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arXiv 2024年

作者： Bisigello, L. Gandolfi, G. Feltre, A. Haro, Arrabal P. Calabrò, A. Cleri, N.J. Costantin, L. Girardi, G. Giulietti, M. Grazian, A. Gruppioni, C. Hathi, N.P. Holwerda, B.W. Llerena, M. Lucas, R.A. Pacucci, F. Prandoni, I. Rodighiero, G. Seillé, L.-M. Wilkins, S.M. Bagley, M. Dickinson, M. Finkelstein, S.L. Kartaltepe, J. Koekemoer, A.M. Papovich, C. Pirzkal, N. INAF Istituto di Radioastronomia Via Piero Gobetti 101 Bologna40129 Italy Dipartimento di Fisica e Astronomia "G. Galilei" Università di Padova Via Marzolo 8 Padova35131 Italy INAF-Osservatorio Astrofisico di Arcetri Largo E. Fermi 5 Firenze50125 Italy NSF’s National Optical-Infrared Astronomy Research Laboratory 950 N. Cherry Ave. TucsonAZ85719 United States Astrophysics Science Division NASA Goddard Space Flight Center 8800 Greenbelt Rd GreenbeltMD20771 United States INAF - Osservatorio Astronomico di Roma via di Frascati 33 Monte Porzio Catone00078 Italy Department of Astronomy and Astrophysics The Pennsylvania State University University ParkPA16802 United States Institute for Computational and Data Sciences The Pennsylvania State University University ParkPA16802 United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States CSIC-INTA Ctra de Ajalvir km 4 Torrejón de Ardoz Madrid28850 Spain INAF–Osservatorio Astronomico di Padova Vicolo dell’Osservatorio 5 PadovaI-35122 Italy INAF-Osservatorio di Astrofisica e Scienza dello Spazio via Gobetti 93/3 BolognaI-40129 Italy Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States Physics & Astronomy Department University of Louisville LouisvilleKY40292 United States Center for Astrophysics | Harvard & Smithsonian 60 Garden St CambridgeMA02138 United States Black Hole Initiative Harvard University 20 Garden St CambridgeMA02138 United States Aix Marseille Univ CNRS CNES LAM Marseille France Astronomy Centre University of Sussex Falmer BrightonBN1 9QH United Kingdom Institute of Space Sciences and Astronomy University of Malta MsidaMSD 2080 Malta Department of Astronomy The University of Texas at Austin AustinTX United States Cosmic Frontier Center The University of Texas at Austin AustinTX United States Laboratory for Multiwavelength Astrophysics School of Physics and Astro

We present the first spectroscopic confirmation of a dust-obscured dwarf galaxy, CEERS-14821. The analysis was performed by combining JWST NIRCam broadband photometry and NIRSpec/PRISM spectroscopic data. From the detection of multiple rest-frame optical lines, we derive that CEERS-14821 is located at z = 4.883 ± 0.003. Moreover, from a secure detection of the Hα and Hβ, we derive that the galaxy has a dust extinction ranging from AV = 2.2+−00.56 to AV = 3.4+−00.79, depending on the assumed reddening law. This value is extremely large given that we estimated a low stellar mass, that is, log10(M/M☉) = 8.17+−00.0504 or log10(M/M☉) = 8.65+−00.0605, based on two different dust extinction laws. Moreover, the combination of different metallicity tracers and the spectro-photometric fit suggests that the galaxy may also be metal-rich, with 12 + log10(O/H) > 8.3, but a low metallicity value cannot be totally ruled out. The high metallicity value would be above the expectation based on the mass-metallicity relation. Both metallicity estimations are above the expectations based on the fundamental mass-metallicity relation since CEERS-14821 is going through a burst of star formation. The constraints on a possible active galactic nucleus presence are limited and loose, but they point towards a possible non-dominant contribution (fAGN © 2024, CC BY.

