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1. SN2023ixf in Messier 101: the twilight years of the progenitor as seen by Pan-STARRS

Author

Ransome, Conor L., Villar, V. Ashley, Tartaglia, Anna, Gonzalez, Sebastian Javier, Jacobson-Galán, Wynn V., Kilpatrick, Charles D., Margutti, Raffaella, Foley, Ryan J., Grayling, Matthew, Ni, Yuan Qi, Yarza, Ricardo, Ye, Christine, Auchettl, Katie, de Boer, Thomas, Chambers, Kenneth C., Coulter, David A., Drout, Maria R., Farias, Diego, Gall, Christa, Gao, Hua, Huber, Mark E., Ibik, Adaeze L., Jones, David O., Khetan, Nandita, Lin, Chien-Cheng, Politsch, Collin A., Raimundo, Sandra I., Rest, Armin, Wainscoat, Richard J., Yadavalli, S. Karthik, and Zenati, Yossef

Subjects
Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - High Energy Astrophysical Phenomena
Abstract

The nearby type II supernova, SN2023ixf in M101 exhibits signatures of early-time interaction with circumstellar material in the first week post-explosion. This material may be the consequence of prior mass loss suffered by the progenitor which possibly manifested in the form of a detectable pre-supernova outburst. We present an analysis of the long-baseline pre-explosion photometric data in $g$, $w$, $r$, $i$, $z$ and $y$ filters from Pan-STARRS as part of the Young Supernova Experiment, spanning $\sim$5,000 days. We find no significant detections in the Pan-STARRS pre-explosion light curve. We train a multilayer perceptron neural network to classify pre-supernova outbursts. We find no evidence of eruptive pre-supernova activity to a limiting absolute magnitude of $-7$. The limiting magnitudes from the full set of $gwrizy$ (average absolute magnitude $\approx$-8) data are consistent with previous pre-explosion studies. We use deep photometry from the literature to constrain the progenitor of SN2023ixf, finding that these data are consistent with a dusty red supergiant (RSG) progenitor with luminosity $\log\left(L/L_\odot\right)$$\approx$5.12 and temperature $\approx$3950K, corresponding to a mass of 14-20 M$_\odot$, Comment: 19 pages, 8 figures, 1 table

Published
2023

2. SN 2022oqm: A Bright and Multi-peaked Calcium-rich Transient

Author

Yadavalli, S. Karthik, Villar, V. Ashley, Izzo, Luca, Zenati, Yossef, Foley, Ryan J., Wheeler, J. Craig, Angus, Charlotte R., Bánhidi, Dominik, Auchettl, Katie, Bíró, Barna Imre, Bódi, Attila, Bodola, Zsófia, de Boer, Thomas, Chambers, Kenneth C., Chornock, Ryan, Coulter, David A., Csányi, István, Cseh, Borbála, Dandu, Srujan, Davis, Kyle W., Dickinson, Connor Braden, Farias, Diego, Farah, Joseph, Gall, Christa, Gao, Hua, Howell, D. Andrew, Jacobson-Galan, Wynn V., Khetan, Nandita, Kilpatrick, Charles D., Könyves-Tóth, Réka, Kriskovics, Levente, LeBaron, Natalie, Loertscher, Kayla, Saux, X. K. Le, Margutti, Rafaella, Magnier, Eugene A., McCully, Curtis, McGill, Peter, Miao, Hao-Yu, Newsome, Megan, Gonzalez, Estefania Padilla, Pál, András, Pál, Boróka H., Pan, Yen-Chen, Politsch, Collin A., Ransome, Conor L., Ramirez-Ruiz, Enrico, Rest, Armin, Rest, Sofia, Robinson, Olivia, Sears, Huei, Scheer, Jackson, Sódor, Ádám, Swift, Jonathan, Székely, Péter, Szakáts, Róbert, Szalai, Tamás, Taggart, Kirsty, Terreran, Giacomo, Venkatraman, Padma, Vinkó, József, Yang, Grace, and Zhou, Henry

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

We present the photometric and spectroscopic evolution of SN 2022oqm, a nearby multi-peaked hydrogen- and helium-weak calcium-rich transient (CaRT). SN 2022oqm was detected 13.1 kpc from its host galaxy, the face-on spiral galaxy NGC 5875. Extensive spectroscopic coverage reveals an early hot (T >= 40,000 K) continuum and carbon features observed $\sim$1~day after discovery, SN Ic-like photospheric-phase spectra, and strong forbidden calcium emission starting 38 days after discovery. SN 2022oqm has a relatively high peak luminosity (MB = -17 mag) for (CaRTs), making it an outlier in the population. We determine that three power sources are necessary to explain the light curve (LC), with each corresponding to a distinct peak. The first peak is powered by an expanding blackbody with a power law luminosity, suggesting shock cooling by circumstellar material (CSM). Subsequent LC evolution is powered by a double radioactive decay model, consistent with two sources of photons diffusing through optically thick ejecta. From the LC, we derive an ejecta mass and 56Ni mass of ~0.6 solar masses and ~0.09 solar masses. Spectroscopic modeling suggests 0.6 solar masses of ejecta, and with well-mixed Fe-peak elements throughout. We discuss several physical origins for SN 2022oqm and find either a surprisingly massive white dwarf progenitor or a peculiar stripped envelope model could explain SN 2022oqm. A stripped envelope explosion inside a dense, hydrogen- and helium-poor CSM, akin to SNe Icn, but with a large 56Ni mass and small CSM mass could explain SN 2022oqm. Alternatively, helium detonation on an unexpectedly massive white dwarf could also explain SN 2022oqm., Comment: 35 pages, 17 figures, 7 tables, Accepted for Publication in ApJ

Published
2023

3. Flight of the Bumblebee: the Early Excess Flux of Type Ia Supernova 2023bee revealed by $TESS$, $Swift$ and Young Supernova Experiment Observations

Author

Wang, Qinan, Rest, Armin, Dimitriadis, Georgios, Ridden-harper, Ryan, Siebert, Matthew R., Magee, Mark, Angus, Charlotte R., Auchettl, Katie, Davis, Kyle W., Foley, Ryan J., Fox, Ori D., Gomez, Sebastian, Jencson, Jacob E., Jones, David O., Kilpatrick, Charles D., Pierel, Justin D. R., Piro, Anthony L., Polin, Abigail, Politsch, Collin A., Rojas-bravo, César, Shahbandeh, Melissa, Villar, V. Ashley, Zenati, Yossef, Ashall, C., Chambers, Kenneth C., Coulter, David A., De Boer, Thomas, Dilullo, Nico, Gall, Christa, Gao, Hua, Hsiao, Eric Y., Huber, Mark E., Izzo, Luca, Khetan, Nandita, Lebaron, Natalie, Magnier, Eugene A., Mandel, Kaisey S., Mcgill, Peter, Miao, Hao-yu, Pan, Yen-chen, Stevens, Catherine P., Swift, Jonathan J., Taggart, Kirsty, and Yang, Grace

