D., Bertin, E., Bocquet, S., Brooks, D., Carretero, J., Castander, F. J., da Costa, L. N., Pereira, M. E. S., De Vicente, J., Desai, S., Doel, P., Ferrero, I., Flaugher, B., Frieman, J., Garcia-Bellido, J., Gaztanaga, E., Gutierrez, G., Hinton, S. R., Hollowood, D. L., Honscheid, K., Huterer, D., James, D. J., Kuehn, K., Lahav, O., Lee, S., Marshall, J. L., Mena-Fernandez, J., Miquel, R., Muir, J., Paterno, M., Malagon, A. A. Plazas, Porredon, A., Romer, A. K., Samuroff, S., Sanchez, E., Cid, D. Sanchez, Smith, M., Soares-Santos, M., Suchyta, E., Swanson, M. E. C., Tarle, G., To, C., Weaverdyck, N., Weller, J., Wiseman, P., and Yamamoto, M.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract

Extracting precise cosmology from weak lensing surveys requires modelling the non-linear matter power spectrum, which is suppressed at small scales due to baryonic feedback processes. However, hydrodynamical galaxy formation simulations make widely varying predictions for the amplitude and extent of this effect. We use measurements of Dark Energy Survey Year 3 weak lensing (WL) and Atacama Cosmology Telescope DR5 kinematic Sunyaev-Zel'dovich (kSZ) to jointly constrain cosmological and astrophysical baryonic feedback parameters using a flexible analytical model, `baryonification'. First, using WL only, we compare the $S_8$ constraints using baryonification to a simulation-calibrated halo model, a simulation-based emulator model and the approach of discarding WL measurements on small angular scales. We find that model flexibility can shift the value of $S_8$ and degrade the uncertainty. The kSZ provides additional constraints on the astrophysical parameters and shifts $S_8$ to $S_8=0.823^{+0.019}_{-0.020}$, a higher value than attained using the WL-only analysis. We measure the suppression of the non-linear matter power spectrum using WL + kSZ and constrain a mean feedback scenario that is more extreme than the predictions from most hydrodynamical simulations. We constrain the baryon fractions and the gas mass fractions and find them to be generally lower than inferred from X-ray observations and simulation predictions. We conclude that the WL + kSZ measurements provide a new and complementary benchmark for building a coherent picture of the impact of gas around galaxies across observations.

Published
2024

34. Astrophysical systematics in Kinematic Lensing: quantifying an Intrinsic Alignment analog

Author

Huang, Yu-Hsiu, Krause, Elisabeth, Xu, Jiachuan, Eifler, Tim, S., Pranjal R., and Huff, Eric

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

Kinematic lensing (KL) is a new weak lensing technique that reduces shape noise for disk galaxies by including spectroscopically measured galaxy kinematics in addition to photometrically measured galaxy shapes. Since KL utilizes the Tully-Fisher relation, any correlation of this relation with the local environment may bias the cosmological interpretation. For the first time, we explore such a Tully-Fisher environmental dependence (TED) effect as a potential astrophysical systematic for KL. Our derivation of the TED systematic can be described in a similar analytical form as intrinsic alignment for traditional weak lensing. We demonstrate analytically that TED only impacts KL if intrinsic aligment for disk galaxies is non-zero. We further use IllustrisTNG simulations to quantify the TED effect. Our two-point correlation measurements do not yield any additional coherent signals that would indicate a systematic bias on KL, within the uncertainties set by the simulation volume., Comment: 12 pages, 5 figures, published in PRD

Published
2024
Full Text
View/download PDF

35. Towards a Psychology of Machines: Large Language Models Predict Human Memory

Author

Huff, Markus and Ulakçı, Elanur

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Large language models (LLMs), such as ChatGPT, have shown remarkable abilities in natural language processing, opening new avenues in psychological research. This study explores whether LLMs can predict human memory performance in tasks involving garden-path sentences and contextual information. In the first part, we used ChatGPT to rate the relatedness and memorability of garden-path sentences preceded by either fitting or unfitting contexts. In the second part, human participants read the same sentences, rated their relatedness, and completed a surprise memory test. The results demonstrated that ChatGPT's relatedness ratings closely matched those of the human participants, and its memorability ratings effectively predicted human memory performance. Both LLM and human data revealed that higher relatedness in the unfitting context condition was associated with better memory performance, aligning with probabilistic frameworks of context-dependent learning. These findings suggest that LLMs, despite lacking human-like memory mechanisms, can model aspects of human cognition and serve as valuable tools in psychological research. We propose the field of machine psychology to explore this interplay between human cognition and artificial intelligence, offering a bidirectional approach where LLMs can both benefit from and contribute to our understanding of human cognitive processes., Comment: 34 pages, 3 figures, 2 tables