关键词： Stars

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RUBIES: JWST/NIRSpec resolves evolutionary phases of dusty star-forming galaxies at z ∼ 2

arXiv

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arXiv 2024年

作者： Cooper, Olivia R. Brammer, Gabriel Heintz, Kasper E. Toft, Sune Casey, Caitlin M. Setton, David J. de Graaff, Anna Boogaard, Leindert Cleri, Nikko J. Gillman, Steven Gottumukkala, Rashmi Greene, Jenny E. Gullberg, Bitten Hirschmann, Michaela Hviding, Raphael E. Lambrides, Erini Leja, Joel Long, Arianna S. Manning, Sinclaire M. Maseda, Michael V. McConachie, Ian McKinney, Jed Narayanan, Desika Price, Sedona H. Strait, Victoria Weibel, Andrea Williams, Christina C. Department of Astronomy The University of Texas at Austin AustinTX United States Denmark Niels Bohr Institute University of Copenhagen Jagtvej 128 Copenhagen N2200 Denmark Department of Astronomy University of Geneva Chemin Pegasi 51 Versoix1290 Switzerland Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08544 United States Max-Planck-Institut für Astronomie Königstuhl 17 Heidelberg69117 Germany Leiden Observatory Leiden University PO Box 9513 LeidenNL-2300 RA Netherlands Department of Astronomy and Astrophysics The Pennsylvania State University University ParkPA16802 United States Institute for Computational and Data Sciences The Pennsylvania State University University ParkPA16802 United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States DTU-Space Technical University of Denmark Elektrovej 327 Kgs. LyngbyDK-2800 Denmark Institute of Physics Laboratory for Galaxy Evolution Ecole Polytechnique Federale de Lausanne Observatoire de Sauverny Chemin Pegasi 51 Versoix1290 Switzerland NASA-Goddard Space Flight Center Code 662 GreenbeltMD20771 United States Department of Astronomy The University of Washington SeattleWA United States Department of Astronomy University of Massachusetts Amherst 710 N Pleasant Street AmherstMA01003 United States Department of Astronomy University of Wisconsin-Madison MadisonWI53706 United States Department of Astronomy University of Florida 211 Bryant Space Sciences Center GainesvilleFL United States Department of Physics and Astronomy PITT PACC University of Pittsburgh PittsburghPA15260 United States NSF’s National Optical-Infrared Astronomy Research Laboratory 950 North Cherry Avenue TucsonAZ85719 United States Steward Observatory University of Arizona 933 North Cherry Avenue TucsonAZ85721 United States

The dearth of high quality spectroscopy of dusty star-forming galaxies (DSFGs) — the main drivers of the assembly of dust and stellar mass at the peak of activity in the Universe — greatly hinders our ability to interpret their physical processes and evolutionary pathways. We present JWST/NIRSpec observations from RUBIES of four submillimeter-selected, ALMA-detected DSFGs at cosmic noon, z ∼ 2.3 − 2.7. While photometry uniformly suggests vigorous ongoing star formation for the entire sample in line with canonical DSFGs, the spectra differ: one source has spectroscopic evidence of an evolved stellar population, indicating a recent transition to a post-starburst phase, while the remainder show strong spectroscopic signatures of ongoing starbursts. All four galaxies are infrared-luminous (log10 LIR/L☉ > 12.4), massive (log10 M★/M☉ > 11), and very dust-obscured (AV ∼ 3 − 4 ABmag). Leveraging detections of multiple Balmer and Paschen lines, we derive an optical attenuation curve consistent with Calzetti overall, yet an optical extinction ratio RV ∼ 2.5, potentially indicating smaller dust grains or differences in star-dust geometry. This case study provides some of the first detailed spectroscopic evidence that the DSFGs encompass a heterogeneous sample spanning a range of star formation properties and evolutionary stages, and illustrates the advantages of synergistic JWST and ALMA analysis of DSFGs. © 2024, CC BY.

