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arXiv 2024年

作者： Agazie, Gabriella Anumarlapudi, Akash Archibald, Anne M. Arzoumanian, Zaven Baier, Jeremy G. Baker, Paul T. Bécsy, 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 Esmyol, David 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. Gültekin, Kayhan Hazboun, Jeffrey S. Jennings, Ross J. Johnson, Aaron D. Jones, Megan L. Kaplan, David L. Kelley, Luke Zoltan Kerr, Matthew Key, Joey S. Laal, Nima Lam, Michael T. Lamb, William G. Larsen, Bjorn Joseph, T. Lazio, W. Lewandowska, Natalia Lino dos Santos, Rafael R. 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. Mingarelli, Chiara M.F. Mitridate, Andrea 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 Schröder, Tobias Shapiro-Albert, Brent J. Siemens, Xavier Simon, Joseph Siwek, Magdalena S. Sosa Fiscella, Sophia V. Stairs, Ingrid H. Stinebring, Daniel R. Stovall, Kevin Susobhanan, Abhimanyu Swiggum, Joseph K. Taylor, Stephen R. Turner, Jacob E. Unal, Caner Vallisneri, Michele van Haasteren, Rutger Vigeland, Sarah J. von Eckardstein, Richard Wahl, Haley M. Witt, Caitlin A. Wright, David Young, Olivia Center for Gravitation Cosmology and Astrophysics Department of Physics University of Wisconsin-Milwaukee P.O. Box 413 MilwaukeeWI53201 United States Newcastle University NE1 7RU United Kingdom X-Ray Astrophysics Laboratory NASA Goddard Space Flight Center Code 662 GreenbeltMD20771 United States Department of Physics Oregon State University CorvallisOR97331 United States Department of Physics and Astronomy Widener University One University Place ChesterPA19013 United States Physics Department University of Florida GainesvilleFL32611 United States Cornell Center for Astrophysics and Planetary Science Department of Astronomy Cornell University IthacaNY14853 United States Cornell Center for Advanced Computing Cornell University IthacaNY14853 United States Institute for Gravitational Wave Astronomy School of Physics and Astronomy University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom Department of Physics and Astronomy West Virginia University P.O. Box 6315 MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States Department of Physics University of Connecticut 196 Auditorium Road U-3046 StorrsCT06269-3046 United States Department of Physics and Astronomy Vanderbilt University 2301 Vanderbilt Place NashvilleTN37235 United States Department of Physics New Mexico Institute of Mining and Technology 801 Leroy Place SocorroNM87801 United States Department of Physics Montana State University BozemanMT59717 United States Department of Physics and Astronomy Franklin & Marshall College P.O. Box 3003 LancasterPA17604 United States National Research Council Research Associate National Academy of Sciences WashingtonDC20001 United States U.S. Naval Research Laboratory WashingtonDC20375 United States Department of Physics and Astronomy University of British Columbia 6224 Agricultural Road VancouverBCV6T 1Z1

The NANOGrav 15-year data provides compelling evidence for a stochastic gravitational-wave (GW) background at nanohertz frequencies. The simplest model-independent approach to characterizing the frequency spectrum of this signal consists in a simple power-law fit involving two parameters: an amplitude A and a spectral index γ. In this paper, we consider the next logical step beyond this minimal spectral model, allowing for a running (i.e., logarithmic frequency dependence) of the spectral index, γrun(f) = γ+β ln (f/fref ). We fit this running-power-law (RPL) model to the NANOGrav 15-year data and perform a Bayesian model comparison with the minimal constant-power-law (CPL) model, which results in a 95 % credible interval for the parameter β consistent with no running, β ∈ [−0.80, 2.96], and an inconclusive Bayes factor, B (RPL vs. CPL) = 0.69 ± 0.01. We thus conclude that, at present, the minimal CPL model still suffices to adequately describe the NANOGrav signal;however, future data sets may well lead to a measurement of nonzero β. Finally, we interpret the RPL model as a description of primordial GWs generated during cosmic inflation, which allows us to combine our results with upper limits from big-bang nucleosynthesis, the cosmic microwave background, and LIGO–Virgo–KAGRA. © 2024, CC BY.