Subjects
Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics
Abstract

We present high-cadence ultraviolet through near-infrared observations of the Type Ia supernova (SN Ia) 2023bee in NGC~2708 ($D = 32 \pm 3$ Mpc), finding excess flux in the first days after explosion relative to the expected power-law rise from an expanding fireball. This deviation from typical behavior for SNe Ia is particularly obvious in our 10-minute cadence $TESS$ light curve and $Swift$ UV data. Compared to a few other normal SNe Ia with detected early excess flux, the excess flux in SN 2023bee is redder in the UV and less luminous. We present optical spectra of SN 2023bee, including two spectra during the period where the flux excess is dominant. At this time, the spectra are similar to those of other SNe Ia but with weaker Si II, C II and Ca II absorption lines, perhaps because the excess flux creates a stronger continuum. We compare the data to several theoretical models that have been proposed to explain the early flux excess in SNe Ia. Interaction with either a nearby companion star or close-in circumstellar material is expected to produce a faster evolution than seen in the data. Radioactive material in the outer layers of the ejecta, either from a double detonation explosion or simply an explosion with a $^{56}$Ni clump near the surface, can not fully reproduce the evolution either, likely due to the sensitivity of early UV observable to the treatment of the outer part of ejecta in simulation. We conclude that no current model can adequately explain the full set of observations. We find that a relatively large fraction of nearby, bright SNe Ia with high-cadence observations have some amount of excess flux within a few days of explosion. Considering potential asymmetric emission, the physical cause of this excess flux may be ubiquitous in normal SNe Ia., Comment: 21 pages, 12 figures. Accepted by the astrophysical journal

Published
2023

4. An open repository of real-time COVID-19 indicators

Author

Reinhart, Alex, Brooks, Logan, Jahja, Maria, Rumack, Aaron, Tang, Jingjing, Agrawal, Sumit, Saeed, Wael Al, Arnold, Taylor, Basu, Amartya, Bien, Jacob, Cabrera, Ángel A, Chin, Andrew, Chua, Eu Jing, Clark, Brian, Colquhoun, Sarah, DeFries, Nat, Farrow, David C, Forlizzi, Jodi, Grabman, Jed, Gratzl, Samuel, Green, Alden, Haff, George, Han, Robin, Harwood, Kate, Hu, Addison J, Hyde, Raphael, Hyun, Sangwon, Joshi, Ananya, Kim, Jimi, Kuznetsov, Andrew, La Motte-Kerr, Wichada, Lee, Yeon Jin, Lee, Kenneth, Lipton, Zachary C, Liu, Michael X, Mackey, Lester, Mazaitis, Kathryn, McDonald, Daniel J, McGuinness, Phillip, Narasimhan, Balasubramanian, O’Brien, Michael P, Oliveira, Natalia L, Patil, Pratik, Perer, Adam, Politsch, Collin A, Rajanala, Samyak, Rucker, Dawn, Scott, Chris, Shah, Nigam H, Shankar, Vishnu, Sharpnack, James, Shemetov, Dmitry, Simon, Noah, Smith, Benjamin Y, Srivastava, Vishakha, Tan, Shuyi, Tibshirani, Robert, Tuzhilina, Elena, Van Nortwick, Ana Karina, Ventura, Valérie, Wasserman, Larry, Weaver, Benjamin, Weiss, Jeremy C, Whitman, Spencer, Williams, Kristin, Rosenfeld, Roni, and Tibshirani, Ryan J

Subjects
Bioengineering, Ambulatory Care, COVID-19, Databases, Factual, Epidemiologic Methods, Health Status Indicators, Humans, Internet, Physical Distancing, Surveys and Questionnaires, Travel, United States, open data, digital surveillance, internet surveys, medical insurance claims
Abstract

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.

Published
2021

5. Trend Filtering -- II. Denoising Astronomical Signals with Varying Degrees of Smoothness

Author

Politsch, Collin A., Cisewski-Kehe, Jessi, Croft, Rupert A. C., and Wasserman, Larry

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Solar and Stellar Astrophysics, Statistics - Applications
Abstract

Trend filtering---first introduced into the astronomical literature in Paper I of this series---is a state-of-the-art statistical tool for denoising one-dimensional signals that possess varying degrees of smoothness. In this work, we demonstrate the broad utility of trend filtering to observational astronomy by discussing how it can contribute to a variety of spectroscopic and time-domain studies. The observations we discuss are (1) the Lyman-$\alpha$ forest of quasar spectra; (2) more general spectroscopy of quasars, galaxies, and stars; (3) stellar light curves with planetary transits; (4) eclipsing binary light curves; and (5) supernova light curves. We study the Lyman-$\alpha$ forest in the greatest detail---using trend filtering to map the large-scale structure of the intergalactic medium along quasar-observer lines of sight. The remaining studies share broad themes of: (1) estimating observable parameters of light curves and spectra; and (2) constructing observational spectral/light-curve templates. We also briefly discuss the utility of trend filtering as a tool for one-dimensional data reduction and compression., Comment: Part 2 of 2, Link to Part 1: arXiv:1908.07151; 15 pages, 7 figures

Published
2020
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6. Trend Filtering -- I. A Modern Statistical Tool for Time-Domain Astronomy and Astronomical Spectroscopy

Author

Politsch, Collin A., Cisewski-Kehe, Jessi, Croft, Rupert A. C., and Wasserman, Larry

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Solar and Stellar Astrophysics, Statistics - Applications
Abstract

The problem of denoising a one-dimensional signal possessing varying degrees of smoothness is ubiquitous in time-domain astronomy and astronomical spectroscopy. For example, in the time domain, an astronomical object may exhibit a smoothly varying intensity that is occasionally interrupted by abrupt dips or spikes. Likewise, in the spectroscopic setting, a noiseless spectrum typically contains intervals of relative smoothness mixed with localized higher frequency components such as emission peaks and absorption lines. In this work, we present trend filtering, a modern nonparametric statistical tool that yields significant improvements in this broad problem space of denoising $spatially$ $heterogeneous$ signals. When the underlying signal is spatially heterogeneous, trend filtering is superior to any statistical estimator that is a linear combination of the observed data---including kernel smoothers, LOESS, smoothing splines, Gaussian process regression, and many other popular methods. Furthermore, the trend filtering estimate can be computed with practical and scalable efficiency via a specialized convex optimization algorithm, e.g. handling sample sizes of $n\gtrsim10^7$ within a few minutes. In a companion paper, we explicitly demonstrate the broad utility of trend filtering to observational astronomy by carrying out a diverse set of spectroscopic and time-domain analyses., Comment: Part 1 of 2; 15 pages, 3 figures

Published
2019
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7. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Author