关键词： Galaxies

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The UNCOVER Survey: A first-look HST+JWST catalog of 60, 000 galaxies near Abell 2744 and beyond

arXiv

引用

arXiv 2023年

作者： Weaver, John R. Cutler, Sam E. Pan, Richard Whitaker, Katherine E. Labbé, Ivo Price, Sedona H. Bezanson, Rachel Brammer, Gabriel Marchesini, Danilo Leja, Joel Wang, Bingjie Furtak, Lukas J. Zitrin, Adi Atek, Hakim Chemerynska, Iryna Coe, Dan Dayal, Pratika van Dokkum, Pieter Feldmann, Robert Förster Schreiber, Natascha M. Franx, Marijn Fujimoto, Seiji Fudamoto, Yoshinobu Glazebrook, Karl de Graaff, Anna Greene, Jenny E. Juneau, Stéphanie Kassin, Susan Kriek, Mariska Khullar, Gourav Maseda, Michael V. Mowla, Lamiya A. Muzzin, Adam Nanayakkara, Themiya Nelson, Erica J. Oesch, Pascal A. Pacifici, Camilla Papovich, Casey Setton, David J. Shapley, Alice E. Shipley, Heath V. Smit, Renske Stefanon, Mauro Taylor, Edward N. Weibel, Andrea Williams, Christina C. Department of Astronomy University of Massachusetts AmherstMA01003 United States Department of Physics and Astronomy Tufts University 574 Boston Ave. MedfordMA02155 United States Denmark Centre for Astrophysics and Supercomputing Swinburne University of Technology MelbourneVIC3122 Australia Department of Physics and Astronomy PITT PACC University of Pittsburgh PittsburghPA15260 United States Niels Bohr Institute University of Copenhagen Jagtvej 128 København NDK-2200 Denmark Department of Astronomy & Astrophysics The Pennsylvania State University University ParkPA16802 United States Institute for Computational & Data Sciences The Pennsylvania State University University ParkPA16802 United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States Physics Department Ben-Gurion University of the Negev P.O. Box 653 Beer-Sheva8410501 Israel Institut d’Astrophysique de Paris CNRS Sorbonne Université 98bis Boulevard Arago Paris75014 France 3700 San Martin Drive BaltimoreMD21218 United States United States Center for Astrophysical Sciences Department of Physics and Astronomy The Johns Hopkins University 3400 N Charles St. BaltimoreMD21218 United States Kapteyn Astronomical Institute University of Groningen P.O. Box 800 Groningen9700 AV Netherlands Department of Astronomy Yale University New HavenCT06511 United States Institute for Computational Science University of Zurich Winterhurerstrasse 190 ZurichCH-8057 Switzerland Max-Planck-Institut für extraterrestrische Physik Gießenbachstraße 1 Garching85748 Germany Leiden Observatory Leiden University P.O.Box 9513 LeidenNL-2300 AA Netherlands Department of Astronomy The University of Texas at Austin AustinTX78712 United States Waseda Research Institute for Science and Engineering Faculty of Science and Engineering Waseda University 3-4-1 Okubo Shinjuku Tokyo169-8555 Japan National Astronomical Observatory of Jap

In November 2022, the James Webb Space Telescope (JWST) returned deep near-infrared images of Abell 2744 – a powerful lensing cluster capable of magnifying distant, incipient galaxies beyond it. Together with the existing Hubble Space Telescope (HST) imaging, this publicly available dataset opens a fundamentally new discovery space to understand the remaining mysteries of the formation and evolution of galaxies across cosmic time. In this work, we detect and measure some 60 000 objects across the 49 arcmin2 JWST footprint down to a 5 σ limiting magnitude of ∼30 mag in 0.32′′ apertures. Photometry is performed using circular apertures on images matched to the point spread function of the reddest NIRCam broad band, F444W, and cleaned of bright cluster galaxies and the related intracluster light. To give an impression of the photometric performance, we measure photometric redshifts and achieve a σNMAD ≈ 0.03 based on known, but relatively small, spectroscopic samples. With this paper, we publicly release our HST and JWST PSF-matched photometric catalog with optimally assigned aperture sizes for easy use, along with single aperture catalogs, photometric redshifts, rest-frame colors, and individual magnification estimates. These catalogs will set the stage for efficient and deep spectroscopic follow-up of some of the first JWST-selected samples in Summer 2023. © 2023, CC BY.

关键词： Photometry

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Why is the Winner the Best?

Why is the Winner the Best?

