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arXiv 2020年

作者： Reardon, Daniel J. Coles, William A. Bailes, Matthew Bhat, N.D. Ramesh Dai, Shi Hobbs, George B. Kerr, Matthew Manchester, Richard N. Oslowski, Stefan Parthasarathy, Aditya Russell, Christopher J. Shannon, Ryan M. Spiewak, Renée Toomey, Lawrence Tuntsov, Artem V. van Straten, Willem Walker, Mark A. Wang, Jingbo Zhang, Lei Zhu, Xing-Jiang Centre for Astrophysics and Supercomputing Swinburne University of Technology P.O. Box 218 HawthornVIC3122 Australia Australia Electrical and Computer Engineering University of California at San Diego La Jolla CA United States International Centre for Radio Astronomy Research Curtin University BentleyWA6102 Australia Australia Telescope National Facility CSIRO Astronomy & Space Science P.O. Box 76 EppingNSW1710 Australia Space Science Division Naval Research Laboratory WashingtonDC20375-5352 United States CSIRO Scientific Computing Services Australian Technology Park Locked Bag 9013 AlexandriaNSW1435 Australia Manly Astrophysics 15/41-42 East Esplanade ManlyNSW2095 Australia Institute for Radio Astronomy & Space Research Auckland University of Technology Private Bag 92006 Auckland1142 New Zealand Xinjiang Astronomical Observatory Chinese Academy of Sciences 150 Science 1-Street Xinjiang Urumqi830011 China National Astronomical Observatories Chinese Academy of Sciences A20 Datun Road Chaoyang District Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China School of Physics and Astronomy Monash University VIC3800 Australia

Intensity scintillations of radio pulsars are known to originate from interference between waves scattered by the electron density irregularities of interstellar plasma, often leading to parabolic arcs in the two-dimensional power spectrum of the recorded dynamic spectrum. The degree of arc curvature depends on the distance to the scattering plasma and its transverse velocity with respect to the line-of-sight. We report the observation of annual and orbital variations in the curvature of scintillation arcs over a period of 16 years for the bright millisecond pulsar, PSR J0437−4715. These variations are the signature of the relative transverse motions of the Earth, pulsar, and scattering medium, which we model to obtain precise measurements of parameters of the pulsar’s binary orbit and the scattering medium itself. We observe two clear scintillation arcs in most of our >5000 observations and we show that they originate from scattering by thin screens located at distances D1 = 89.8 ± 0.4 pc and D2 = 124 ± 3 pc from Earth. The best-fit scattering model we derive for the brightest arc yields the pulsar’s orbital inclination angle i = 137.1 ± 0.3◦, and longitude of ascending node, Ω = 206.3 ± 0.4◦. Using scintillation arcs for precise astrometry and orbital dynamics can be superior to modelling variations in the diffractive scintillation timescale, because the arc curvature is independent of variations in the level of turbulence of interstellar plasma. This technique can be used in combination with pulsar timing to determine the full three-dimensional orbital geometries of binary pulsars, and provides parameters essential for testing theories of gravity and constraining neutron star masses. Copyright © 2020, The Authors. All rights reserved.

关键词： Pulsars

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Enhancing Supervised Classifications with Metamorphic Relations

Enhancing Supervised Classifications with Metamorphic Relati...

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IEEE/ACM International Workshop on Metamorphic Testing (MET)

作者： Liming Xu Dave Towey Andrew P. French Steve Benford Zhi Quan Zhou Tsong Yueh Chen International Doctoral Innovation Centre University of Nottingham Ningbo China Ningbo Zhejiang China AIOP Group University of Nottingham Ningbo China Ningbo Zhejiang China School of Computer Science University of Nottingham Nottingham UK School of Computing and Information Technology University of Wollongong Wollongong NSW Australia Dept. of Computer Science and Software Engineering Swinburne University of Technology Hawthorn VIC Australia

We report on a novel use of metamorphic relations (MRs) in machine learning: instead of conducting metamorphic testing, we use MRs for the augmentation of the machine learning algorithms themselves. In particular, we report on how MRs can enable enhancements to an image classification problem of images containing hidden visual markers (“Artcodes”). Working on an original classifier, and using the characteristics of two different categories of images, two MRs, based on separation and occlusion, were used to improve the performance of the classifier. Our experimental results show that the MR-augmented classifier achieves better performance than the original classifier, algorithms, and extending the use of MRs beyond the context of software testing.