Published
2024

36. Deep learning enables accurate soft tissue tendon deformation estimation in vivo via ultrasound imaging.

Author

Huff, Reece, Houghton, Frederick, Earl, Conner, Ghajar-Rahimi, Elnaz, Dogra, Ishan, Yu, Denny, Harris-Adamson, Carisa, Goergen, Craig, and OConnell, Grace

Subjects
Biomechanics, Deep learning, Image texture correlation, StrainNet, Deep Learning, Tendons, Humans, Ultrasonography, Biomechanical Phenomena, Image Processing, Computer-Assisted
Abstract

Image-based deformation estimation is an important tool used in a variety of engineering problems, including crack propagation, fracture, and fatigue failure. These tools have been important in biomechanics research where measuring in vitro and in vivo tissue deformations are important for evaluating tissue health and disease progression. However, accurately measuring tissue deformation in vivo is particularly challenging due to limited image signal-to-noise ratio. Therefore, we created a novel deep-learning approach for measuring deformation from a sequence of images collected in vivo called StrainNet. Utilizing a training dataset that incorporates image artifacts, StrainNet was designed to maximize performance in challenging, in vivo settings. Artificially generated image sequences of human flexor tendons undergoing known deformations were used to compare benchmark StrainNet against two conventional image-based strain measurement techniques. StrainNet outperformed the traditional techniques by nearly 90%. High-frequency ultrasound imaging was then used to acquire images of the flexor tendons engaged during contraction. Only StrainNet was able to track tissue deformations under the in vivo test conditions. Findings revealed strong correlations between tendon deformation and applied forces, highlighting the potential for StrainNet to be a valuable tool for assessing rehabilitation strategies or disease progression. Additionally, by using real-world data to train our model, StrainNet was able to generalize and reveal important relationships between the effort exerted by the participant and tendon mechanics. Overall, StrainNet demonstrated the effectiveness of using deep learning for image-based strain analysis in vivo.

Published
2024

37. Genome-wide association study identifies high-impact susceptibility loci for HCC in North America.

Author

Hassan, Manal, Li, Donghui, Han, Younghun, Byun, Jinyoung, Hatia, Rikita, Long, Erping, Choi, Jiyeon, Kelley, Robin, Cleary, Sean, Lok, Anna, Bracci, Paige, Permuth, Jennifer, Bucur, Roxana, Yuan, Jian-Min, Singal, Amit, Jalal, Prasun, Ghobrial, R, Santella, Regina, Kono, Yuko, Shah, Dimpy, Nguyen, Mindie, Liu, Geoffrey, Parikh, Neehar, Kim, Richard, Wu, Hui-Chen, El-Serag, Hashem, Chang, Ping, Li, Yanan, Chun, Yun, Lee, Sunyoung, Gu, Jian, Hawk, Ernest, Sun, Ryan, Huff, Chad, Rashid, Asif, Amin, Hesham, Beretta, Laura, Wolff, Robert, Antwi, Samuel, Patt, Yehuda, Hwang, Lu-Yu, Klein, Alison, Zhang, Karen, Schmidt, Mikayla, White, Donna, Goss, John, Khaderi, Saira, Marrero, Jorge, Cigarroa, Francisco, Shah, Pankil, Kaseb, Ahmed, Roberts, Lewis, and Amos, Christopher

Subjects
Humans, Genome-Wide Association Study, Liver Neoplasms, Genetic Predisposition to Disease, Carcinoma, Hepatocellular, Male, Female, Middle Aged, North America, Case-Control Studies, Polymorphism, Single Nucleotide, Aged, Genetic Loci, White People
Abstract