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

引用

arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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OzDES multi-object fibre spectroscopy for the Dark Energy Survey: Results and second data release

arXiv

引用

arXiv 2020年

作者： Lidman, C. Tucker, B.E. Davis, T.M. Uddin, S.A. Asorey, J. Bolejko, K. Brout, D. Calcino, J. Carollo, D. Carr, A. Childress, M. Hoormann, J.K. Foley, R.J. Galbany, L. Glazebrook, K. Hinton, S.R. Kessler, R. Kim, A.G. King, A. Kremin, A. Kuehn, K. Lagattuta, D. Lewis, G.F. Macaulay, E. Malik, U. March, M. Martini, P. Möller, A. Mudd, D. Nichol, R.C. Panther, F. Parkinson, D. Pursiainen, M. Sako, M. Swann, E. Scalzo, R. Scolnic, D. Sharp, R. Smith, M. Sommer, N.E. Sullivan, M. Webb, S. Wiseman, P. Yu, Z. Yuan, F. Zhang, B. Abbott, T.M.C. Aguena, M. Allam, S. Annis, J. Avila, S. Bertin, E. Bhargava, S. Brooks, D. Carnero Rosell, A. Carrasco Kind, M. Carretero, J. Castander, F.J. Costanzi, M. da Costa, L.N. de Vicente, J. Doel, P. Eifler, T.F. Everett, S. Fosalba, P. Frieman, J. García-Bellido, J. Gaztanaga, E. Gruen, D. Gruendl, R.A. Gschwend, J. Gutierrez, G. Hartley, W.G. Hollowood, D.L. Honscheid, K. James, D.J. Kuropatkin, N. Li, T.S. Lima, M. Lin, H. Maia, M.A.G. Marshall, J.L. Melchior, P. Menanteau, F. Miquel, R. Palmese, A. Paz-Chinchón, F. Plazas, A.A. Roodman, A. Rykoff, E.S. Sanchez, E. Santiago, B. Scarpine, V. Schubnell, M. Serrano, S. Sevilla-Noarbe, I. Suchyta, E. Swanson, M.E.C. Tarle, G. Tucker, D.L. Varga, T.N. Walker, A.R. Wester, W. Wilkinson, R.D. Research School of Astronomy and Astrophysics Australian National University ACT2601 Australia School of Mathematics and Physics University of Queensland BrisbaneQLD4072 Australia Observatories of the Carnegie Institution for Science 813 Santa Barbara St. PasadenaCA91101 United States Madrid Spain School of Natural Sciences College of Sciences and Engineering University of Tasmania Private Bag 37 Hobart TAS7001 Australia NASA Einstein Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States INAF Astrophysical Observatory of Turin Pino TorineseI-10025 Italy School of Physics and Astronomy University of Southampton SouthamptonSO17 1BJ United Kingdom Santa Cruz Institute for Particle Physics Santa CruzCA95064 United States PITT PACC Department of Physics and Astronomy University of Pittsburgh PittsburghPA15260 United States Centre for Astrophysics & Supercomputing Swinburne University of Technology Victoria3122 Australia Department of Astronomy and Astrophysics University of Chicago ChicagoIL60637 United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States School of Physics University of Melbourne ParkvilleVIC3010 Australia Department of Physics University of Michigan Ann ArborMI48109 United States Australian Astronomical Optics Macquarie University North RydeNSW2113 Australia Lowell Observatory 1400 Mars Hill Rd FlagstaffAZ86001 United States Centre for Extragalactic Astronomy Department of Physics Durham University DurhamDH1 3LE United Kingdom Institute for Computational Cosmology Durham University South Road DurhamDH1 3LE United Kingdom Sydney Institute for Astronomy School of Physics University of Sydney A28 NSW2006 Australia Institute of Cosmology and Gravitation University of Portsmouth PortsmouthPO1 3FX United Kingdom Center for Cosmology