关键词： Machine learning Testing Software Forestry Histograms Visualization computer science

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Erratum: Towards a muon collider

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The European Physical Journal C 2024年第1期84卷 1-4页

作者： Accettura, Carlotta Adams, Dean Agarwal, Rohit Ahdida, Claudia Aimè, Chiara Amapane, Nicola Amorim, David Andreetto, Paolo Anulli, Fabio Appleby, Robert Apresyan, Artur Apyan, Aram Arsenyev, Sergey Asadi, Pouya Mahmoud, Mohammed Attia Azatov, Aleksandr Back, John Balconi, Lorenzo Bandiera, Laura Barlow, Roger Bartosik, Nazar Barzi, Emanuela Batsch, Fabian Bauce, Matteo Berg, J. Scott Bersani, Andrea Bertarelli, Alessandro Bertolin, Alessandro Black, Kevin Boattini, Fulvio Bogacz, Alex Bonesini, Maurizio Bordini, Bernardo Bottaro, Salvatore Bottura, Luca Braghieri, Alessandro Breschi, Marco Bruhwiler, Natalie Buffat, Xavier Buonincontri, Laura Burrows, Philip N. Burt, Graeme Buttazzo, Dario Caiffi, Barbara Calviani, Marco Calzaferri, Simone Calzolari, Daniele Capdevilla, Rodolfo Carli, Christian Casaburo, Fausto Casarsa, Massimo Castelli, Luca Catanesi, Maria Gabriella Cavallucci, Lorenzo Cavoto, Gianluca Celiberto, Francesco Giovanni Celona, Luigi Cerri, Alessandro Cesarini, Gianmario Cesarotti, Cari Chachamis, Grigorios Chance, Antoine Chen, Siyu Chien, Yang-Ting Chiesa, Mauro Colaleo, Anna Collamati, Francesco Collazuol, Gianmaria Costa, Marco Craig, Nathaniel Curatolo, Camilla Curtin, David Da Molin, Giacomo Dam, Magnus Damerau, Heiko Dasu, Sridhara de Blas, Jorge De Curtis, Stefania De Matteis, Ernesto De Rosa, Stefania Delahaye, Jean-Pierre Denisov, Dmitri Denizli, Haluk Densham, Christopher Dermisek, Radovan Di Luzio, Luca Di Meco, Elisa Di Micco, Biagio Dienes, Keith Diociaiuti, Eleonora Dorigo, Tommaso Dudarev, Alexey Edgecock, Robert Errico, Filippo Fabbrichesi, Marco Farinon, Stefania Ferrari, Anna Somoza, Jose Antonio Ferreira Filthaut, Frank Fiorina, Davide Fol, Elena Forslund, Matthew Franceschini, Roberto Ximenes, Rui Franqueira Gabrielli, Emidio Gallinaro, Michele Garosi, Francesco Giambastiani, Luca Gianelle, Alessio Gilardoni, Simone Giove, Dario Augusto Giraldin, Carlo Glioti, Alfredo Greco, Mario Greljo, Admir Groeber, Ramona Grojean, Christophe Grudiev, Alexej Gu, Jiayin Han, Chengcheng Han, T Organisation Européenne pour la Recherche Nucléaire (CERN) Geneva Switzerland STFC Rutherford Appleton Laboratory (RAL) Oxford UK Computer Science Department University of California (UC) Berkeley USA Dipartimento di Fisica Università di Pavia Pavia Italy INFN Sezione di Pavia Pavia Italy INFN Sezione di Torino Turin Italy Dipartimento di Fisica Università di Torino Turin Italy INFN Sezione di Padova Padua Italy INFN Sezione di Roma Rome Italy School of Physics and Astronomy University of Manchester Manchester UK Fermi National Accelerator Laboratory Batavia USA Department of Physics Brandeis University Waltham USA IRFU CEA University Paris-Saclay Gif-sur-Yvette France Center for Theoretical Physics Massachusetts Institute of Technology Cambridge USA Center for High Energy Physics (CHEP-FU) Fayoum University El-Fayoum Egypt SISSA International School for Advanced Studies Trieste Italy INFN Sezione di Trieste Trieste Italy Department of Physics University of Warwick Coventry UK Dipartimento di Fisica Università di Milano Milan Italy INFN Laboratori Acceleratori e Superconduttività Applicata (LASA) Milan Italy INFN Sezione di Ferrara Ferrara Italy School of Computing and Engineering The University of Huddersfield Huddersfield UK Ohio State University Columbus USA Brookhaven National Laboratory Upton USA INFN Sezione di Genova Genoa Italy University of Wisconsin Wisconsin USA Center for Advanced Studies of Accelerators Jefferson Lab Newport News USA INFN Sezione di Milano Bicocca Milan Italy Dipartimento di Fisica Università di Milano Bicocca Milan Italy Scuola Normale Superiore Pisa Italy INFN Sezione di Pisa Pisa Italy Dipartimento di Ingegneria dell’Energia Elettrica e dell’Informazione Università di Bologna Bologna Italy INFN Sezione di Bologna Bologna Italy Department of Physics University of California (UC) Berkeley USA Dipartimento di Fisica e Astronomia Università di Padova Padoa Italy John Adams Institute University of O