Cramer, Estee Y., Ray, Evan L., Lopez, Velma K., Bracher, Johannes, Brennen, Andrea, Rivadeneira, Alvaro J. Castro, Gerding, Aaron, Gneiting, Tilmann, House, Katie H., Huang, Yuxin, Jayawardena, Dasuni, Kanji, Abdul H., Khandelwal, Ayush, Le, Khoa, Mühlemann, Anja, Niemi, Jarad, Shah, Apurv, Stark, Ariane, Wang, Yijin, Wattanachit, Nutcha, Zorn, Martha W., Gu, Youyang, Jain, Sansiddh, Bannur, Nayana, Deva, Ayush, Kulkarni, Mihir, Merugu, Srujana, Raval, Alpan, Shingi, Siddhant, Tiwari, Avtansh, White, Jerome, Abernethy, Neil F., Woody, Spencer, Dahan, Maytal, Fox, Spencer, Gaither, Kelly, Lachmann, Michael, Meyers, Lauren Ancel, Scott, James G., Tec, Mauricio, Srivastava, Ajitesh, George, Glover E., Cegan, Jeffrey C., Dettwiller, Ian D., England, William P., Farthing, Matthew W., Hunter, Robert H., Lafferty, Brandon, Linkov, Igor, Mayo, Michael L., Parno, Matthew D., Rowland, Michael A., Trump, Benjamin D., Zhang-James, Yanli, Chen, Samuel, Faraone, Stephen V., Hess, Jonathan, Morley, Christopher P., Salekin, Asif, Wang, Dongliang, Corsetti, Sabrina M., Baer, Thomas M., Eisenberg, Marisa C., Falb, Karl, Huang, Yitao, Martin, Emily T., McCauley, Ella, Myers, Robert L., Schwarz, Tom, Sheldon, Daniel, Gibson, Graham Casey, Yu, Rose, Gao, Liyao, Ma, Yian, Wu, Dongxia, Yan, Xifeng, Jin, Xiaoyong, Wang, Yu-Xiang, Chen, YangQuan, Guo, Lihong, Zhao, Yanting, Gu, Quanquan, Chen, Jinghui, Wang, Lingxiao, Xu, Pan, Zhang, Weitong, Zou, Difan, Biegel, Hannah, Lega, Joceline, McConnell, Steve, Nagraj, V. P., Guertin, Stephanie L., Hulme-Lowe, Christopher, Turner, Stephen D., Shi, Yunfeng, Ban, Xuegang, Walraven, Robert, Hong, Qi-Jun, Kong, Stanley, van de Walle, Axel, Turtle, James A., Ben-Nun, Michal, Riley, Steven, Riley, Pete, Koyluoglu, Ugur, DesRoches, David, Forli, Pedro, Hamory, Bruce, Kyriakides, Christina, Leis, Helen, Milliken, John, Moloney, Michael, Morgan, James, Nirgudkar, Ninad, Ozcan, Gokce, Piwonka, Noah, Ravi, Matt, Schrader, Chris, Shakhnovich, Elizabeth, Siegel, Daniel, Spatz, Ryan, Stiefeling, Chris, Wilkinson, Barrie, Wong, Alexander, Cavany, Sean, España, Guido, Moore, Sean, Oidtman, Rachel, Perkins, Alex, Kraus, David, Kraus, Andrea, Gao, Zhifeng, Bian, Jiang, Cao, Wei, Ferres, Juan Lavista, Li, Chaozhuo, Liu, Tie-Yan, Xie, Xing, Zhang, Shun, Zheng, Shun, Vespignani, Alessandro, Chinazzi, Matteo, Davis, Jessica T., Mu, Kunpeng, Piontti, Ana Pastore y, Xiong, Xinyue, Zheng, Andrew, Baek, Jackie, Farias, Vivek, Georgescu, Andreea, Levi, Retsef, Sinha, Deeksha, Wilde, Joshua, Perakis, Georgia, Bennouna, Mohammed Amine, Nze-Ndong, David, Singhvi, Divya, Spantidakis, Ioannis, Thayaparan, Leann, Tsiourvas, Asterios, Sarker, Arnab, Jadbabaie, Ali, Shah, Devavrat, Della Penna, Nicolas, Celi, Leo A., Sundar, Saketh, Wolfinger, Russ, Osthus, Dave, Castro, Lauren, Fairchild, Geoffrey, Michaud, Isaac, Karlen, Dean, Kinsey, Matt, Mullany, Luke C., Rainwater-Lovett, Kaitlin, Shin, Lauren, Tallaksen, Katharine, Wilson, Shelby, Lee, Elizabeth C., Dent, Juan, Grantz, Kyra H., Hill, Alison L., Kaminsky, Joshua, Kaminsky, Kathryn, Keegan, Lindsay T., Lauer, Stephen A., Lemaitre, Joseph C., Lessler, Justin, Meredith, Hannah R., Perez-Saez, Javier, Shah, Sam, Smith, Claire P., Truelove, Shaun A., Wills, Josh, Marshall, Maximilian, Gardner, Lauren, Nixon, Kristen, Burant, John C., Wang, Lily, Gao, Lei, Gu, Zhiling, Kim, Myungjin, Li, Xinyi, Wang, Guannan, Wang, Yueying, Yu, Shan, Reiner, Robert C., Barber, Ryan, Gakidou, Emmanuela, Hay, Simon I., Lim, Steve, Murray, Chris, Pigott, David, Gurung, Heidi L., Baccam, Prasith, Stage, Steven A., Suchoski, Bradley T., Prakash, B. Aditya, Adhikari, Bijaya, Cui, Jiaming, Rodríguez, Alexander, Tabassum, Anika, Xie, Jiajia, Keskinocak, Pinar, Asplund, John, Baxter, Arden, Oruc, Buse Eylul, Serban, Nicoleta, Arik, Sercan O., Dusenberry, Mike, Epshteyn, Arkady, Kanal, Elli, Le, Long T., Li, Chun-Liang, Pfister, Tomas, Sava, Dario, Sinha, Rajarishi, Tsai, Thomas, Yoder, Nate, Yoon, Jinsung, Zhang, Leyou, Abbott, Sam, Bosse, Nikos I., Funk, Sebastian, Hellewell, Joel, Meakin, Sophie R., Sherratt, Katharine, Zhou, Mingyuan, Kalantari, Rahi, Yamana, Teresa K., Pei, Sen, Shaman, Jeffrey, Li, Michael L., Bertsimas, Dimitris, Lami, Omar Skali, Soni, Saksham, Bouardi, Hamza Tazi, Ayer, Turgay, Adee, Madeline, Chhatwal, Jagpreet, Dalgic, Ozden O., Ladd, Mary A., Linas, Benjamin P., Mueller, Peter, Xiao, Jade, Wang, Yuanjia, Wang, Qinxia, Xie, Shanghong, Zeng, Donglin, Green, Alden, Bien, Jacob, Brooks, Logan, Hu, Addison J., Jahja, Maria, McDonald, Daniel, Narasimhan, Balasubramanian, Politsch, Collin, Rajanala, Samyak, Rumack, Aaron, Simon, Noah, Tibshirani, Ryan J., Tibshirani, Rob, Ventura, Valerie, Wasserman, Larry, O’Dea, Eamon B., Drake, John M., Pagano, Robert, Tran, Quoc T., Ho, Lam Si Tung, Huynh, Huong, Walker, Jo W., Slayton, Rachel B., Johansson, Michael A., Biggerstaff, Matthew, and Reich, Nicholas G.

Published
2022

8. SN 2023ixf in Messier 101: The Twilight Years of the Progenitor as Seen by Pan-STARRS

Author

Ransome, Conor L., primary, Villar, V. Ashley, additional, Tartaglia, Anna, additional, Gonzalez, Sebastian Javier, additional, Jacobson-Galán, Wynn V., additional, Kilpatrick, Charles D., additional, Margutti, Raffaella, additional, Foley, Ryan J., additional, Grayling, Matthew, additional, Ni, Yuan Qi, additional, Yarza, Ricardo, additional, Ye, Christine, additional, Auchettl, Katie, additional, de Boer, Thomas, additional, Chambers, Kenneth C., additional, Coulter, David A., additional, Drout, Maria R., additional, Farias, Diego, additional, Gall, Christa, additional, Gao, Hua, additional, Huber, Mark E., additional, Ibik, Adaeze L., additional, Jones, David O., additional, Khetan, Nandita, additional, Lin, Chien-Cheng, additional, Politsch, Collin A., additional, Raimundo, Sandra I., additional, Rest, Armin, additional, Wainscoat, Richard J., additional, Yadavalli, S. Karthik, additional, and Zenati, Yossef, additional

Published
2024
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9. Flight of the Bumblebee: the Early Excess Flux of Type Ia Supernova 2023bee Revealed by TESS, Swift, and Young Supernova Experiment Observations