We present a description of the Australian Dark Energy Survey (OzDES) and summarise the results from its six years of operations. Using the 2dF fibre positioner and AAOmega spectrograph on the 3.9-m Anglo-Australian Telescope, OzDES has monitored 771 AGN, classified hundreds of supernovae, and obtained redshifts for thousands of galaxies that hosted a transient within the 10 deep fields of the Dark Energy Survey. We also present the second OzDES data release, containing the redshifts of almost 30,000 sources, some as faint as rAB = 24 mag, and 375,000 individual spectra. These data, in combination with the time-series photometry from the Dark Energy Survey, will be used to measure the expansion history of the Universe out to z ~ 1.2 and the masses of hundreds of black holes out to z ~ 4. OzDES is a template for future surveys that combine simultaneous monitoring of targets with wide-field imaging cameras and wide-field multi-object spectrographs. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

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A New Census of the 0.2 < z < 3.0 Universe, Part II: The Star-Forming Sequence

arXiv

引用

arXiv 2021年

作者： Leja, Joel Speagle, Joshua S. Ting, Yuan-Sen Johnson, Benjamin D. Conroy, Charlie Whitaker, Katherine E. Nelson, Erica J. van Dokkum, Pieter Franx, Marijn Department of Astronomy & Astrophysics The Pennsylvania State University University ParkPA16802 United States Institute for Computational & Data Sciences The Pennsylvania State University University ParkPA United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States Department of Statistical Sciences University of Toronto TorontoONM5S 3G3 Canada David A. Dunlap Department of Astronomy & Astrophysics University of Toronto TorontoONM5S 3H4 Canada Dunlap Institute for Astronomy & Astrophysics University of Toronto TorontoONM5S 3H4 Canada Research School of Astronomy & Astrophysics Australian National University Cotter Rd. Weston ACT2611 Australia Institute for Advanced Study PrincetonNJ08540 United States Department of Astrophysical Sciences Princeton University PrincetonNJ08540 United States Observatories of the Carnegie Institution of Washington 813 Santa Barbara Street PasadenaCA91101 United States Research School of Computer Science Australian National University ActonACT2601 Australia Center for Astrophysics | Harvard & Smithsonian 60 Garden St. CambridgeMA02138 United States Department of Astronomy University of Massachusetts Amherst 710 N Pleasant Street AmherstMA01003 United States Copenhagen Denmark Astrophysical & Planetary Sciences 391 UCB 2000 Colorado Ave Duane Physics Building Rm. E226 BoulderCO80309 United States Department of Astronomy Yale University New HavenCT06511 United States Leiden Observatory Leiden University LeidenNL-2300 RA Netherlands

We use the panchromatic SED-fitting code Prospector to measure the galaxy logM∗-logSFR relationship (the ‘star-forming sequence’) across 0.2 ∗-logSFR-redshift plane using a flexible neural network known as a normalizing flow. The resulting star-forming sequence has a low-mass slope near unity and a much flatter slope at higher masses, with a normalization 0.2 − 0.5 dex lower than typical inferences in the literature. We show this difference is due to the sophistication of the Prospector stellar populations modeling: the nonparametric star formation histories naturally produce higher masses while the combination of individualized metallicity, dust, and star formation history constraints produce lower star formation rates than typical UV+IR formulae. We introduce a simple formalism to understand the difference between SFRs inferred from spectral energy distribution fitting and standard template-based approaches such as UV+IR SFRs. Finally, we demonstrate the inferred star-forming sequence is consistent with predictions from theoretical models of galaxy formation, resolving a long-standing ∼ 0.2 − 0.5 dex offset with observations at 0.5 ∗, log SFR, z) is made available online a) to facilitate straightforward comparisons with future work. Copyright © 2021, The Authors. All rights reserved.