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Gammapy: A Python package for gamma-ray astronomy

arXiv

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arXiv 2023年

作者： Donath, Axel Terrier, Régis Remy, Quentin Sinha, Atreyee Nigro, Cosimo Pintore, Fabio Khélifi, Bruno Olivera-Nieto, Laura Ruiz, Jose Enrique Brügge, Kai Linhoff, Maximilian Contreras, Jose Luis Acero, Fabio Aguasca-Cabot, Arnau Berge, David Bhattacharjee, Pooja Buchner, Johannes Boisson, Catherine Fidalgo, David Carreto Chen, Andrew de Lavergne, Mathieu de Bony de Miranda Cardoso, José Vinícius Deil, Christoph Füßling, Matthias Funk, Stefan Giunti, Luca Hinton, Jim Jouvin, Léa King, Johannes Lefaucheur, Julien Lemoine-Goumard, Marianne Lenain, Jean-Philippe López-Coto, Rubén Mohrmann, Lars Morcuende, Daniel Panny, Sebastian Regeard, Maxime Saha, Lab Siejkowski, Hubert Siemiginowska, Aneta Sipőcz, Brigitta M. Unbehaun, Tim van Eldik, Christopher Vuillaume, Thomas Zanin, Roberta Center for Astrophysics Harvard and Smithsonian United States Université de Paris Cité CNRS Astroparticule et Cosmologie ParisF-75013 France Max-Planck-Institut für Kernphysik P.O. Box 103980 HeidelbergD 69029 Germany IPARCOS Institute EMFTEL Department Universidad Complutense de Madrid MadridE-28040 Spain The Barcelona Institute of Science and Technology Campus UAB Bellaterra Barcelona08193 Spain INAF/IASF Palermo Via U. La Malfa 153 PA Palermo90146 Italy Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Point8 GmbH Germany Astroparticle Physics Department of Physics TU Dortmund University Otto-Hahn-Str. 4a DortmundD-44227 Germany Université Paris-Saclay Université Paris Cité CEA CNRS AIM Gif-sur-YvetteF-91191 France c. Martí i Franqués 1 Barcelona08028 Spain c. Martí i Franqués 1 Barcelona08028 Spain c. Gran Capità 2-4 Barcelona08034 Spain Departament de Física Quàntica i Astrofísica Institut de Ciències del Cosmos Universitat de Barcelona IEEC-UB Martí i Franquès 1 Barcelona08028 Spain ZeuthenD-15738 Germany Institute of physics Humboldt-University of Berlin BerlinD-12489 Germany Université Savoie Mont Blanc CNRS Laboratoire d’Annecy de Physique des Particules - IN2P3 Annecy74000 France Max Planck Institute for extraterrestrial Physics Giessenbachstrasse Garching85748 Germany Laboratoire Univers et Théories Observatoire de Paris Université PSL Université Paris Cité CNRS MeudonF-92190 France Max Planck Computing and Data Facility Gießenbachstraße 2 Garching85748 Germany School of Physics University of the Witwatersrand 1 Jan Smuts Avenue Braamfontein Johannesburg2050 South Africa The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Saupfercheckweg Heidelberg1 69117 Germany Friedrich-Alexander-Universität Erlangen-Nürnberg Nikolaus-Fiebiger Strasse 2 Erlangen91058 Germany IRFU CEA Université Pa