Author

Wang, Qinan, primary, Rest, Armin, additional, Dimitriadis, Georgios, additional, Ridden-Harper, Ryan, additional, Siebert, Matthew R., additional, Magee, Mark, additional, Angus, Charlotte R., additional, Auchettl, Katie, additional, Davis, Kyle W., additional, Foley, Ryan J., additional, Fox, Ori D., additional, Gomez, Sebastian, additional, Jencson, Jacob E., additional, Jones, David O., additional, Kilpatrick, Charles D., additional, Pierel, Justin D. R., additional, Piro, Anthony L., additional, Polin, Abigail, additional, Politsch, Collin A., additional, Rojas-Bravo, César, additional, Shahbandeh, Melissa, additional, Villar, V. Ashley, additional, Zenati, Yossef, additional, Ashall, C., additional, Chambers, Kenneth C., additional, Coulter, David A., additional, de Boer, Thomas, additional, DiLullo, Nico, additional, Gall, Christa, additional, Gao, Hua, additional, Hsiao, Eric Y., additional, Huber, Mark E., additional, Izzo, Luca, additional, Khetan, Nandita, additional, LeBaron, Natalie, additional, Magnier, Eugene A., additional, Mandel, Kaisey S., additional, McGill, Peter, additional, Miao, Hao-Yu, additional, Pan, Yen-Chen, additional, Stevens, Catherine P., additional, Swift, Jonathan J., additional, Taggart, Kirsty, additional, and Yang, Grace, additional

Published
2024
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10. Flight of the Bumblebee:the Early Excess Flux of Type Ia Supernova 2023bee Revealed by TESS, Swift, and Young Supernova Experiment Observations

Author

Wang, Qinan, Rest, Armin, Dimitriadis, Georgios, Ridden-harper, Ryan, Siebert, Matthew R., Magee, Mark, Angus, Charlotte R., Auchettl, Katie, Davis, Kyle W., Foley, Ryan J., Fox, Ori D., Gomez, Sebastian, Jencson, Jacob E., Jones, David O., Kilpatrick, Charles D., Pierel, Justin D. R., Piro, Anthony L., Polin, Abigail, Politsch, Collin A., Rojas-bravo, César, Shahbandeh, Melissa, Villar, V. Ashley, Zenati, Yossef, Ashall, C., Chambers, Kenneth C., Coulter, David A., De Boer, Thomas, Dilullo, Nico, Gall, Christa, Gao, Hua, Hsiao, Eric Y., Huber, Mark E., Izzo, Luca, Khetan, Nandita, Lebaron, Natalie, Magnier, Eugene A., Mandel, Kaisey S., Mcgill, Peter, Miao, Hao-yu, Pan, Yen-chen, Stevens, Catherine P., Swift, Jonathan J., Taggart, Kirsty, Yang, Grace, Wang, Qinan, Rest, Armin, Dimitriadis, Georgios, Ridden-harper, Ryan, Siebert, Matthew R., Magee, Mark, Angus, Charlotte R., Auchettl, Katie, Davis, Kyle W., Foley, Ryan J., Fox, Ori D., Gomez, Sebastian, Jencson, Jacob E., Jones, David O., Kilpatrick, Charles D., Pierel, Justin D. R., Piro, Anthony L., Polin, Abigail, Politsch, Collin A., Rojas-bravo, César, Shahbandeh, Melissa, Villar, V. Ashley, Zenati, Yossef, Ashall, C., Chambers, Kenneth C., Coulter, David A., De Boer, Thomas, Dilullo, Nico, Gall, Christa, Gao, Hua, Hsiao, Eric Y., Huber, Mark E., Izzo, Luca, Khetan, Nandita, Lebaron, Natalie, Magnier, Eugene A., Mandel, Kaisey S., Mcgill, Peter, Miao, Hao-yu, Pan, Yen-chen, Stevens, Catherine P., Swift, Jonathan J., Taggart, Kirsty, and Yang, Grace

Published
2024

11. SN 2023ixf in Messier 101:The Twilight Years of the Progenitor as Seen by Pan-STARRS

Author

Ransome, Conor L., Villar, V. Ashley, Tartaglia, Anna, Gonzalez, Sebastian Javier, Jacobson-galán, Wynn V., Kilpatrick, Charles D., Margutti, Raffaella, Foley, Ryan J., Grayling, Matthew, Ni, Yuan Qi, Yarza, Ricardo, Ye, Christine, Auchettl, Katie, De Boer, Thomas, Chambers, Kenneth C., Coulter, David A., Drout, Maria R., Farias, Diego, Gall, Christa, Gao, Hua, Huber, Mark E., Ibik, Adaeze L., Jones, David O., Khetan, Nandita, Lin, Chien-cheng, Politsch, Collin A., Raimundo, Sandra I., Rest, Armin, Wainscoat, Richard J., Yadavalli, S. Karthik, Zenati, Yossef, Ransome, Conor L., Villar, V. Ashley, Tartaglia, Anna, Gonzalez, Sebastian Javier, Jacobson-galán, Wynn V., Kilpatrick, Charles D., Margutti, Raffaella, Foley, Ryan J., Grayling, Matthew, Ni, Yuan Qi, Yarza, Ricardo, Ye, Christine, Auchettl, Katie, De Boer, Thomas, Chambers, Kenneth C., Coulter, David A., Drout, Maria R., Farias, Diego, Gall, Christa, Gao, Hua, Huber, Mark E., Ibik, Adaeze L., Jones, David O., Khetan, Nandita, Lin, Chien-cheng, Politsch, Collin A., Raimundo, Sandra I., Rest, Armin, Wainscoat, Richard J., Yadavalli, S. Karthik, and Zenati, Yossef