关键词： Cosmology

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

arXiv

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arXiv 2024年

作者： Agazie, Gabriella Anumarlapudi, Akash Archibald, Anne M. Arzoumanian, Zaven Baier, Jeremy G. Baker, Paul T. Bécsy, 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 Gültekin, 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.W. Joseph 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. Sosa Fiscella, Sophia V. 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 Center for Gravitation Cosmology and Astrophysics Department of Physics University of Wisconsin-Milwaukee P.O. Box 413 MilwaukeeWI53201 United States Newcastle University NE1 7RU United Kingdom X-Ray Astrophysics Laboratory NASA Goddard Space Flight Center Code 662 GreenbeltMD20771 United States Department of Physics Oregon State University CorvallisOR97331 United States Department of Physics and Astronomy Widener University One University Place ChesterPA19013 United States Physics Department University of Florida GainesvilleFL32611 United States Cornell Center for Astrophysics and Planetary Science Department of Astronomy Cornell University IthacaNY14853 United States Cornell Center for Advanced Computing Cornell University IthacaNY14853 United States Institute for Gravitational Wave Astronomy School of Physics and Astronomy University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom Department of Physics and Astronomy West Virginia University P.O. Box 6315 MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States Department of Physics University of Connecticut 196 Auditorium Road U-3046 StorrsCT06269-3046 United States Department of Physics and Astronomy Vanderbilt University 2301 Vanderbilt Place NashvilleTN37235 United States Department of Physics New Mexico Institute of Mining and Technology 801 Leroy Place SocorroNM87801 United States Department of Physics Montana State University BozemanMT59717 United States Department of Physics and Astronomy Franklin & Marshall College P.O. Box 3003 LancasterPA17604 United States National Research Council Research Associate National Academy of Sciences WashingtonDC20001 United States Naval Research Laboratory WashingtonDC20375 United States Department of Physics and Astronomy University of British Columbia 6224 Agricultural Road VancouverBCV6T 1Z1 Cana

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. This signal has been interpreted as indicative of the presence of a nanohertz stochastic gravitational wave background. To explore the internal consistency of this result we investigate how the recovered signal strength changes as we remove the pulsars one by one from the data set. We calculate the signal strength using the (noise-marginalized) optimal statistic, a frequentist metric designed to measure correlated excess power in the residuals of the arrival times of the radio pulses. We identify several features emerging from this analysis that were initially unexpected. The significance of these features, however, can only be assessed by comparing the real data to synthetic data sets. After conducting identical analyses on simulated data sets, we do not find anything inconsistent with the presence of a stochastic gravitational wave background in the NANOGrav 15-year data. The methodologies developed here can offer additional tools for application to future, more sensitive data sets. While this analysis provides an internal consistency check of the NANOGrav results, it does not eliminate the necessity for additional investigations that could identify potential systematics or uncover unmodeled physical phenomena in the data. © 2024, CC BY.

关键词： Gravitational effects

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How to Detect an Astrophysical Nanohertz Gravitational-Wave Background