Context. Traditionally, TeV-γ-ray astronomy has been conducted by experiments employing proprietary data and analysis software. However, the next generation of γ-ray instruments, such as the Cherenkov Telescope Array Observatory (CTAO), will be operated as open observatories. Alongside the data, they will also make the associated software tools available to a wider community. This necessity prompted the development of open, high-level, astronomical software customized for high-energy astrophysics. Aims. In this article, we present Gammapy, an open-source Python package for the analysis of astronomical γ-ray data, and illustrate the functionalities of its first long-term-support release, version 1.0. Built on the modern Python scientific ecosystem, Gammapy provides a uniform platform for reducing and modeling data from different γ-ray instruments for many analysis scenarios. Gammapy complies with several well-established data conventions in high-energy astrophysics, providing serialized data products that are interoperable with other software packages. Methods. Starting from event lists and instrument response functions, Gammapy provides functionalities to reduce these data by binning them in energy and sky coordinates. Several techniques for background estimation are implemented in the package to handle the residual hadronic background affecting γ-ray instruments. After the data are binned, the flux and morphology of one or more γ-ray sources can be estimated using Poisson maximum likelihood fitting and assuming a variety of spectral, temporal, and spatial models. Estimation of flux points, likelihood profiles, and light curves is also supported. Results. After describing the structure of the package, we show, using publicly available gamma-ray data, the capabilities of Gammapy in multiple traditional and novel γ-ray analysis scenarios, such as spectral and spectro-morphological modeling and estimations of a spectral energy distribution and a light curve. Its flexib

关键词： Gamma rays

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Author Correction: Inferring structural variant cancer cell fraction

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Nature communications 2022年第1期13卷 7568页

作者： Marek Cmero Ke Yuan Cheng Soon Ong Jan Schröder Niall M Corcoran Tony Papenfuss Christopher M Hovens Florian Markowetz Geoff Macintyre Department of Surgery Division of Urology Royal Melbourne Hospital and University of Melbourne Parkville VIC 3050 Australia. cmerom@unimelb.edu.au. The Epworth Prostate Centre Epworth Hospital Richmond VIC 3121 Australia. cmerom@unimelb.edu.au. Department of Computing and Information Systems University of Melbourne Parkville VIC 3010 Australia. cmerom@unimelb.edu.au. Bioinformatics Division The Walter and Eliza Hall Institute of Medical Research Parkville VIC Australia. cmerom@unimelb.edu.au. Murdoch Children's Research Institute Parkville VIC 3052 Australia. cmerom@unimelb.edu.au. School of Computing Science University of Glasgow Sir Alwyn Williams Building Glasgow G12 8RZ UK. Cancer Research UK Cambridge Institute University of Cambridge Cambridge CB2 0RE UK. Electrical and Electronic Engineering University of Melbourne Parkville VIC 3010 Australia. Machine Learning Research Group Data61 Canberra ACT 2601 Australia. Research School of Computer Science Australian National University Canberra ACT 2601 Australia. Bioinformatics Division The Walter and Eliza Hall Institute of Medical Research Parkville VIC Australia. Department of Surgery Division of Urology Royal Melbourne Hospital and University of Melbourne Parkville VIC 3050 Australia. The Epworth Prostate Centre Epworth Hospital Richmond VIC 3121 Australia. Department of Computing and Information Systems University of Melbourne Parkville VIC 3010 Australia. geoff.macintyre@cruk.cam.ac.uk. Cancer Research UK Cambridge Institute University of Cambridge Cambridge CB2 0RE UK. geoff.macintyre@cruk.cam.ac.uk.