Published
2024

13. The United States COVID-19 Forecast Hub dataset

Author

US COVID-19 Forecast Hub Consortium, Cramer, Estee Y., Huang, Yuxin, Wang, Yijin, Ray, Evan L., Cornell, Matthew, Bracher, Johannes, Brennen, Andrea, Rivadeneira, Alvaro J. Castro, Gerding, Aaron, House, Katie, Jayawardena, Dasuni, Kanji, Abdul Hannan, Khandelwal, Ayush, Le, Khoa, Mody, Vidhi, Mody, Vrushti, Niemi, Jarad, Stark, Ariane, Shah, Apurv, Wattanchit, Nutcha, Zorn, Martha W., Reich, Nicholas G., Gneiting, Tilmann, Mühlemann, Anja, Gu, Youyang, Chen, Yixian, Chintanippu, Krishna, Jivane, Viresh, Khurana, Ankita, Kumar, Ajay, Lakhani, Anshul, Mehrotra, Prakhar, Pasumarty, Sujitha, Shrivastav, Monika, You, Jialu, Bannur, Nayana, Deva, Ayush, Jain, Sansiddh, Kulkarni, Mihir, Merugu, Srujana, Raval, Alpan, Shingi, Siddhant, Tiwari, Avtansh, White, Jerome, Adiga, Aniruddha, Hurt, Benjamin, Lewis, Bryan, Marathe, Madhav, Peddireddy, Akhil Sai, Porebski, Przemyslaw, Venkatramanan, Srinivasan, Wang, Lijing, Dahan, Maytal, Fox, Spencer, Gaither, Kelly, Lachmann, Michael, Meyers, Lauren Ancel, Scott, James G., Tec, Mauricio, Woody, Spencer, Srivastava, Ajitesh, Xu, Tianjian, Cegan, Jeffrey C., Dettwiller, Ian D., England, William P., Farthing, Matthew W., George, Glover E., Hunter, Robert H., Lafferty, Brandon, Linkov, Igor, Mayo, Michael L., Parno, Matthew D., Rowland, Michael A., Trump, Benjamin D., Chen, Samuel, Faraone, Stephen V., Hess, Jonathan, Morley, Christopher P., Salekin, Asif, Wang, Dongliang, Zhang-James, Yanli, Baer, Thomas M., Corsetti, Sabrina M., Eisenberg, Marisa C., Falb, Karl, Huang, Yitao, Martin, Emily T., McCauley, Ella, Myers, Robert L., Schwarz, Tom, Gibson, Graham Casey, Sheldon, Daniel, Gao, Liyao, Ma, Yian, Wu, Dongxia, Yu, Rose, Jin, Xiaoyong, Wang, Yu-Xiang, Yan, Xifeng, Chen, YangQuan, Guo, Lihong, Zhao, Yanting, Chen, Jinghui, Gu, Quanquan, Wang, Lingxiao, Xu, Pan, Zhang, Weitong, Zou, Difan, Chattopadhyay, Ishanu, Huang, Yi, Lu, Guoqing, Pfeiffer, Ruth, Sumner, Timothy, Wang, Dongdong, Wang, Liqiang, Zhang, Shunpu, Zou, Zihang, Biegel, Hannah, Lega, Joceline, Hussain, Fazle, Khan, Zeina, Van Bussel, Frank, McConnell, Steve, Guertin, Stephanie L., Hulme-Lowe, Christopher, Nagraj, V. P., Turner, Stephen D., Bejar, Benjamín, Choirat, Christine, Flahault, Antoine, Krymova, Ekaterina, Lee, Gavin, Manetti, Elisa, Namigai, Kristen, Obozinski, Guillaume, Sun, Tao, Thanou, Dorina, Ban, Xuegang, Shi, Yunfeng, Walraven, Robert, Hong, Qi-Jun, Van De Walle, Axel, Ben-Nun, Michal, Riley, Steven, Riley, Pete, Turtle, James, Cao, Duy, Galasso, Joseph, Cho, Jae H., Jo, Areum, DesRoches, David, Forli, Pedro, Hamory, Bruce, Koyluoglu, Ugur, Kyriakides, Christina, Leis, Helen, Milliken, John, Moloney, Michael, Morgan, James, Nirgudkar, Ninad, Ozcan, Gokce, Piwonka, Noah, Ravi, Matt, Schrader, Chris, Shakhnovich, Elizabeth, Siegel, Daniel, Spatz, Ryan, Stiefeling, Chris, Wilkinson, Barrie, Wong, Alexander, Cavany, Sean, España, Guido, Moore, Sean, Oidtman, Rachel, Perkins, Alex, Ivy, Julie S., Mayorga, Maria E., Mele, Jessica, Rosenstrom, Erik T., Swann, Julie L., Kraus, Andrea, Kraus, David, Bian, Jiang, Cao, Wei, Gao, Zhifeng, Ferres, Juan Lavista, Li, Chaozhuo, Liu, Tie-Yan, Xie, Xing, Zhang, Shun, Zheng, Shun, Chinazzi, Matteo, Vespignani, Alessandro, Xiong, Xinyue, Davis, Jessica T., Mu, Kunpeng, Piontti, Ana Pastore Y, Baek, Jackie, Farias, Vivek, Georgescu, Andreea, Levi, Retsef, Sinha, Deeksha, Wilde, Joshua, Zheng, Andrew, Lami, Omar Skali, Bennouna, Amine, Ndong, David Nze, Perakis, Georgia, Singhvi, Divya, Spantidakis, Ioannis, Thayaparan, Leann, Tsiourvas, Asterios, Weisberg, Shane, Jadbabaie, Ali, Sarker, Arnab, Shah, Devavrat, Celi, Leo A., Penna, Nicolas D., Sundar, Saketh, Berlin, Abraham, Gandhi, Parth D., McAndrew, Thomas, Piriya, Matthew, Chen, Ye, Hlavacek, William, Lin, Yen Ting, Mallela, Abhishek, Miller, Ely, Neumann, Jacob, Posner, Richard, Wolfinger, Russ, Castro, Lauren, Fairchild, Geoffrey, Michaud, Isaac, Osthus, Dave, Wolffram, Daniel, Karlen, Dean, Panaggio, Mark J., Kinsey, Matt, Mullany, Luke C., Rainwater-Lovett, Kaitlin, Shin, Lauren, Tallaksen, Katharine, Wilson, Shelby, Brenner, Michael, Coram, Marc, Edwards, Jessie K., Joshi, Keya, Klein, Ellen, Hulse, Juan Dent, Grantz, Kyra H., Hill, Alison L., Kaminsky, Kathryn, Kaminsky, Joshua, Keegan, Lindsay T., Lauer, Stephen A., Lee, Elizabeth C., Lemaitre, Joseph C., Lessler, Justin, Meredith, Hannah R., Perez-Saez, Javier, Shah, Sam, Smith, Claire P., Truelove, Shaun A., Wills, Josh, Gardner, Lauren, Marshall, Maximilian, Nixon, Kristen, Burant, John C., Budzinski, Jozef, Chiang, Wen-Hao, Mohler, George, Gao, Junyi, Glass, Lucas, Qian, Cheng, Romberg, Justin, Sharma, Rakshith, Spaeder, Jeffrey, Sun, Jimeng, Xiao, Cao, Gao, Lei, Gu, Zhiling, Kim, Myungjin, Li, Xinyi, Wang, Yueying, Wang, Guannan, Wang, Lily, Yu, Shan, Jain, Chaman, Bhatia, Sangeeta, Nouvellet, Pierre, Barber, Ryan, Gaikedu, Emmanuela, Hay, Simon, Lim, Steve, Murray, Chris, Pigott, David, Reiner, Robert C., Baccam, Prasith, Gurung, Heidi L., Stage, Steven A., Suchoski, Bradley T., Fong, Chung-Yan, Yeung, Dit-Yan, Adhikari, Bijaya, Cui, Jiaming, Prakash, B. Aditya, Rodríguez, Alexander, Tabassum, Anika, Xie, Jiajia, Asplund, John, Baxter, Arden, Keskinocak, Pinar, Oruc, Buse Eylul, Serban, Nicoleta, Arik, Sercan O., Dusenberry, Mike, Epshteyn, Arkady, Kanal, Elli, Le, Long T., Li, Chun-Liang, Pfister, Tomas, Sinha, Rajarishi, Tsai, Thomas, Yoder, Nate, Yoon, Jinsung, Zhang, Leyou, Wilson, Daniel, Belov, Artur A., Chow, Carson C., Gerkin, Richard C., Yogurtcu, Osman N., Ibrahim, Mark, Lacroix, Timothee, Le, Matthew, Liao, Jason, Nickel, Maximilian, Sagun, Levent, Abbott, Sam, Bosse, Nikos I., Funk, Sebastian, Hellewell, Joel, Meakin, Sophie R., Sherratt, Katharine, Kalantari, Rahi, Zhou, Mingyuan, Karimzadeh, Morteza, Lucas, Benjamin, Ngo, Thoai, Zoraghein, Hamidreza, Vahedi, Behzad, Wang, Zhongying, Pei, Sen, Shaman, Jeffrey, Yamana, Teresa K., Bertsimas, Dimitris, Li, Michael L., Soni, Saksham, Bouardi, Hamza Tazi, Adee, Madeline, Ayer, Turgay, Chhatwal, Jagpreet, Dalgic, Ozden O., Ladd, Mary A., Linas, Benjamin P., Mueller, Peter, Xiao, Jade, Bosch, Jurgen, Wilson, Austin, Zimmerman, Peter, Wang, Qinxia, Wang, Yuanjia, Xie, Shanghong, Zeng, Donglin, Bien, Jacob, Brooks, Logan, Green, Alden, Hu, Addison J., Jahja, Maria, McDonald, Daniel, Narasimhan, Balasubramanian, Politsch, Collin, Rajanala, Samyak, Rumack, Aaron, Simon, Noah, Tibshirani, Ryan J., Tibshirani, Rob, Ventura, Valerie, Wasserman, Larry, Drake, John M., O’Dea, Eamon B., Abu-Mostafa, Yaser, Bathwal, Rahil, Chang, Nicholas A., Chitta, Pavan, Erickson, Anne, Goel, Sumit, Gowda, Jethin, Jin, Qixuan, Jo, HyeongChan, Kim, Juhyun, Kulkarni, Pranav, Lushtak, Samuel M., Mann, Ethan, Popken, Max, Soohoo, Connor, Tirumala, Kushal, Tseng, Albert, Varadarajan, Vignesh, Vytheeswaran, Jagath, Wang, Christopher, Yeluri, Akshay, Yurk, Dominic, Zhang, Michael, Zlokapa, Alexander, Pagano, Robert, Jain, Chandini, Tomar, Vishal, Ho, Lam, Huynh, Huong, Tran, Quoc, Lopez, Velma K., Walker, Jo W., Slayton, Rachel B., Johansson, Michael A., and Biggerstaff, Matthew

Subjects
ddc:510, Mathematics
Abstract

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.