BACKGROUND AND AIMS: Despite the substantial impact of environmental factors, individuals with a family history of liver cancer have an increased risk for HCC. However, genetic factors have not been studied systematically by genome-wide approaches in large numbers of individuals from European descent populations (EDP). APPROACH AND RESULTS: We conducted a 2-stage genome-wide association study (GWAS) on HCC not affected by HBV infections. A total of 1872 HCC cases and 2907 controls were included in the discovery stage, and 1200 HCC cases and 1832 controls in the validation. We analyzed the discovery and validation samples separately and then conducted a meta-analysis. All analyses were conducted in the presence and absence of HCV. The liability-scale heritability was 24.4% for overall HCC. Five regions with significant ORs (95% CI) were identified for nonviral HCC: 3p22.1, MOBP , rs9842969, (0.51, [0.40-0.65]); 5p15.33, TERT , rs2242652, (0.70, (0.62-0.79]); 19q13.11, TM6SF2 , rs58542926, (1.49, [1.29-1.72]); 19p13.11 MAU2 , rs58489806, (1.53, (1.33-1.75]); and 22q13.31, PNPLA3 , rs738409, (1.66, [1.51-1.83]). One region was identified for HCV-induced HCC: 6p21.31, human leukocyte antigen DQ beta 1, rs9275224, (0.79, [0.74-0.84]). A combination of homozygous variants of PNPLA3 and TERT showing a 6.5-fold higher risk for nonviral-related HCC compared to individuals lacking these genotypes. This observation suggests that gene-gene interactions may identify individuals at elevated risk for developing HCC. CONCLUSIONS: Our GWAS highlights novel genetic susceptibility of nonviral HCC among European descent populations from North America with substantial heritability. Selected genetic influences were observed for HCV-positive HCC. Our findings indicate the importance of genetic susceptibility to HCC development.

Published
2024

38. The EMPOWER Program: A History and Guide for Increasing Diversity Using Integrated Research and Education

Author

Adrienne Lester King, Cristi Bell-Huff, Collins Airhihenbuwa, Susan Ogletree, and Christa Wright

Abstract

The prevalence and pervasive nature of emerging chemicals of concern have created widespread environmental injustice apprehensions in vulnerable communities. To alleviate and address these concerns, identifying, engaging, and training a diverse environmental health research workforce will be critical and necessary steps to combat and prevent the consequences of environmental injustice. While there is an obvious need to enhance diversity in environmental health research, this process is hampered by facets of systemic racism that reduce access to educational resources needed to build interest and knowledge in students and teachers. We present here a historical perspective to offer a guide for building programs and relationships with underserved schools to help overcome limiting factors that have plagued certain public school systems. With the proper training and mentorship, the untapped workforce present within these schools will be empowered to understand and address current and emerging environmental health and safety threats. Through this transformative 8-week high school research program, we will develop well-prepared, ethical researchers committed to scientific inquiry, intensive fieldwork, and collaborative problem solving to address environmental health challenges. Following the four-step risk assessment process, students, teachers, and faculty mentors will work collaboratively to identify toxicants, potential hazards and risks, and environmental disparities in urban neighborhoods, which provides the necessary training to formulate critical thinking skills for use in academic or nonacademic careers.

Published
2024
Full Text
View/download PDF

40. Immunotherapy response induces divergent tertiary lymphoid structure morphologies in hepatocellular carcinoma

Author

Shu, Daniel H., Ho, Won Jin, Kagohara, Luciane T., Girgis, Alexander, Shin, Sarah M., Danilova, Ludmila, Lee, Jae W., Sidiropoulos, Dimitrios N., Mitchell, Sarah, Munjal, Kabeer, Howe, Kathryn, Bendinelli, Kayla J., Kartalia, Emma, Qi, Hanfei, Mo, Guanglan, Montagne, Janelle, Leatherman, James M., Lopez-Vidal, Tamara Y., Zhu, Qingfeng, Huff, Amanda L., Yuan, Xuan, Hernandez, Alexei, Coyne, Erin M., Zaidi, Neeha, Zabransky, Daniel J., Engle, Logan L., Ogurtsova, Aleksandra, Baretti, Marina, Laheru, Daniel, Durham, Jennifer N., Wang, Hao, Sunshine, Joel C., Johnston, Robert J., Deutsch, Julie Stein, Taube, Janis M., Anders, Robert A., Jaffee, Elizabeth M., Fertig, Elana J., and Yarchoan, Mark