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Abstracts

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JOURNAL OF SPINAL CORD MEDICINE 2021年第SUPPL 1期44卷 S278-S324页

作者： [Anonymous] Department of Medicine Division of Physical Medicine and Rehabilitation University of Toronto Toronto ON CA KITE - Toronto Rehabilitation Institute University Health Network Toronto ON CA Institute for Health Policy Management and Evaluation University of Toronto Toronto ON CA Rehabilitation Sciences Institute University of Toronto Toronto ON CA Institute of Medical Sciences University of Toronto Toronto ON CA. Rehabilitation Sciences Institute University of Toronto ON CA KITE Research Institute-Toronto Rehab-University Health Network Toronto ON CA Physical Therapy Department University of Toronto ON CA Praxis Spinal Cord Institute Vancouver BC Université de Montréal (École de réadaptation) Montreal QC CA Institut de réadaptation Gingras-Linsay-de-Montréal Montreal QC CA Division of Physical Medicine & Rehabilitation Department of Medicine Faculty of Medicine & Dentistry University of Alberta Edmonton AB CA University of Calgary Calgary AB CA Alberta Health Services Centre for Family Medicine Kitchener ON CA McMaster University Department of Family Medicine Hamilton ON CA Dept. of Physical Therapy University of Toronto Toronto ON CA KITE-University Health Network Toronto ON CA Praxis Spinal Cord Institute Vancouver BC CA Center for Interdisciplinary Research in Rehabilitation and Social Integration (CIRRIS) Quebec City QC CA Dept. of Rehabilitation Laval University Quebec City QC CA Institute of Health Policy Management and Evaluation University of Toronto Toronto ON CA School of Rehabilitation Science University of Saskatchewan Saskatoon SK CA Ontario Neurotrauma Foundation Toronto ON CA Canadian Spinal Research Organization Toronto ON CA CIUSSS du Centre-Sud-de-l ‘Île-de-Montréal (Installation Gingras-Lindsay) Montréal QC CA École de réadaptation Université de Montréal Montréal QC CA First Steps Wellness Centre Regina SK CA Stan Cassidy Centre for Rehabilitation Horizon Health Network Fredericton NB CA Departmen

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Cribriform pattern detection in prostate histopathological images using deep learning models

arXiv

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arXiv 2019年

作者： Singh, Malay Kalaw, Emarene Mationg Jie, Wang Al-Shabi, Mundher Wong, Chin Fong Giron, Danilo Medina Chong, Kian-Tai Tan, Maxine Zeng, Zeng Lee, Hwee Kuan Unit Institute of Molecular and Cell Biology Singapore Imaging Informatics Division Bioinformatics Institute Singapore Department of Computer Science School of Computing National University of Singapore Singapore UQ Centre for Clinical Research University of Queensland Brisbane Australia Distributed Analytics Lab Institute for Infocomm Research Singapore School of Engineering Monash University Malaysia Selangor Darul Ehsan Malaysia Department of Pathology Tan Tock Seng Hospital Singapore PanAsia Surgery Pte Ltd Mount Elizabeth Novena Hospital Singapore Surgi-TEN Specialists Pte Ltd Farrer Park Hospital Singapore Singapore Singapore Eye Research Institute Singapore

Architecture, size, and shape of glands are most important patterns used by pathologists for assessment of cancer malignancy in prostate histopathological tissue slides. Varying structures of glands along with cumbersome manual observations may result in subjective and inconsistent assessment. Cribriform gland with irregular border is an important feature in Gleason pattern 4. We propose using deep neural networks for cribriform pattern classification in prostate histopathological images. 163708 Hematoxylin and Eosin (H&E) stained images were extracted from histopathologic tissue slides of 19 patients with prostate cancer and annotated for cribriform patterns. Our automated image classification system analyses the H&E images to classify them as either 'Cribriform' or 'Non-cribriform'. Our system uses various deep learning approaches and hand-crafted image pixel intensity-based features. We present our results for cribriform pattern detection across various parameters and configuration allowed by our system. The combination of fine-tuned deep learning models outperformed the state-of-art nuclei feature based methods. Our image classification system achieved the testing accuracy of 85.93 ± 7.54 (cross-validated) and 88.04 ± 5.63 (additional unseen test set) across three folds. In this paper, we present an annotated cribriform dataset along with analysis of deep learning models and hand-crafted features for cribriform pattern detection in prostate histopathological images. Copyright © 2019, The Authors. All rights reserved.