关键词： Stars

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Supermassive Black Hole Winds in X-rays: SUBWAYS. II. HST UV spectroscopy of winds at intermediate redshifts

arXiv

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arXiv 2022年

作者： Mehdipour, M. Kriss, G.A. Brusa, M. Matzeu, G.A. Gaspari, M. Kraemer, S.B. Mathur, S. Behar, E. Bianchi, S. Cappi, M. Chartas, G. Costantini, E. Cresci, G. Dadina, M. De Marco, B. De Rosa, A. Dunn, J.P. Gianolli, V.E. Giustini, M. Kaastra, J.S. King, A.R. Krongold, Y. La Franca, F. Lanzuisi, G. Longinotti, A.L. Luminari, A. Middei, R. Miniutti, G. Nardini, E. Perna, M. Petrucci, P.-O. Piconcelli, E. Ponti, G. Ricci, F. Tombesi, F. Ursini, F. Vignali, C. Zappacosta, L. Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States University of Bologna Via Gobetti 93/2 BolognaI-40129 Italy INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Gobetti 93/3 BolognaI-40129 Italy Villanueva de la Cañada MadridE-28691 Spain Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08544-1001 United States Department of Physics Institute for Astrophysics and Computational Sciences The Catholic University of America WashingtonDC20064 United States Department of Astronomy The Ohio State University 140 West 18th Avenue ColumbusOH43210 United States Center for Cosmology and Astroparticle Physics 191 West Woodruff Avenue ColumbusOH43210 United States Department of Physics Technion-Israel Institute of Technology Haifa32000 Israel Dipartimento di Matematica e Fisica Università degli Studi Roma Tre via della Vasca Navale 84 Roma00146 Italy Department of Physics and Astronomy College of Charleston CharlestonSC29424 United States SRON Netherlands Institute for Space Research Niels Bohrweg 4 Leiden2333 CA Netherlands Anton Pannekoek Institute University of Amsterdam Postbus 94249 Amsterdam1090 GE Netherlands INAF - Osservatorio Astrofisico di Arcetri Largo Enrico Fermi 5 FirenzeI-50125 Italy Departament de Física EEBE Universitat Politècnica de Catalunya Av. Eduard Maristany 16 BarcelonaE-08019 Spain via Fosso del Cavaliere RomaI-00133 Italy Department of Physics and Astronomy Georgia State University AtlantaGA30303 United States Université Grenoble Alpes CNRS IPAG Grenoble38000 France CSIC-INTA Camino Bajo del Castillo s/n Villanueva de la Cañada Madrid28692 Spain Leiden Observatory Leiden University PO Box 9513 Leiden2300 RA Netherlands School of Physics and Astronomy University of Leicester LeicesterLE1 7RH United Kingdom Instituto de Astronomía Universidad Nacional Autónoma de México Circuito Exterior Ciudad Universita

We present a UV spectroscopic study of ionized outflows in 21 active galactic nuclei (AGN), observed with the Hubble Space Telescope (HST). The targets of the Supermassive Black Hole Winds in X-rays (SUBWAYS) sample were selected with the aim to probe the parameter space of the underexplored AGN between the local Seyfert galaxies and the luminous quasars at high redshifts. Our targets, spanning redshifts of 0.1-0.4 and bolometric luminosities (Lbol) of 1045-1046 erg s−1, have been observed with a large multi-wavelength campaign using XMM-Newton, NuSTAR, and HST. Here, we model the UV spectra and look for different types of AGN outflows that may produce either narrow or broad UV absorption features. We examine the relations between the observed UV outflows and other properties of the AGN. We find that 60% of our targets show a presence of outflowing H i absorption, while 40% exhibit ionized outflows seen as absorption by either C iv, N v, or O vi. This is comparable to the occurrence of ionized outflows seen in the local Seyfert galaxies. All UV absorption lines in the sample are relatively narrow, with outflow velocities reaching up to −3300 km s−1. We did not detect any UV counterparts to the X-ray ultra-fast outflows (UFOs), most likely due to their being too highly ionized to produce significant UV absorption. However, all SUBWAYS targets with an X-ray UFO that have HST data demonstrate the presence of UV outflows at lower velocities. We find significant correlations between the column density (N) of the UV ions and Lbol of the AGN, with NH I decreasing with Lbol, while NO VI is increasing with Lbol. This is likely to be a photoionization effect, where toward higher AGN luminosities, the wind becomes more ionized, resulting in less absorption by neutral or low-ionization ions and more absorption by high-ionization ions. In addition, we find that N of the UV ions decreases as their outflow velocity increases. This may be explained by a mechanical power that is eva