arXiv

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arXiv 2023年

作者： Bécsy, Bence Cornish, Neil J. Meyers, Patrick M. Kelley, Luke Zoltan Agazie, Gabriella Anumarlapudi, Akash Archibald, Anne M. Arzoumanian, Zaven Baker, Paul T. Blecha, Laura Brazier, Adam Brook, Paul R. Burke-Spolaor, Sarah Casey-Clyde, J. Andrew Charisi, Maria Chatterjee, Shami Chatziioannou, Katerina Cohen, Tyler Cordes, James M. Crawford, Fronefield Cromartie, H. Thankful Crowter, Kathryn DeCesar, Megan E. Demorest, Paul B. Dolch, Timothy Ferrara, Elizabeth C. Fiore, William Fonseca, Emmanuel Freedman, Gabriel E. Garver-Daniels, Nate Gentile, Peter A. Glaser, Joseph Good, Deborah C. Gültekin, Kayhan Hazboun, Jeffrey S. Hourihane, Sophie Jennings, Ross J. Johnson, Aaron D. Jones, Megan L. Kaiser, Andrew R. Kaplan, David L. Kerr, Matthew Key, Joey S. Laal, Nima Lam, Michael T. Lamb, William G. Lazio, T.W. Joseph Lewandowska, Natalia Littenberg, Tyson B. 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. Mingarelli, Chiara M.F. Mitridate, Andrea 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. Sardesai, Shashwat C. Schmiedekamp, Ann Schmiedekamp, Carl Schmitz, Kai Shapiro-Albert, Brent J. Siemens, Xavier Simon, Joseph Siwek, Magdalena S. Sosa Fiscella, Sophia V. Stairs, Ingrid H. Stinebring, Daniel R. Stovall, Kevin Susobhanan, Abhimanyu Swiggum, Joseph K. Taylor, Stephen R. Turner, Jacob E. Unal, Caner Vallisneri, Michele van Haasteren, Rutger Vigeland, Sarah J. Wahl, Haley M. Witt, Caitlin A. Young, Olivia Department of Physics Oregon State University CorvallisOR97331 United States eXtreme Gravity Institute Department of Physics Montana State University BozemanMT59717 United States Division of Physics Mathematics and Astronomy California Institute of Technology PasadenaCA91125 United States Department of Astronomy University of California at Berkeley BerkeleyCA94720 United States Center for Gravitation Cosmology and Astrophysics Department of Physics University of Wisconsin-Milwaukee P.O. Box 413 MilwaukeeWI53201 United States Newcastle University NE1 7RU United Kingdom X-Ray Astrophysics Laboratory NASA Goddard Space Flight Center Code 662 GreenbeltMD20771 United States Department of Physics and Astronomy Widener University One University Place ChesterPA19013 United States Physics Department University of Florida GainesvilleFL32611 United States Cornell Center for Astrophysics and Planetary Science Department of Astronomy Cornell University IthacaNY14853 United States Cornell Center for Advanced Computing Cornell University IthacaNY14853 United States Institute for Gravitational Wave Astronomy School of Physics and Astronomy University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom Department of Physics and Astronomy West Virginia University P.O. Box 6315 MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States Department of Physics University of Connecticut 196 Auditorium Road U-3046 StorrsCT06269-3046 United States Department of Physics and Astronomy Vanderbilt University 2301 Vanderbilt Place NashvilleTN37235 United States Department of Physics New Mexico Institute of Mining and Technology 801 Leroy Place SocorroNM87801 United States Department of Physics and Astronomy Franklin & Marshall College P.O. Box 3003 LancasterPA17604 United States Department of Physics and Astronomy University of

Analyses of pulsar timing data have provided evidence for a stochastic gravitational wave background in the nHz frequency band. The most plausible source of such a background is the superposition of signals from millions of supermassive black hole binaries. The standard statistical techniques used to search for such a background and assess its significance make several simplifying assumptions, namely: i) Gaussianity;ii) isotropy;and most often iii) a power-law spectrum. However, a stochastic background from a finite collection of binaries does not exactly satisfy any of these assumptions. To understand the effect of these assumptions, we test standard analysis techniques on a large collection of realistic simulated datasets. The dataset length, observing schedule, and noise levels were chosen to emulate the NANOGrav 15-year dataset. Simulated signals from millions of binaries drawn from models based on the Illustris cosmological hydrodynamical simulation were added to the data. We find that the standard statistical methods perform remarkably well on these simulated datasets, despite their fundamental assumptions not being strictly met. They are able to achieve a confident detection of the background. However, even for a fixed set of astrophysical parameters, different realizations of the universe result in a large variance in the significance and recovered parameters of the background. We also find that the presence of loud individual binaries can bias the spectral recovery of the background if we do not account for them. © 2023, CC BY.

关键词： Gravity waves

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The NANOGrav 12.5-year data set: A computationally efficient eccentric binary search pipeline and constraints on an eccentric supermassive binary candidate in 3C 66B