关键词： Urology

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A Direct Proof of Improved Biased Random Walk with Gastric Cancer Dataset

A Direct Proof of Improved Biased Random Walk with Gastric C...

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International Conference on Applied Mathematics & computer science

作者： Choon Sen Seah Shahreen Kasim Mohd Farhan Md Fudzee Mohd Saberi Mohamad Rd Rohmat Saedudin Deden Witarsyah Rohayanti Hassan Mohd Arfian Ismail Rodziah Atan Soft Computing and Data Mining Centre Faculty of Computer Sciences and Information Technology Universiti Tun Hussein Onn Malaysia Faculty of Creative Technology and Heritage Universiti Malaysia Kelantan School of Industrial Engineering Telkom University Laboratory of Biodiversity and Bioinformatics Universiti Teknologi Malaysia Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Department of Software Engineering & Information System Faculty of Computer Science and Information Technology University Putra Malaysia (UPM)

ISBN: (数字)9781728132587

ISBN: (纸本)9781728132594

The relationship between the weight of a single genes and the connection between the genes had been studied. Chemistry and physical sciences have proved the attraction between molecules. Molecules are attracted to each other by bond. This paper is presented to disclose the relationship between weight and connectivity of nodes with biased random walk. An equation of biased random walk which named as significant directed random walk is formed to enhance the connectivity of nodes in directed graph via weigh. To be completely biased to the random walk, references data is implement as directed graph. Weight of genes will be used as one of the parameter in the formula. While the adjacency matrix is further enhanced by Warshall's algorithm to increases the accessibility of nodes via vector. The evolution of random walk is disclosed in this paper as well. Significant directed random walk will be used to prove the importance of weight in this paper. Comparison of the result between biased random walk is presented to prove the enhancement of random walk.

关键词： Biased random walk Cancer classification Gene expression dataset Random walk cancer classification Biased Ganglia one-parameter Mentoring Physical science

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FAIR Data Pipeline: provenance-driven data management for traceable scientific workflows

arXiv

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arXiv 2021年

作者： Mitchell, Sonia Natalie Lahiff, Andrew Cummings, Nathan Hollocombe, Jonathan Boskamp, Bram Field, Ryan Reddyhoff, Dennis Zarebski, Kristian Wilson, Antony Viola, Bruno Burke, Martin Archibald, Blair Bessell, Paul Blackwell, Richard Boden, Lisa A. Brett, Alys Brett, Sam Dundas, Ruth Enright, Jessica Gonzalez-Beltran, Alejandra N. Harris, Claire Hinder, Ian Hughes, Christopher David Knight, Martin Mano, Vino McMonagle, Ciaran Mellor, Dominic Mohr, Sibylle Marion, Glenn Matthews, Louise McKendrick, Iain J. Pooley, Christopher Mark Porphyre, Thibaud Reeves, Aaron Townsend, Edward Turner, Robert Walton, Jeremy Reeve, Richard Institute of Biodiversity Animal Health and Comparative Medicine College of Medical Veterinary and Life Sciences University of Glasgow GlasgowG12 8QQ United Kingdom Boyd Orr Centre for Population and Ecosystem Health University of Glasgow Glasgow United Kingdom United Kingdom Atomic Energy Authority Didcot OX14 3DB United Kingdom James Clerk Maxwell Building Peter Guthrie Tait Road The King’s Buildings EdinburghEH9 3FD United Kingdom MRC/CSO Social and Public Health Sciences Unit Institute of Health and Wellbeing College of Medical Veterinary and Life Sciences University of Glasgow GlasgowG12 8QQ United Kingdom Department of Computer Science University of Sheffield Regent Court SheffieldS1 4DP United Kingdom Science and Technology Facilities Council Harwell Campus United Kingdom School of Computing Science College of Science and Engineering University of Glasgow GlasgowG12 8QQ United Kingdom Roslin Institute University of Edinburgh Edinburgh United Kingdom Man Group plc Riverbank House 2 Swan Lane LondonEC4R 3AD United Kingdom The University of Manchester Research IT ManchesterM1 3BU United Kingdom School of Veterinary Medicine College of Medical Veterinary and Life Sciences University of Glasgow GlasgowG61 1QH United Kingdom VetAgro Sup UMR5558 Laboratoire de Biométrie et Biologie Évolutive Campus Vétérinaire de Lyon Marcy-l’Etoile 69280 France Peter Wilson Building The King’s Buildings West Mains Road EdinburghEH9 3JG United Kingdom UK Earth System Model Core Group Met Office ExeterEX1 3PB United Kingdom