Published
2024
Full Text
View/download PDF

42. Phase transitions in random circuit sampling

Author

Morvan, A., Villalonga, B., Mi, X., Mandrà, S., Bengtsson, A., Klimov, P. V., Chen, Z., Hong, S., Erickson, C., Drozdov, I. K., Chau, J., Laun, G., Movassagh, R., Asfaw, A., Brandão, L. T. A. N., Peralta, R., Abanin, D., Acharya, R., Allen, R., Andersen, T. I., Anderson, K., Ansmann, M., Arute, F., Arya, K., Atalaya, J., Bardin, J. C., Bilmes, A., Bortoli, G., Bourassa, A., Bovaird, J., Brill, L., Broughton, M., Buckley, B. B., Buell, D. A., Burger, T., Burkett, B., Bushnell, N., Campero, J., Chang, H.-S., Chiaro, B., Chik, D., Chou, C., Cogan, J., Collins, R., Conner, P., Courtney, W., Crook, A. L., Curtin, B., Debroy, D. M., Barba, A. Del Toro, Demura, S., Paolo, A. Di, Dunsworth, A., Faoro, L., Farhi, E., Fatemi, R., Ferreira, V. S., Burgos, L. Flores, Forati, E., Fowler, A. G., Foxen, B., Garcia, G., Genois, É., Giang, W., Gidney, C., Gilboa, D., Giustina, M., Gosula, R., Dau, A. Grajales, Gross, J. A., Habegger, S., Hamilton, M. C., Hansen, M., Harrigan, M. P., Harrington, S. D., Heu, P., Hoffmann, M. R., Huang, T., Huff, A., Huggins, W. J., Ioffe, L. B., Isakov, S. V., Iveland, J., Jeffrey, E., Jiang, Z., Jones, C., Juhas, P., Kafri, D., Khattar, T., Khezri, M., Kieferová, M., Kim, S., Kitaev, A., Klots, A. R., Korotkov, A. N., Kostritsa, F., Kreikebaum, J. M., Landhuis, D., Laptev, P., Lau, K.-M., Laws, L., Lee, J., Lee, K. W., Lensky, Y. D., Lester, B. J., Lill, A. T., Liu, W., Livingston, W. P., Locharla, A., Malone, F. D., Martin, O., Martin, S., McClean, J. R., McEwen, M., Miao, K. C., Mieszala, A., Montazeri, S., Mruczkiewicz, W., Naaman, O., Neeley, M., Neill, C., Nersisyan, A., Newman, M., Ng, J. H., Nguyen, A., Nguyen, M., Niu, M. Yuezhen, O’Brien, T. E., Omonije, S., Opremcak, A., Petukhov, A., Potter, R., Pryadko, L. P., Quintana, C., Rhodes, D. M., Rocque, C., Rosenberg, E., Rubin, N. C., Saei, N., Sank, D., Sankaragomathi, K., Satzinger, K. J., Schurkus, H. F., Schuster, C., Shearn, M. J., Shorter, A., Shutty, N., Shvarts, V., Sivak, V., Skruzny, J., Smith, W. C., Somma, R. D., Sterling, G., Strain, D., Szalay, M., Thor, D., Torres, A., Vidal, G., Heidweiller, C. Vollgraff, White, T., Woo, B. W. K., Xing, C., Yao, Z. J., Yeh, P., Yoo, J., Young, G., Zalcman, A., Zhang, Y., Zhu, N., Zobrist, N., Rieffel, E. G., Biswas, R., Babbush, R., Bacon, D., Hilton, J., Lucero, E., Neven, H., Megrant, A., Kelly, J., Roushan, P., Aleiner, I., Smelyanskiy, V., Kechedzhi, K., Chen, Y., and Boixo, S.