Published
2022

14. An Open Repository of Real-Time COVID-19 Indicators

Author

Reinhart, Alex, primary, Brooks, Logan, additional, Jahja, Maria, additional, Rumack, Aaron, additional, Tang, Jingjing, additional, Agrawal, Sumit, additional, Saeed, Wael Al, additional, Arnold, Taylor, additional, Basu, Amartya, additional, Bien, Jacob, additional, Cabrera, Ángel A., additional, Chin, Andrew, additional, Chua, Eu Jing, additional, Clark, Brian, additional, Colquhoun, Sarah, additional, DeFries, Nat, additional, Farrow, David C., additional, Forlizzi, Jodi, additional, Grabman, Jed, additional, Gratzl, Samuel, additional, Green, Alden, additional, Haff, George, additional, Han, Robin, additional, Harwood, Kate, additional, Hu, Addison J., additional, Hyde, Raphael, additional, Hyun, Sangwon, additional, Joshi, Ananya, additional, Kim, Jimi, additional, Kuznetsov, Andrew, additional, Motte-Kerr, Wichada La, additional, Lee, Yeon Jin, additional, Lee, Kenneth, additional, Lipton, Zachary C., additional, Liu, Michael X., additional, Mackey, Lester, additional, Mazaitis, Kathryn, additional, McDonald, Daniel J., additional, McGuinness, Phillip, additional, Narasimhan, Balasubramanian, additional, O’Brien, Michael P., additional, Oliveira, Natalia L., additional, Patil, Pratik, additional, Perer, Adam, additional, Politsch, Collin A., additional, Rajanala, Samyak, additional, Rucker, Dawn, additional, Scott, Chris, additional, Shah, Nigam H., additional, Shankar, Vishnu, additional, Sharpnack, James, additional, Shemetov, Dmitry, additional, Simon, Noah, additional, Smith, Benjamin Y., additional, Srivastava, Vishakha, additional, Tan, Shuyi, additional, Tibshirani, Robert, additional, Tuzhilina, Elena, additional, Van Nortwick, Ana Karina, additional, Ventura, Valérie, additional, Wasserman, Larry, additional, Weaver, Benjamin, additional, Weiss, Jeremy C., additional, Whitman, Spencer, additional, Williams, Kristin, additional, Rosenfeld, Roni, additional, and Tibshirani, Ryan J., additional

Published
2021
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15. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