Modern epidemiological analyses to understand and combat the spread of disease depend critically on access to, and use of, data. Rapidly evolving data, such as data streams changing during a disease outbreak, are particularly challenging. Data management is further complicated by data being imprecisely identified when used. Public trust in policy decisions resulting from such analyses is easily damaged and is often low, with cynicism arising where claims of "following the science" are made without accompanying evidence. Tracing the provenance of such decisions back through open software to primary data would clarify this evidence, enhancing the transparency of the decision-making process. Here, we demonstrate a Findable, Accessible, Interoperable and Reusable (FAIR) data pipeline. Although developed during the COVID-19 pandemic, it allows easy annotation of any data as they are consumed by analyses, or conversely traces the provenance of scientific outputs back through the analytical or modelling source code to primary data. Such a tool provides a mechanism for the public, and fellow scientists, to better assess scientific evidence by inspecting its provenance, while allowing scientists to support policy-makers in openly justifying their decisions. We believe that such tools should be promoted for use across all areas of policy-facing research. Copyright © 2021, The Authors. All rights reserved.

关键词： Information management

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Comparing recent PTA results on the nanohertz stochastic gravitational wave background

arXiv

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arXiv 2023年

作者： Agazie, G. Antoniadis, J. Anumarlapudi, A. Archibald, A.M. Arumugam, P. Arumugam, S. Arzoumanian, Z. Askew, J. Babak, S. Bagchi, M. Bailes, M. Bak Nielsen, A.-S. Baker, P.T. Bassa, C.G. Bathula, A. Bécsy, B. Berthereau, A. Bhat, N.D.R. Blecha, L. Bonetti, M. Bortolas, E. Brazier, A. Brook, P.R. Burgay, M. Burke-Spolaor, S. Burnette, R. Caballero, R.N. Cameron, A. Case, R. Chalumeau, A. Champion, D.J. Chanlaridis, S. Charisi, M. Chatterjee, S. Chatziioannou, K. Cheeseboro, B.D. Chen, S. Chen, Z.-C. Cognard, I. Cohen, T. Coles, W.A. Cordes, J.M. Cornish, N.J. Crawford, F. Cromartie, H.T. Crowter, K. Curylo, M. Cutler, C.J. Dai, S. Dandapat, S. Deb, D. DeCesar, M.E. DeGan, D. Demorest, P.B. Deng, H. Desai, S. Desvignes, G. Dey, L. Dhanda-Batra, N. Di Marco, V. Dolch, T. Drachler, B. Dwivedi, C. Ellis, J.A. Falxa, M. Feng, Y. Ferdman, R.D. Ferrara, E.C. Fiore, W. Fonseca, E. Franchini, A. Freedman, G.E. Gair, J.R. Garver-Daniels, N. Gentile, P.A. Gersbach, K.A. Glaser, J. Good, D.C. Goncharov, B. Gopakumar, A. Graikou, E. Grießmeier, J.-M. Guillemot, L. Gültekin, K. Guo, Y.J. Gupta, Y. Grunthal, K. Hazboun, J.S. Hisano, S. Hobbs, G.B. Hourihane, S. Hu, H. Iraci, F. Islo, K. Izquierdo-Villalba, D. Jang, J. Jawor, J. Janssen, G.H. Jennings, R.J. Jessner, A. Johnson, A.D. Jones, M.L. Joshi, B.C. Kaiser, A.R. Kaplan, D.L. Kapur, A. Kareem, F. Karuppusamy, R. Keane, E.F. Keith, M.J. Kelley, L.Z. Kerr, M. Key, J.S. Kharbanda, D. Kikunaga, T. Klein, T.C. Kolhe, N. Kramer, M. Krishnakumar, M.A. Kulkarni, A. Laal, N. Lackeos, K. Lam, M.T. Lamb, W.G. Larsen, B.B. Lazio, T.J.W. Lee, K.J. Levin, Y. Lewandowska, N. Littenberg, T.B. Liu, K. Liu, T. Liu, Y. Lommen, A. Lorimer, D.R. Lower, M.E. Luo, J. Luo, R. Lynch, R.S. Lyne, A.G. Ma, C.