arXiv

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arXiv 2023年

作者： Agazie, Gabriella Arzoumanian, Zaven Baker, Paul T. Bécsy, Bence Blecha, Laura Blumer, Harsha Brazier, Adam Brook, Paul R. Burke-Spolaor, Sarah Casey-Clyde, J. Andrew Charisi, Maria Chatterjee, Shami Cheeseboro, Belinda D. Cohen, Tyler Cordes, James M. Cornish, Neil J. Crawford, Fronefield Cromartie, H. Thankful DeCesar, Megan E. Demorest, Paul B. Dey, Lankeswar Dolch, Timothy Ellis, Justin A. Ferdman, Robert D. Ferrara, Elizabeth C. Fiore, William Fonseca, Emmanuel Freedman, Gabriel E. Garver-Daniels, Nate Gentile, Peter A. Glaser, Joseph Good, Deborah C. Gopakumar, Achamveedu Gültekin, Kayhan Hazboun, Jeffrey S. Jennings, Ross J. Johnson, Aaron D. Jones, Megan L. Kaiser, Andrew R. Kaplan, David L. Kelley, Luke Zoltan Key, Joey S. Laal, Nima Lam, Michael T. Lamb, William G. Lazio, T.W. Joseph 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. Meyers, Patrick M. Mingarelli, Chiara M.F. Mitridate, Andrea Ng, Cherry Nice, David J. Ocker, Stella Koch Olum, Ken D. Pennucci, Timothy T. Pol, Nihan S. Radovan, Henri A. Ransom, Scott M. Ray, Paul S. Romano, Joseph D. Sardesai, Shashwat C. Schmitz, Kai Siemens, Xavier Simon, Joseph Siwek, Magdalena S. Sosa Fiscella, Sophia V. Spiewak, Renée Stairs, Ingrid H. Stinebring, Daniel R. Stovall, Kevin Susobhanan, Abhimanyu Swiggum, Joseph K. Taylor, Stephen R. Turner, Jacob E. Unal, Caner Vallisneri, Michele Vigeland, Sarah J. Witt, Caitlin A. Young, Olivia Center for Gravitation Cosmology and Astrophysics Department of Physics University of Wisconsin-Milwaukee P.O. Box 413 MilwaukeeWI53201 United States X-Ray Astrophysics Laboratory NASA Goddard Space Flight Center Code 662 GreenbeltMD20771 United States Department of Physics and Astronomy Widener University One University Place ChesterPA19013 United States Department of Physics Oregon State University CorvallisOR97331 United States Physics Department University of Florida GainesvilleFL32611 United States Department of Physics and Astronomy West Virginia University P.O. Box 6315 MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States Cornell Center for Astrophysics and Planetary Science Department of Astronomy Cornell University IthacaNY14853 United States Cornell Center for Advanced Computing Cornell University IthacaNY14853 United States Institute for Gravitational Wave Astronomy School of Physics and Astronomy University of Birmingham Birmingham EdgbastonB15 2TT United Kingdom Department of Physics University of Connecticut 196 Auditorium Road U-3046 StorrsCT06269-3046 United States Department of Physics and Astronomy Vanderbilt University 2301 Vanderbilt Place NashvilleTN37235 United States Department of Physics New Mexico Institute of Mining and Technology 801 Leroy Place SocorroNM87801 United States Department of Physics Montana State University BozemanMT59717 United States Department of Physics and Astronomy Franklin & Marshall College P.O. Box 3003 LancasterPA17604 United States George Mason University The Naval Research Laboratory WashingtonDC20375 United States National Radio Astronomy Observatory 1003 Lopezville Rd. SocorroNM87801 United States Department of Physics Hillsdale College 33 E. College Street HillsdaleMI49242 United States Eureka Scientific 2452 Delmer Street Suite 100 Oakland

The radio galaxy 3C 66B has been hypothesized to host a supermassive black hole binary (SMBHB) at its center based on electromagnetic observations. Its apparent 1.05-year period and low redshift (∼ 0.02) make it an interesting testbed to search for low-frequency gravitational waves (GWs) using Pulsar Timing Array (PTA) experiments. This source has been subjected to multiple searches for continuous GWs from a circular SMBHB, resulting in progressively more stringent constraints on its GW amplitude and chirp mass. In this paper, we develop a pipeline for performing Bayesian targeted searches for eccentric SMBHBs in PTA data sets, and test its efficacy by applying it on simulated data sets with varying injected signal strengths. We also search for a realistic eccentric SMBHB source in 3C 66B using the NANOGrav 12.5-year data set employing PTA signal models containing Earth term-only as well as Earth+Pulsar term contributions using this pipeline. Due to limitations in our PTA signal model, we get meaningful results only when the initial eccentricity e0 0.1. We find no evidence for an eccentric SMBHB signal in our data, and therefore place 95% upper limits on the PTA signal amplitude of 88.1 ± 3.7 ns for the Earth term-only and 81.74 ±0.86 ns for the Earth+Pulsar term searches for e0 0.1. Similar 95% upper limits on the chirp mass are (1.98 ± 0.05) × 109 M☉ and (1.81 ± 0.01) × 109 M☉. These upper limits, while less stringent than those calculated from a circular binary search in the NANOGrav 12.5-year data set, are consistent with the SMBHB model of 3C 66B developed from electromagnetic observations. © 2023, CC BY.