Author

Cramer, Estee Y, primary, Ray, Evan L, additional, Lopez, Velma K, additional, Bracher, Johannes, additional, Brennen, Andrea, additional, Castro Rivadeneira, Alvaro J, additional, Gerding, Aaron, additional, Gneiting, Tilmann, additional, House, Katie H, additional, Huang, Yuxin, additional, Jayawardena, Dasuni, additional, Kanji, Abdul H, additional, Khandelwal, Ayush, additional, Le, Khoa, additional, Mühlemann, Anja, additional, Niemi, Jarad, additional, Shah, Apurv, additional, Stark, Ariane, additional, Wang, Yijin, additional, Wattanachit, Nutcha, additional, Zorn, Martha W, additional, Gu, Youyang, additional, Jain, Sansiddh, additional, Bannur, Nayana, additional, Deva, Ayush, additional, Kulkarni, Mihir, additional, Merugu, Srujana, additional, Raval, Alpan, additional, Shingi, Siddhant, additional, Tiwari, Avtansh, additional, White, Jerome, additional, Abernethy, Neil F, additional, Woody, Spencer, additional, Dahan, Maytal, additional, Fox, Spencer, additional, Gaither, Kelly, additional, Lachmann, Michael, additional, Meyers, Lauren Ancel, additional, Scott, James G, additional, Tec, Mauricio, additional, Srivastava, Ajitesh, additional, George, Glover E, additional, Cegan, Jeffrey C, additional, Dettwiller, Ian D, additional, England, William P, additional, Farthing, Matthew W, additional, Hunter, Robert H, additional, Lafferty, Brandon, additional, Linkov, Igor, additional, Mayo, Michael L, additional, Parno, Matthew D, additional, Rowland, Michael A, additional, Trump, Benjamin D, additional, Zhang-James, Yanli, additional, Chen, Samuel, additional, Faraone, Stephen V, additional, Hess, Jonathan, additional, Morley, Christopher P, additional, Salekin, Asif, additional, Wang, Dongliang, additional, Corsetti, Sabrina M, additional, Baer, Thomas M, additional, Eisenberg, Marisa C, additional, Falb, Karl, additional, Huang, Yitao, additional, Martin, Emily T, additional, McCauley, Ella, additional, Myers, Robert L, additional, Schwarz, Tom, additional, Sheldon, Daniel, additional, Gibson, Graham Casey, additional, Yu, Rose, additional, Gao, Liyao, additional, Ma, Yian, additional, Wu, Dongxia, additional, Yan, Xifeng, additional, Jin, Xiaoyong, additional, Wang, Yu-Xiang, additional, Chen, YangQuan, additional, Guo, Lihong, additional, Zhao, Yanting, additional, Gu, Quanquan, additional, Chen, Jinghui, additional, Wang, Lingxiao, additional, Xu, Pan, additional, Zhang, Weitong, additional, Zou, Difan, additional, Biegel, Hannah, additional, Lega, Joceline, additional, McConnell, Steve, additional, Nagraj, VP, additional, Guertin, Stephanie L, additional, Hulme-Lowe, Christopher, additional, Turner, Stephen D, additional, Shi, Yunfeng, additional, Ban, Xuegang, additional, Walraven, Robert, additional, Hong, Qi-Jun, additional, Kong, Stanley, additional, van de Walle, Axel, additional, Turtle, James A, additional, Ben-Nun, Michal, additional, Riley, Steven, additional, Riley, Pete, additional, Koyluoglu, Ugur, additional, DesRoches, David, additional, Forli, Pedro, additional, Hamory, Bruce, additional, Kyriakides, Christina, additional, Leis, Helen, additional, Milliken, John, additional, Moloney, Michael, additional, Morgan, James, additional, Nirgudkar, Ninad, additional, Ozcan, Gokce, additional, Piwonka, Noah, additional, Ravi, Matt, additional, Schrader, Chris, additional, Shakhnovich, Elizabeth, additional, Siegel, Daniel, additional, Spatz, Ryan, additional, Stiefeling, Chris, additional, Wilkinson, Barrie, additional, Wong, Alexander, additional, Cavany, Sean, additional, España, Guido, additional, Moore, Sean, additional, Oidtman, Rachel, additional, Perkins, Alex, additional, Kraus, David, additional, Kraus, Andrea, additional, Gao, Zhifeng, additional, Bian, Jiang, additional, Cao, Wei, additional, Ferres, Juan Lavista, additional, Li, Chaozhuo, additional, Liu, Tie-Yan, additional, Xie, Xing, additional, Zhang, Shun, additional, Zheng, Shun, additional, Vespignani, Alessandro, additional, Chinazzi, Matteo, additional, Davis, Jessica T, additional, Mu, Kunpeng, additional, y Piontti, Ana Pastore, additional, Xiong, Xinyue, additional, Zheng, Andrew, additional, Baek, Jackie, additional, Farias, Vivek, additional, Georgescu, Andreea, additional, Levi, Retsef, additional, Sinha, Deeksha, additional, Wilde, Joshua, additional, Perakis, Georgia, additional, Bennouna, Mohammed Amine, additional, Nze-Ndong, David, additional, Singhvi, Divya, additional, Spantidakis, Ioannis, additional, Thayaparan, Leann, additional, Tsiourvas, Asterios, additional, Sarker, Arnab, additional, Jadbabaie, Ali, additional, Shah, Devavrat, additional, Penna, Nicolas Della, additional, Celi, Leo A, additional, Sundar, Saketh, additional, Wolfinger, Russ, additional, Osthus, Dave, additional, Castro, Lauren, additional, Fairchild, Geoffrey, additional, Michaud, Isaac, additional, Karlen, Dean, additional, Kinsey, Matt, additional, Mullany, Luke C., additional, Rainwater-Lovett, Kaitlin, additional, Shin, Lauren, additional, Tallaksen, Katharine, additional, Wilson, Shelby, additional, Lee, Elizabeth C, additional, Dent, Juan, additional, Grantz, Kyra H, additional, Hill, Alison L, additional, Kaminsky, Joshua, additional, Kaminsky, Kathryn, additional, Keegan, Lindsay T, additional, Lauer, Stephen A, additional, Lemaitre, Joseph C, additional, Lessler, Justin, additional, Meredith, Hannah R, additional, Perez-Saez, Javier, additional, Shah, Sam, additional, Smith, Claire P, additional, Truelove, Shaun A, additional, Wills, Josh, additional, Marshall, Maximilian, additional, Gardner, Lauren, additional, Nixon, Kristen, additional, Burant, John C., additional, Wang, Lily, additional, Gao, Lei, additional, Gu, Zhiling, additional, Kim, Myungjin, additional, Li, Xinyi, additional, Wang, Guannan, additional, Wang, Yueying, additional, Yu, Shan, additional, Reiner, Robert C, additional, Barber, Ryan, additional, Gakidou, Emmanuela, additional, Hay, Simon I., additional, Lim, Steve, additional, Murray, Chris J.L., additional, Pigott, David, additional, Gurung, Heidi L, additional, Baccam, Prasith, additional, Stage, Steven A, additional, Suchoski, Bradley T, additional, Prakash, B. Aditya, additional, Adhikari, Bijaya, additional, Cui, Jiaming, additional, Rodríguez, Alexander, additional, Tabassum, Anika, additional, Xie, Jiajia, additional, Keskinocak, Pinar, additional, Asplund, John, additional, Baxter, Arden, additional, Oruc, Buse Eylul, additional, Serban, Nicoleta, additional, Arik, Sercan O, additional, Dusenberry, Mike, additional, Epshteyn, Arkady, additional, Kanal, Elli, additional, Le, Long T, additional, Li, Chun-Liang, additional, Pfister, Tomas, additional, Sava, Dario, additional, Sinha, Rajarishi, additional, Tsai, Thomas, additional, Yoder, Nate, additional, Yoon, Jinsung, additional, Zhang, Leyou, additional, Abbott, Sam, additional, Bosse, Nikos I, additional, Funk, Sebastian, additional, Hellewell, Joel, additional, Meakin, Sophie R, additional, Sherratt, Katharine, additional, Zhou, Mingyuan, additional, Kalantari, Rahi, additional, Yamana, Teresa K, additional, Pei, Sen, additional, Shaman, Jeffrey, additional, Li, Michael L, additional, Bertsimas, Dimitris, additional, Lami, Omar Skali, additional, Soni, Saksham, additional, Bouardi, Hamza Tazi, additional, Ayer, Turgay, additional, Adee, Madeline, additional, Chhatwal, Jagpreet, additional, Dalgic, Ozden O, additional, Ladd, Mary A, additional, Linas, Benjamin P, additional, Mueller, Peter, additional, Xiao, Jade, additional, Wang, Yuanjia, additional, Wang, Qinxia, additional, Xie, Shanghong, additional, Zeng, Donglin, additional, Green, Alden, additional, Bien, Jacob, additional, Brooks, Logan, additional, Hu, Addison J, additional, Jahja, Maria, additional, McDonald, Daniel, additional, Narasimhan, Balasubramanian, additional, Politsch, Collin, additional, Rajanala, Samyak, additional, Rumack, Aaron, additional, Simon, Noah, additional, Tibshirani, Ryan J, additional, Tibshirani, Rob, additional, Ventura, Valerie, additional, Wasserman, Larry, additional, O’Dea, Eamon B, additional, Drake, John M, additional, Pagano, Robert, additional, Tran, Quoc T, additional, Tung Ho, Lam Si, additional, Huynh, Huong, additional, Walker, Jo W, additional, Slayton, Rachel B, additional, Johansson, Michael A, additional, Biggerstaff, Matthew, additional, and Reich, Nicholas G, additional

Published
2021
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16. Statistical Astrophysics: From Extrasolar Planets to the Large-scale Structure of the Universe

Author

Politsch, Collin

Subjects
Astrophysics::Cosmology and Extragalactic Astrophysics, Astrophysics, Astrophysics::Galaxy Astrophysics
Abstract

Modern astronomical surveys compile massive catalogs of images, light curves, and spectra that allow us to study the Universe from the relatively local (e.g. stars and extrasolar planets) to the extremely remote (e.g. quasars and the cosmic microwave background). As the magnitude of these catalogs continues to grow exponentially, the presence of statisticians and computer scientists working at the interface of astronomy and astrophysics becomes increasingly essential to the advancement of the field. In this dissertation, we study a variety of astrophysical problems of a statistical nature.In Chapters 2 and 3, we introduce trend filtering (Tibshirani, 2014) into the astronomical literature and demonstrate its broad utility by discussing how it can contribute to a variety of spectroscopic and time-domain studies. The astronomical observations we discuss are (1) the Lyman-α forest absorptions in the spectra of high redshift quasars; (2) the broader spectroscopic signatures of quasars, galaxies, and stars; (3) stellar light curves with planetary transits; (4) light curves of eclipsing binary star systems; and (5) supernova light curves. We study the Lyman-α forest in the greatest detail — using trend filtering to map the large-scale structure of the intergalactic medium along one-dimensional quasar-observer sightlines. The remaining studies share broad themes of: (1) estimating observable parameters of light curves and spectra; and (2) constructing observational spectral/light-curve templates.In Chapters 4 and 5, we continue our Lyman-α absorption spectroscopy analysis of the intergalactic medium by utilizing the full redshift z ≳ 2.1 quasar catalog compiled by the Baryon Oscillation Spectroscopic Survey (Dawson et al., 2013) to reconstruct a 47 h-3 Gpc3 three-dimensional large-scale structure map of the high redshift intergalactic medium — the largest volume map of the Universe to date — from the dense collection of one-dimensional quasar sightlines. We accompany the map with rigorous statistical error quantification and compile an extensive census of candidates for never-before-seen galaxy protoclusters and cosmic voids. The statistical reconstruction requires minimal assumptions on the underlying matter density field and is specifically optimized to recover three-dimensional structures lying between the one-dimensional sightlines backlit by quasars.This Thesis has been approved by the Department of Statistics & Data Science and the Machine Learning Department for the joint Doctor of Philosophy degree in Statistics and Machine Learning