-P. Maan, Y. Madison, D.R. Main, R.A. Manchester, R.N. Mandow, R. Mattson, M.A. McEwen, A. McKee, J.W. McLaughlin, M.A. McMann, N. Meyers, B.W. Center for Gravitation Cosmology and Astrophysics Department of Physics University of Wisconsin-Milwaukee P.O. Box 413 MilwaukeeWI53201 United States Institute of Astrophysics FORTH N. Plastira 100 Heraklion70013 Greece Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 Bonn53121 Germany Newcastle University NE1 7RU United Kingdom Department of Physics Indian Institute of Technology Roorkee Roorkee247667 India Department of Electrical Engineering IIT Hyderabad Telangana Kandi502284 India X-Ray Astrophysics Laboratory NASA Goddard Space Flight Center Code 662 GreenbeltMD20771 United States Centre for Astrophysics and Supercomputing Swinburne University of Technology P.O. Box 218 HawthornVIC3122 Australia Australia Université Paris Cité CNRS Astroparticule et Cosmologie Paris75013 France The Institute of Mathematical Sciences C.I.T. Campus Taramani Chennai600113 India Homi Bhabha National Institute Training School Complex Anushakti Nagar Mumbai400094 India Fakultät für Physik Universität Bielefeld Postfach 100131 Bielefeld33501 Germany Department of Physics and Astronomy Widener University One University Place ChesterPA19013 United States ASTRON Netherlands Institute for Radio Astronomy Oude Hoogeveensedijk 4 Dwingeloo7991 PD Netherlands Department of Physical Sciences Indian Institute of Science Education and Research Punjab Mohali140306 India Department of Physics Oregon State University CorvallisOR97331 United States Laboratoire de Physique et Chimie de l'Environnement et de l'Espace Université d'Orléans CNRS Orléans45071 Cedex 02 France Observatoire Radioastronomique de Nançay Observatoire de Paris Université PSL Université d'Orléans CNRS Nançay18330 France International Centre for Radio Astronomy Research Curtin University BentleyWA6102 Australia Physics Department University of Florida GainesvilleFL32611 United States Dipartimento di Fisica "G. Occhialini" Universitá degli Studi di Milano-Bicocca

The Australian, Chinese, European, Indian, and North American pulsar timing array (PTA) collaborations recently reported, at varying levels, evidence for the presence of a nanohertz gravitational wave background (GWB). Given that each PTA made different choices in modeling their data, we perform a comparison of the GWB and individual pulsar noise parameters across the results reported from the PTAs that constitute the International Pulsar Timing Array (IPTA). We show that despite making different modeling choices, there is no significant difference in the GWB parameters that are measured by the different PTAs, agreeing within 1σ. The pulsar noise parameters are also consistent between different PTAs for the majority of the pulsars included in these analyses. We bridge the differences in modeling choices by adopting a standardized noise model for all pulsars and PTAs, finding that under this model there is a reduction in the tension in the pulsar noise parameters. As part of this reanalysis, we"extended" each PTA's data set by adding extra pulsars that were not timed by that PTA. Under these extensions, we find better constraints on the GWB amplitude and a higher signal-to-noise ratio for the Hellings and Downs correlations. These extensions serve as a prelude to the benefits offered by a full combination of data across all pulsars in the IPTA, i.e., the IPTA's Data Release 3, which will involve not just adding in additional pulsars, but also including data from all three PTAs where any given pulsar is timed by more than as single PTA. Copyright © 2023, The Authors. All rights reserved.

关键词： Pulsars

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