关键词： Gravity waves

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A genomic catalog of Earth's microbiomes (November, 10.1038/s41587-020-0718-6, 2020)

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NATURE BIOtechnology 2021年第4期39卷 520-520页

作者： Nayfach, Stephen Roux, Simon Seshadri, Rekha Udwary, Daniel Varghese, Neha Schulz, Frederik Wu, Dongying Paez-Espino, David Chen, I-Min Huntemann, Marcel Palaniappan, Krishna Ladau, Joshua Mukherjee, Supratim Reddy, T. B. K. Nielsen, Torben Kirton, Edward Faria, Jose P. Edirisinghe, Janaka N. Henry, Christopher S. Jungbluth, Sean P. Chivian, Dylan Dehal, Paramvir Wood-Charlson, Elisha M. Arkin, Adam P. Tringe, Susannah G. Visel, Axel Woyke, Tanja Mouncey, Nigel J. Ivanova, Natalia N. Kyrpides, Nikos C. Eloe-Fadrosh, Emiley A. DOE Joint Genome Institute Berkeley CA USA Argonne National Laboratory Argonne IL USA Lawrence Berkeley National Laboratory Berkeley CA USA Travessa Alexandre da Conceicao ALGAplus Ilhavo Portugal Department of Marine Biology and Oceanography Institute of Marine Sciences-CSIC Barcelona Spain Scripps Institution of Oceanography University of California San Diego La Jolla CA USA School of Biotechnology and Biomolecular Sciences UNSW Sydney Sydney New South Wales Australia Centre for Microbiology and Environmental Systems Science University of Vienna Vienna Austria Department of Environmental Science Aarhus University Roskilde Denmark Rumen Microbiology Animal Science AgResearch Grasslands Research Centre Palmerston North New Zealand Department of Biology Pennsylvania State University University Park Pennsylvania PA USA Scottish Association for Marine Science Oban UK Department of Earth and Environmental Sciences University of Minnesota Minneapolis MN USA Department of Marine Science University of Texas Austin Austin TX USA Institute of Microbiology of the Czech Academy of Sciences Praha 4 Czech Republic Department of Biology University of Akron Akron OH USA Department of Chemical Engineering & eScience Institute University of Washington Seattle WA USA Department of Biology University of North Alabama Florence AL USA Life and Environmental Sciences and Sierra Nevada Research Institute University of California Merced Merced CA USA Ecole Polytechnique Federale de Lausanne Lausanne Switzerland Department of Soil Science University of Wisconsin-Madison Madison WI USA School of Biological Sciences Georgia Institute of Technology Atlanta GA USA Department of Aquatic Sciences and Assessment Swedish University of Agricultural Sciences Uppsala Sweden Department of Biology Boston University Boston MA USA University of Arkansas for Medical Sciences Little Rock AR USA Biology Department University of Massachusetts Amherst Amherst MA USA

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

关键词： Computational biology and bioinformatics Microbiology

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Stack-u-net: Refinement network for improved optic disc and cup image segmentation

arXiv

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arXiv 2018年

作者： Sevastopolsky, Artem Drapak, Stepan Kiselev, Konstantin Snyder, Blake M. Keenan, Jeremy D. Georgievskaya, Anastasia Youth Laboratories Ltd. Moscow Russia Skolkovo Institute of Science and Technology Moscow Russia Lomonosov Moscow State University Moscow Russia University of Colorado Denver School of Medicine AuroraCO United States Francis I. Proctor Foundation University of California San Francisco San FranciscoCA United States Deparment of Ophthalmology University of California San Francisco San FranciscoCA United States Institution of Russian Academy of Sciences Dorodnicyn Computing Centre of RAS Moscow Russia

In this work, we propose a special cascade network for image segmentation, which is based on the U-Net networks as building blocks and the idea of the iterative refinement. The model was mainly applied to achieve higher recognition quality for the task of finding borders of the optic disc and cup, which are relevant to the presence of glaucoma. Compared to a single U-Net and the state-of-the-art methods for the investigated tasks, the presented method outperforms others by multiple benchmarks without a need for increasing the volume of datasets. Our experiments include comparison with the best-known methods on publicly available databases DRIONS-DB, RIM-ONE v.3, DRISHTI-GS, and evaluation on a private data set collected in collaboration with University of California San Francisco Medical School. The analysis of the architecture details is presented. It is argued that the model can be employed for a broad scope of image segmentation problems of similar nature. Copyright © 2018, The Authors. All rights reserved.