Published
2024
Full Text
View/download PDF

45. SPT Clusters with DES and HST Weak Lensing. II. Cosmological Constraints from the Abundance of Massive Halos

Author

Bocquet, S., Grandis, S., Bleem, L. E., Klein, M., Mohr, J. J., Schrabback, T., Abbott, T. M. C., Ade, P. A. R., Aguena, M., Alarcon, A., Allam, S., Allen, S. W., Alves, O., Amon, A., Anderson, A. J., Annis, J., Ansarinejad, B., Austermann, J. E., Avila, S., Bacon, D., Bayliss, M., Beall, J. A., Bechtol, K., Becker, M. R., Bender, A. N., Benson, B. A., Bernstein, G. M., Bhargava, S., Bianchini, F., Brodwin, M., Brooks, D., Bryant, L., Campos, A., Canning, R. E. A., Carlstrom, J. E., Rosell, A. Carnero, Kind, M. Carrasco, Carretero, J., Castander, F. J., Cawthon, R., Chang, C. L., Chang, C., Chaubal, P., Chen, R., Chiang, H. C., Choi, A., Chou, T-L., Citron, R., Moran, C. Corbett, Cordero, J., Costanzi, M., Crawford, T. M., Crites, A. T., da Costa, L. N., Pereira, M. E. S., Davis, C., Davis, T. M., DeRose, J., Desai, S., de Haan, T., Diehl, H. T., Dobbs, M. A., Dodelson, S., Doux, C., Drlica-Wagner, A., Eckert, K., Elvin-Poole, J., Everett, S., Everett, W., Ferrero, I., Ferté, A., Flores, A. M., Frieman, J., Gallicchio, J., García-Bellido, J., Gatti, M., George, E. M., Giannini, G., Gladders, M. D., Gruen, D., Gruendl, R. A., Gupta, N., Gutierrez, G., Halverson, N. W., Harrison, I., Hartley, W. G., Herner, K., Hinton, S. R., Holder, G. P., Hollowood, D. L., Holzapfel, W. L., Honscheid, K., Hrubes, J. D., Huang, N., Hubmayr, J., Huff, E. M., Huterer, D., Irwin, K. D., James, D. J., Jarvis, M., Khullar, G., Kim, K., Knox, L., Kraft, R., Krause, E., Kuehn, K., Kuropatkin, N., Kéruzoré, F., Lahav, O., Lee, A. T., Leget, P. -F., Li, D., Lin, H., Lowitz, A., MacCrann, N., Mahler, G., Mantz, A., Marshall, J. L., McCullough, J., McDonald, M., McMahon, J. J., Mena-Fernández, J., Menanteau, F., Meyer, S. S., Miquel, R., Montgomery, J., Myles, J., Natoli, T., Navarro-Alsina, A., Nibarger, J. P., Noble, G. I., Novosad, V., Ogando, R. L. C., Omori, Y., Padin, S., Pandey, S., Paschos, P., Patil, S., Pieres, A., Malagón, A. A. Plazas, Porredon, A., Prat, J., Pryke, C., Raveri, M., Reichardt, C. L., Roberson, J., Rollins, R. P., Romero, C., Roodman, A., Ruhl, J. E., Rykoff, E. S., Saliwanchik, B. R., Salvati, L., Sánchez, C., Sanchez, E., Cid, D. Sanchez, Saro, A., Schaffer, K. K., Secco, L. F., Sevilla-Noarbe, I., Sharon, K., Sheldon, E., Shin, T., Sievers, C., Smecher, G., Smith, M., Somboonpanyakul, T., Sommer, M., Stalder, B., Stark, A. A., Stephen, J., Strazzullo, V., Suchyta, E., Tarle, G., To, C., Troxel, M. A., Tucker, C., Tutusaus, I., Varga, T. N., Veach, T., Vieira, J. D., Vikhlinin, A., von der Linden, A., Wang, G., Weaverdyck, N., Weller, J., Whitehorn, N., Wu, W. L. K., Yanny, B., Yefremenko, V., Yin, B., Young, M., Zebrowski, J. A., Zhang, Y., Zohren, H., and Zuntz, J.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We present cosmological constraints from the abundance of galaxy clusters selected via the thermal Sunyaev-Zel'dovich (SZ) effect in South Pole Telescope (SPT) data with a simultaneous mass calibration using weak gravitational lensing data from the Dark Energy Survey (DES) and the Hubble Space Telescope (HST). The cluster sample is constructed from the combined SPT-SZ, SPTpol ECS, and SPTpol 500d surveys, and comprises 1,005 confirmed clusters in the redshift range $0.25-1.78$ over a total sky area of 5,200 deg$^2$. We use DES Year 3 weak-lensing data for 688 clusters with redshifts $z<0.95$ and HST weak-lensing data for 39 clusters with $0.6

