The NANOGrav 15 year Data Set: Removing pulsars one by one from the pulsar timing array

Autor: Agazie, Gabriella, Anumarlapudi, Akash, Archibald, Anne M., Arzoumanian, Zaven, Baier, Jeremy G., Baker, Paul T., Becsy, Bence, Blecha, Laura, Brazier, Adam, Brook, Paul R., Burke-Spolaor, Sarah, Casey-Clyde, J. Andrew, Charisi, Maria, Chatterjee, Shami, Cohen, Tyler, Cordes, James M., Cornish, Neil J., Crawford, Fronefield, Cromartie, H. Thankful, Crowter, Kathryn, DeCesar, Megan E., Demorest, Paul B., Deng, Heling, Dey, Lankeswar, Dolch, Timothy, Ferrara, Elizabeth C., Fiore, William, Fonseca, Emmanuel, Freedman, Gabriel E., Gardiner, Emiko C., Garver-Daniels, Nate, Gentile, Peter A., Gersbach, Kyle A., Glaser, Joseph, Good, Deborah C., Guertin, Lydia, Gultekin, Kayhan, Hazboun, Jeffrey S., Jennings, Ross J., Johnson, Aaron D., Jones, Megan L., Kaiser, Andrew R., Kaplan, David L., Kelley, Luke Zoltan, Kerr, Matthew, Key, Joey S., Laal, Nima, Lam, Michael T., Lamb, William G., Larsen, Bjorn, Lazio, T. Joseph W., Lewandowska, Natalia, Liu, Tingting, Lorimer, Duncan R., Luo, Jing, Lynch, Ryan S., Ma, Chung-Pei, Madison, Dustin R., McEwen, Alexander, McKee, James W., McLaughlin, Maura A., McMann, Natasha, Meyers, Bradley W., Meyers, Patrick M., Middleton, Hannah, Mingarelli, Chiara M. F., Mitridate, Andrea, Moore, Christopher J., Ng, Cherry, Nice, David J., Ocker, Stella Koch, Olum, Ken D., Pennucci, Timothy T., Perera, Benetge B. P., Pol, Nihan S., Radovan, Henri A., Ransom, Scott M., Ray, Paul S., Romano, Joseph D., Runnoe, Jessie C., Saffer, Alexander, Sardesai, Shashwat C., Schmiedekamp, Ann, Schmiedekamp, Carl, Schmitz, Kai, Shapiro-Albert, Brent J., Siemens, Xavier, Simon, Joseph, Siwek, Magdalena S., Fiscella, Sophia V. Sosa, Stairs, Ingrid H., Stinebring, Daniel R., Stovall, Kevin, Susobhanan, Abhimanyu, Swiggum, Joseph K., Taylor, Stephen R., Turner, Jacob E., Unal, Caner, Vallisneri, Michele, Vecchio, Alberto, Vigeland, Sarah J., Wahl, Haley M., Witt, Caitlin A., Wright, David, Young, Olivia

Evidence has emerged for a stochastic signal correlated among 67 pulsars within the 15-year pulsar-timing data set compiled by the NANOGrav collaboration. Similar signals have been found in data from the European, Indian, Parkes, and Chinese PTAs. Th

Externí odkaz: http://arxiv.org/abs/2411.14846

Deep Neural Emulation of the Supermassive Black-hole Binary Population

Autor: Laal, Nima, Taylor, Stephen R., Kelley, Luke Zoltan, Simon, Joseph, Gultekin, Kayhan, Wright, David, Becsy, Bence, Casey-Clyde, J. Andrew, Chen, Siyuan, Cingoranelli, Alexander, D'Orazio, Daniel J., Gardiner, Emiko C., Lamb, William G., Matt, Cayenne, Siwek, Magdalena S., Wachter, Jeremy M.

While supermassive black-hole (SMBH)-binaries are not the only viable source for the low-frequency gravitational wave background (GWB) signal evidenced by the most recent pulsar timing array (PTA) data sets, they are expected to be the most likely. T

Externí odkaz: http://arxiv.org/abs/2411.10519

Double Distributionally Robust Bid Shading for First Price Auctions

Autor: Qu, Yanlin, Kant, Ravi, Chen, Yan, Kitts, Brendan, Gultekin, San, Flores, Aaron, Blanchet, Jose

Bid shading has become a standard practice in the digital advertising industry, in which most auctions for advertising (ad) opportunities are now of first price type. Given an ad opportunity, performing bid shading requires estimating not only the va

Externí odkaz: http://arxiv.org/abs/2410.14864

Searching for the Highest-z Dual AGN in the Deepest Chandra Surveys

Autor: Sandoval, Brandon, Foord, Adi, Allen, Steven W., Volonteri, Marta, Stemo, Aaron, Chen, Nianyi, Di Matteo, Tiziana, Gultekin, Kayhan, Habouzit, Melanie, Puerto-Sanchez, Clara, Hodges-Kluck, Edmund, Dubois, Yohan

We present an analysis searching for dual AGN among 62 high-redshift ($2.5 < z < 3.5$) X-ray sources selected from publicly available deep Chandra fields. We aim to quantify the frequency of dual AGN in the high-redshift Universe, which holds implica

Externí odkaz: http://arxiv.org/abs/2312.02311

An energy-based comparative analysis of common approaches to text classification in the Legal domain

Autor: Gultekin, Sinan, Globo, Achille, Zugarini, Andrea, Ernandes, Marco, Rigutini, Leonardo

Publikováno v: Computer Science & Information Technology (CS & IT) ISSN 2231-5403 Volume 14, Number 02, January 2024

Most Machine Learning research evaluates the best solutions in terms of performance. However, in the race for the best performing model, many important aspects are often overlooked when, on the contrary, they should be carefully considered. In fact,

Externí odkaz: http://arxiv.org/abs/2311.01256