关键词： Image segmentation

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An Evolutionary Learning Approach to Self-configuring Image Pipelines in the Context of Carbon Fiber Fault Detection

An Evolutionary Learning Approach to Self-configuring Image ...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Andreas Margraf Anthony Stein Leonhard Engstler Steffen Geinitz Jorg Hahner Fraunhofer IGCV Institution for Casting Composite and Processing Technology Germany Organic Computing Group Institute of Computer Science University of Augsburg Germany Org. Comput. Group Universitat Augsburg Augsburg Bayern DE

Carbon fiber reinforced plastics (CFRP) play a key role for the production of leightweight structures. Simultaneously, online quality inspection of CFRP becomes more important, especially for environments with high safety standards. In this context, vision systems aim to find defects of different shape, size, contour and orientation. Little effort, however, has been made in detecting defect areas in images taken from the surface of carbon fibers. A common approach for segmenting filament defects are edge detection and thresholding. With every change of material and process adjustments, the filter parameters have to be adapted. In this paper, we propose a cartesian genetic programming (CGP) approach to semi-automatically select the best parameters. This strategy saves time for parameter identification while at the same time increases precision. A test run on randomly selected samples shows how the approach can substantially improve detection reliability.

关键词： Carbon Pipelines Production Image processing Cameras Monitoring Lighting

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Genomic basis for RNA alterations in cancer (vol 578, pg 129, 2020)

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NATURE 2023年第7948期614卷 E37-E37页

作者： Calabrese, Claudia Davidson, Natalie R. Demircioglu, Deniz Fonseca, Nuno A. He, Yao Lehmann, Kjong-Van Liu, Fenglin Shiraishi, Yuichi Soulette, Cameron M. Urban, Lara Greger, Liliana Li, Siliang Liu, Dongbing Perry, Marc D. Xiang, Qian Zhang, Fan Zhang, Junjun Bailey, Peter Erkek, Serap Hoadley, Katherine A. Hou, Yong Huska, Matthew R. Kilpinen, Helena Korbel, Jan O. Marin, Maximillian G. Markowski, Julia Nandi, Tannistha Pan-Hammarstrom, Qiang Pedamallu, Chandra Sekhar Siebert, Reiner Stark, Stefan G. Su, Hong Tan, Patrick Waszak, Sebastian M. Yung, Christina Zhu, Shida Awadalla, Philip Creighton, Chad J. Meyerson, Matthew Ouellette, B. F. Francis Wu, Kui Yang, Huanming Brazma, Alvis Brooks, Angela N. Goke, Jonathan Ratsch, Gunnar Schwarz, Roland F. Stegle, Oliver Zhang, Zemin European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources Universidade do Porto Vairão Portugal Berlin Institute for Medical Systems Biology Max Delbruck Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) partner site Berlin Germany German Cancer Research Center (DKFZ) Heidelberg Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) Partner site Berlin Berlin Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Division of Computational Genomics and Systems Genetics German Cancer Research Center (DKFZ) Heidelberg Germany ETH Zurich Zurich Switzerland Memorial Sloan Kettering Cancer Center New York NY USA Weill Cornell Medical College New York NY USA SIB Swiss Institute of Bioinformatics Lausanne Switzerland University Hospital Zurich Zurich Switzerland Computational Biology Center Memorial Sloan Kettering Cancer Center New York NY USA Department of Biology ETH Zurich Zürich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Korea University Seoul South Korea Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA National University of Singapore Singapore Singapore Genome Institute of Singapore Singapore Singapore Computational and Systems Biology Genome Institute of Singap

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Global climate

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Bulletin of the American Meteorological Society 2020年第8期101卷 S9-S128页