Published
2020
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20. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Author

Estee Y. Cramer, Evan L. Ray, Velma K. Lopez, Johannes Bracher, Andrea Brennen, Alvaro J. Castro Rivadeneira, Aaron Gerding, Tilmann Gneiting, Katie H. House, Yuxin Huang, Dasuni Jayawardena, Abdul H. Kanji, Ayush Khandelwal, Khoa Le, Anja Mühlemann, Jarad Niemi, Apurv Shah, Ariane Stark, Yijin Wang, Nutcha Wattanachit, Martha W. Zorn, Youyang Gu, Sansiddh Jain, Nayana Bannur, Ayush Deva, Mihir Kulkarni, Srujana Merugu, Alpan Raval, Siddhant Shingi, Avtansh Tiwari, Jerome White, Neil F. Abernethy, Spencer Woody, Maytal Dahan, Spencer Fox, Kelly Gaither, Michael Lachmann, Lauren Ancel Meyers, James G. Scott, Mauricio Tec, Ajitesh Srivastava, Glover E. George, Jeffrey C. Cegan, Ian D. Dettwiller, William P. England, Matthew W. Farthing, Robert H. Hunter, Brandon Lafferty, Igor Linkov, Michael L. Mayo, Matthew D. Parno, Michael A. Rowland, Benjamin D. Trump, Yanli Zhang-James, Samuel Chen, Stephen V. Faraone, Jonathan Hess, Christopher P. Morley, Asif Salekin, Dongliang Wang, Sabrina M. Corsetti, Thomas M. Baer, Marisa C. Eisenberg, Karl Falb, Yitao Huang, Emily T. Martin, Ella McCauley, Robert L. Myers, Tom Schwarz, Daniel Sheldon, Graham Casey Gibson, Rose Yu, Liyao Gao, Yian Ma, Dongxia Wu, Xifeng Yan, Xiaoyong Jin, Yu-Xiang Wang, YangQuan Chen, Lihong Guo, Yanting Zhao, Quanquan Gu, Jinghui Chen, Lingxiao Wang, Pan Xu, Weitong Zhang, Difan Zou, Hannah Biegel, Joceline Lega, Steve McConnell, V. P. Nagraj, Stephanie L. Guertin, Christopher Hulme-Lowe, Stephen D. Turner, Yunfeng Shi, Xuegang Ban, Robert Walraven, Qi-Jun Hong, Stanley Kong, Axel van de Walle, James A. Turtle, Michal Ben-Nun, Steven Riley, Pete Riley, Ugur Koyluoglu, David DesRoches, Pedro Forli, Bruce Hamory, Christina Kyriakides, Helen Leis, John Milliken, Michael Moloney, James Morgan, Ninad Nirgudkar, Gokce Ozcan, Noah Piwonka, Matt Ravi, Chris Schrader, Elizabeth Shakhnovich, Daniel Siegel, Ryan Spatz, Chris Stiefeling, Barrie Wilkinson, Alexander Wong, Sean Cavany, Guido España, Sean Moore, Rachel Oidtman, Alex Perkins, David Kraus, Andrea Kraus, Zhifeng Gao, Jiang Bian, Wei Cao, Juan Lavista Ferres, Chaozhuo Li, Tie-Yan Liu, Xing Xie, Shun Zhang, Shun Zheng, Alessandro Vespignani, Matteo Chinazzi, Jessica T. Davis, Kunpeng Mu, Ana Pastore y Piontti, Xinyue Xiong, Andrew Zheng, Jackie Baek, Vivek Farias, Andreea Georgescu, Retsef Levi, Deeksha Sinha, Joshua Wilde, Georgia Perakis, Mohammed Amine Bennouna, David Nze-Ndong, Divya Singhvi, Ioannis Spantidakis, Leann Thayaparan, Asterios Tsiourvas, Arnab Sarker, Ali Jadbabaie, Devavrat Shah, Nicolas Della Penna, Leo A. Celi, Saketh Sundar, Russ Wolfinger, Dave Osthus, Lauren Castro, Geoffrey Fairchild, Isaac Michaud, Dean Karlen, Matt Kinsey, Luke C. Mullany, Kaitlin Rainwater-Lovett, Lauren Shin, Katharine Tallaksen, Shelby Wilson, Elizabeth C. Lee, Juan Dent, Kyra H. Grantz, Alison L. Hill, Joshua Kaminsky, Kathryn Kaminsky, Lindsay T. Keegan, Stephen A. Lauer, Joseph C. Lemaitre, Justin Lessler, Hannah R. Meredith, Javier Perez-Saez, Sam Shah, Claire P. Smith, Shaun A. Truelove, Josh Wills, Maximilian Marshall, Lauren Gardner, Kristen Nixon, John C. Burant, Lily Wang, Lei Gao, Zhiling Gu, Myungjin Kim, Xinyi Li, Guannan Wang, Yueying Wang, Shan Yu, Robert C. Reiner, Ryan Barber, Emmanuela Gakidou, Simon I. Hay, Steve Lim, Chris Murray, David Pigott, Heidi L. Gurung, Prasith Baccam, Steven A. Stage, Bradley T. Suchoski, B. Aditya Prakash, Bijaya Adhikari, Jiaming Cui, Alexander Rodríguez, Anika Tabassum, Jiajia Xie, Pinar Keskinocak, John Asplund, Arden Baxter, Buse Eylul Oruc, Nicoleta Serban, Sercan O. Arik, Mike Dusenberry, Arkady Epshteyn, Elli Kanal, Long T. Le, Chun-Liang Li, Tomas Pfister, Dario Sava, Rajarishi Sinha, Thomas Tsai, Nate Yoder, Jinsung Yoon, Leyou Zhang, Sam Abbott, Nikos I. Bosse, Sebastian Funk, Joel Hellewell, Sophie R. Meakin, Katharine Sherratt, Mingyuan Zhou, Rahi Kalantari, Teresa K. Yamana, Sen Pei, Jeffrey Shaman, Michael L. Li, Dimitris Bertsimas, Omar Skali Lami, Saksham Soni, Hamza Tazi Bouardi, Turgay Ayer, Madeline Adee, Jagpreet Chhatwal, Ozden O. Dalgic, Mary A. Ladd, Benjamin P. Linas, Peter Mueller, Jade Xiao, Yuanjia Wang, Qinxia Wang, Shanghong Xie, Donglin Zeng, Alden Green, Jacob Bien, Logan Brooks, Addison J. Hu, Maria Jahja, Daniel McDonald, Balasubramanian Narasimhan, Collin Politsch, Samyak Rajanala, Aaron Rumack, Noah Simon, Ryan J. Tibshirani, Rob Tibshirani, Valerie Ventura, Larry Wasserman, Eamon B. O’Dea, John M. Drake, Robert Pagano, Quoc T. Tran, Lam Si Tung Ho, Huong Huynh, Jo W. Walker, Rachel B. Slayton, Michael A. Johansson, Matthew Biggerstaff, Nicholas G. 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Subjects
model evaluation, Multidisciplinary, COVID-19, prediction, United States, Data Accuracy, 510 Mathematics, 360 Social problems & social services, weather, Humans, Public Health, ddc:510, ensemble forecast, Pandemics, Mathematics, Forecasting, Probability
Abstract

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub ( https://covid19forecasthub.org/ ) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

Published
2022
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