Published
2024

47. Overview of the Alaskan Layered Pollution and Chemical Analysis (ALPACA) Field Experiment

Author

Simpson, William R, Mao, Jingqiu, Fochesatto, Gilberto J, Law, Kathy S, DeCarlo, Peter F, Schmale, Julia, Pratt, Kerri A, Arnold, Steve R, Stutz, Jochen, Dibb, Jack E, Creamean, Jessie M, Weber, Rodney J, Williams, Brent J, Alexander, Becky, Hu, Lu, Yokelson, Robert J, Shiraiwa, Manabu, Decesari, Stefano, Anastasio, Cort, D’Anna, Barbara, Gilliam, Robert C, Nenes, Athanasios, St. Clair, Jason M, Trost, Barbara, Flynn, James H, Savarino, Joel, Conner, Laura D, Kettle, Nathan, Heeringa, Krista M, Albertin, Sarah, Baccarini, Andrea, Barret, Brice, Battaglia, Michael A, Bekki, Slimane, Brado, TJ, Brett, Natalie, Brus, David, Campbell, James R, Cesler-Maloney, Meeta, Cooperdock, Sol, de Carvalho, Karolina Cysneiros, Delbarre, Hervé, DeMott, Paul J, Dennehy, Conor JS, Dieudonné, Elsa, Dingilian, Kayane K, Donateo, Antonio, Doulgeris, Konstantinos M, Edwards, Kasey C, Fahey, Kathleen, Fang, Ting, Guo, Fangzhou, Heinlein, Laura MD, Holen, Andrew L, Huff, Deanna, Ijaz, Amna, Johnson, Sarah, Kapur, Sukriti, Ketcherside, Damien T, Levin, Ezra, Lill, Emily, Moon, Allison R, Onishi, Tatsuo, Pappaccogli, Gianluca, Perkins, Russell, Pohorsky, Roman, Raut, Jean-Christophe, Ravetta, Francois, Roberts, Tjarda, Robinson, Ellis S, Scoto, Federico, Selimovic, Vanessa, Sunday, Michael O, Temime-Roussel, Brice, Tian, Xinxiu, Wu, Judy, and Yang, Yuhan

Subjects
Earth Sciences, Atmospheric Sciences, Environmental Sciences, Health Effects of Household Energy Combustion, Social Determinants of Health, Health Effects of Indoor Air Pollution, Climate-Related Exposures and Conditions, 2.2 Factors relating to the physical environment
Abstract

The Alaskan Layered Pollution And Chemical Analysis (ALPACA) field experiment was a collaborative study designed to improve understanding of pollution sources and chemical processes during winter (cold climate and low-photochemical activity), to investigate indoor pollution, and to study dispersion of pollution as affected by frequent temperature inversions. A number of the research goals were motivated by questions raised by residents of Fairbanks, Alaska, where the study was held. This paper describes the measurement strategies and the conditions encountered during the January and February 2022 field experiment, and reports early examples of how the measurements addressed research goals, particularly those of interest to the residents. Outdoor air measurements showed high concentrations of particulate matter and pollutant gases including volatile organic carbon species. During pollution events, low winds and extremely stable atmospheric conditions trapped pollution below 73 m, an extremely shallow vertical scale. Tethered-balloon-based measurements intercepted plumes aloft, which were associated with power plant point sources through transport modeling. Because cold climate residents spend much of their time indoors, the study included an indoor air quality component, where measurements were made inside and outside a house to study infiltration and indoor sources. In the absence of indoor activities such as cooking and/or heating with a pellet stove, indoor particulate matter concentrations were lower than outdoors; however, cooking and pellet stove burns often caused higher indoor particulate matter concentrations than outdoors. The mass-normalized particulate matter oxidative potential, a health-relevant property measured here by the reactivity with dithiothreiol, of indoor particles varied by source, with cooking particles having less oxidative potential per mass than pellet stove particles.