作者： Dunn, R.J.H. Stanitski, D.M. Gobron, N. Willett, K.M. Met Office Hadley Centre Exeter United Kingdom NOAA/OAR Earth System Research Laboratories Boulder Colorado European Commission Joint Research Centre Ispra Italy Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Met Office Hadley Centre Exeter United Kingdom University of Reading Reading United Kingdom Department of Atmospheric and Planetary Science Hampton University Hampton Virginia NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina University of Bremen Bremen Germany NOAA/OAR Earth System Research Laboratories Boulder Colorado Centro de Investigaciones sobre Desertificación - Spanish National Research Council Moncada (Valencia) Spain and Regional Climate Group Department of Earth Sciences University of Gothenburg Gothenburg Sweden Instituto de Geografía Pontificia Universidad Católica de Valparaíso Valparaíso Chile NOAA/OAR ESRL Global Monitoring Laboratory Boulder Colorado Department of Civil and Environmental Engineering Princeton University Princeton New Jersey Global Precipitation Climatology Centre Deutscher Wetterdienst Offenbach Germany University of Reading Reading United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom National Oceanography Centre Southampton United Kingdom School of Engineering Newcastle University Newcastle-upon-Tyne United Kingdom Université de Paris Institut de physique du globe de Paris CNRS IGN Paris France and ENSG-Géomatique IGN Marne-la-Vallée France Global Modeling and Assimilation Office NASA Goddard Space Flight Center Greenbelt Maryland Sorbonne Université Paris France Universität Hamburg Hamburg Germany Department of Meteorology University of Reading Reading United Kingdom Geology Department University Centre in Svalbard Longyearbyen Norway Jet Propulsion Laboratory California Institute of Technology Wrightwood Calif

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Deep learning for multi-messenger astrophysics a gateway for discovery in the big data era

arXiv

引用

arXiv 2019年

作者： Allen, Gabrielle Andreoni, Igor Bachelet, Etienne Bruce Berriman, G. Bianco, Federica B. Biswas, Rahul Kind, Matias Carrasco Chard, Kyle Cho, Minsik Cowperthwaite, Philip S. Etienne, Zachariah B. George, Daniel Gibbs, Tom Graham, Matthew Gropp, William Gupta, Anushri Haas, Roland Huerta, E.A. Jennings, Elise Katz, Daniel S. Khan, Asad Kindratenko, Volodymyr Kramer, William T.C. Liu, Xin Mahabal, Ashish McHenry, Kenton Miller, J.M. Neubauer, M.S. Oberlin, Steve Olivas, Alexander R. Rosofsky, Shawn Ruiz, Milton Saxton, Aaron Schutz, Bernard Schwing, Alex Seidel, Ed Shapiro, Stuart L. Shen, Hongyu Shen, Yue Sipocz, Brigitta M. Sun, Lunan Towns, John Tsokaros, Antonios Wei, Wei Wells, Jack Williams, Timothy J. Xiong, Jinjun Zhao, Zhizhen NCSA University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Astronomy University of Illinois at Urbana-Champaign UrbanaIL61801 United States California Institute of Technology 1200 E California Blvd PasadenaCA91125 United States Las Cumbres Observatory 6740 Cortona Drive Suite 102 GoletaCA93117 United States Caltech/IPAC-NExScI 1200 E California Blvd PasadenaCA91125 United States Department of Physics and Astronomy University of Delaware NewarkDE19716 United States Joseph R. Biden Jr . School of Public Policy and Administration University of Delaware NewarkDE19716 United States Data Science Institute University of Delaware NewarkDE19716 United States Center for Urban Science and Progress New York University 370 Jay St BrooklynNY11201 United States The Oskar Klein Centre for Cosmoparticle Physics Stockholm University AlbaNova StockholmSE-106 91 Sweden University of Chicago ChicagoIL60605 United States IBM Systems AustinTX78717 United States Observatories of the Carnegie Institution for Science 813 Santa Barbara St. PasadenaCA91101 United States Department of Mathematics West Virginia University MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States NVIDIA 2788 San Tomas Expressway Santa ClaraCA95050 United States Center for Data Driven Discovery Caltech PasadenaCA91125 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Argonne National Laboratory Leadership Computing Facility LemontIL60439 United States Department of Electrical & Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Information Sciences University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 Unit

This report provides an overview of recent work that harnesses the Big Data Revolution and Large Scale computing to address grand computational challenges in Multi-Messenger Astrophysics, with a particular emphasis on real-time discovery campaigns. Acknowledging the transdisciplinary nature of Multi-Messenger Astrophysics, this document has been prepared by members of the physics, astronomy, computer science, data science, software and cyberinfrastructure communities who attended the NSF-, DOE- and NVIDIA-funded "Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale" workshop, hosted at the National Center for Supercomputing Applications, October 17-19, 2018. Highlights of this report include unanimous agreement that it is critical to accelerate the development and deployment of novel, signal-processing algorithms that use the synergy between artificial intelligence (AI) and high performance computing to maximize the potential for scientific discovery with Multi-Messenger Astrophysics. We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics;(ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data;(iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups;(iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era;(v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics. Copyright © 2019, The Authors. All rights reserved.

关键词： Big data

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