Published
2024

48. Effects of AFQ056 on language learning in fragile X syndrome

Author

Berry-Kravis, Elizabeth, Abbeduto, Leonard, Hagerman, Randi, Coffey, Christopher S, Cudkowicz, Merit, Erickson, Craig A, McDuffie, Andrea, Hessl, David, Ethridge, Lauren, Tassone, Flora, Kaufmann, Walter E, Friedmann, Katherine, Bullard, Lauren, Hoffmann, Anne, Veenstra-VanderWeele, Jeremy, Staley, Kevin, Klements, David, Moshinsky, Michael, Harkey, Brittney, Long, Jeff, Fedler, Janel, Klingner, Elizabeth, Ecklund, Dixie, Costigan, Michele, Huff, Trevis, Pearson, Brenda, and Investigators, NeuroNEXT FXLEARN

Subjects
6.1 Pharmaceuticals, Adult, Animals, Behavioral and Social Science, Biological sciences, Biomedical and clinical sciences, Biomedical and Clinical Sciences, Brain Disorders, Child, Cleft Palate, Clinical Research, Clinical trials, Clinical Trials and Supportive Activities, Evaluation of treatments and therapeutic interventions, Fragile X Syndrome, Health sciences, Humans, Immunology, Indoles, Intellectual and Developmental Disabilities, Language, Learning, Malignant Hyperthermia, Medical and Health Sciences, Mental health, Myotonia Congenita, Neurodevelopment, NeuroNEXT FXLEARN Investigators, Neuroscience, Pediatric, Rare Diseases, Single-Blind Method, Translation, Neurosciences, Intellectual and Developmental Disabilities (IDD), Orphan Drug, Mental Health
Abstract

BACKGROUND: FXLEARN, the first-ever large multisite trial of effects of disease-targeted pharmacotherapy on learning, was designed to explore a paradigm for measuring effects of mechanism-targeted treatment in fragile X syndrome (FXS). In FXLEARN, the effects of metabotropic glutamate receptor type 5 (mGluR5) negative allosteric modulator (NAM) AFQ056 on language learning were evaluated in 3- to 6-year-old children with FXS, expected to have more learning plasticity than adults, for whom prior trials of mGluR5 NAMs have failed.METHODSAfter a 4-month single-blind placebo lead-in, participants were randomized 1:1 to AFQ056 or placebo, with 2 months of dose optimization to the maximum tolerated dose, then 6 months of treatment during which a language-learning intervention was implemented for both groups. The primary outcome was a centrally scored videotaped communication measure, the Weighted Communication Scale (WCS). Secondary outcomes were objective performance-based and parent-reported cognitive and language measures.RESULTSFXLEARN enrolled 110 participants, randomized 99, and had 91 who completed the placebo-controlled period. Although both groups made language progress and there were no safety issues, the change in WCS score during the placebo-controlled period was not significantly different between the AFQ056 and placebo-treated groups, nor were there any significant between-group differences in change in any secondary measures.CONCLUSIONDespite the large body of evidence supporting use of mGluR5 NAMs in animal models of FXS, this study suggests that this mechanism of action does not translate into benefit for the human FXS population and that better strategies are needed to determine which mechanisms will translate from preclinical models to humans in genetic neurodevelopmental disorders.TRIAL REGISTRATIONClincalTrials.gov NCT02920892.FUNDING SOURCESNeuroNEXT network NIH grants U01NS096767, U24NS107200, U24NS107209, U01NS077323, U24NS107183, U24NS107168, U24NS107128, U24NS107199, U24NS107198, U24NS107166, U10NS077368, U01NS077366, U24NS107205, U01NS077179, and U01NS077352; NIH grant P50HD103526; and Novartis IIT grant AFQ056X2201T for provision of AFQ056.

Published
2024

50. Mapping in silico genetic networks of the KMT2D tumour suppressor gene to uncover novel functional associations and cancer cell vulnerabilities

Author

Takemon, Yuka, Pleasance, Erin D., Gagliardi, Alessia, Hughes, Christopher S., Csizmok, Veronika, Wee, Kathleen, Trinh, Diane L., Huff, Ryan D., Mungall, Andrew J., Moore, Richard A., Chuah, Eric, Mungall, Karen L., Lewis, Eleanor, Nelson, Jessica, Lim, Howard J., Renouf, Daniel J., Jones, Steven JM., Laskin, Janessa, and Marra, Marco A.

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results