关键词： Active Galactic Nuclei

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Learning from learning machines: A new generation of AI technology to meet the needs of science

arXiv

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arXiv 2021年

作者： Pion-Tonachini, Luca Bouchard, Kristofer Martin, Hector Garcia Peisert, Sean Holtz, W. Bradley Aswani, Anil Dwivedi, Dipankar Wainwright, Haruko Pilania, Ghanshyam Nachman, Benjamin Marrone, Babetta L. Falco, Nicola Prabhat Arnold, Daniel Wolf-Yadlin, Alejandro Powers, Sarah Climer, Sharlee Jackson, Quinn Carlson, Ty Sohn, Michael Zwart, Petrus Kumar, Neeraj Justice, Amy Tomlin, Claire Jacobson, Daniel Micklem, Gos Gkoutos, Georgios V. Bickel, Peter J. Cazier, Jean-Baptiste Müller, Juliane Webb-Robertson, Bobbie-Jo Stevens, Rick Anderson, Mark Kreutz-Delgado, Ken Mahoney, Michael W. Brown, James B. Pattern Computer Inc. Friday HarborWA98250 United States Biosciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Helen Wils Neuroscience Institute Redwood Center for Theoretical Neuroscience Uc Berkeley BerkeleyCA94720 United States Doe Agile BioFoundry Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Joint BioEnergy Institute Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Bcam Basque Center for Applied Mathematics Bilbao48009 Spain Computer Science University of California Davis DavisCA95616 United States Cenic La MiradaCA90638 United States Health Informatics University of California Davis School of Medicine SacramentoCA95817 United States Berkeley Institute for Data Science University of California Berkeley BerkeleyCA94720 United States Industrial Engineering and Operations Research University of California Berkeley BerkeleyCA94720 United States Environmental & Earth Sciences Area Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Nuclear Engineering University of California Berkeley BerkeleyCA94720 United States Materials Science and Technology Division Los Alamos National Laboratory Los AlamosNM87545 United States Physics Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Bioscience Division Los Alamos National Laboratory Los AlamosNM87545 United States Earth and Environmental Sciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Energy Technologies Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computing and Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Industrial & Systems Engineering The University of Tennessee KnoxvilleTN37996 United States Department of Computer Science University of Missouri-Saint Louis St. LouisMO63121 United

We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data. If we address the fundamental challenges associated with "bridging the gap" between domain-driven scientific models and data-driven AI learning machines, then we expect that these AI models can transform hypothesis generation, scientific discovery, and the scientific process itself. © 2021, CC BY.

关键词： Learning systems

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From data to Software to Science with the Rubin Observatory LSST

arXiv

引用

arXiv 2022年

作者： Breivik, Katelyn Connolly, Andrew J. Ford, K.E. Saavik Jurić, Mario Mandelbaum, Rachel Miller, Adam A. Norman, Dara Olsen, Knut O’Mullane, William Price-Whelan, Adrian Sacco, Timothy Sokoloski, J.L. Villar, Ashley Acquaviva, Viviana Ahumada, Tomas AlSayyad, Yusra Alves, Catarina S. Andreoni, Igor Anguita, Timo Best, Henry J. Bianco, Federica B. Bonito, Rosaria Bradshaw, Andrew Burke, Colin J. de Campos, Andresa Rodrigues Cantiello, Matteo Caplar, Neven Chandler, Colin Orion Chan, James da Costa, Luiz Nicolaci Danieli, Shany Davenport, James R.A. Fabbian, Giulio Fagin, Joshua Gagliano, Alexander Gall, Christa Camargo, Nicolás Garavito Gawiser, Eric Gezari, Suvi Gomboc, Andreja Gonzalez-Morales, Alma X. Graham, Matthew J. Gschwend, Julia Guy, Leanne P. Holman, Matthew J. Hsieh, Henry H. Hundertmark, Markus Ilić, Dragana Ishida, Emille E.O. Jurkić, Tomislav Kannawadi, Arun Kosakowski, Alekzander Kovačević, Andjelka B. Kubica, Jeremy Lanusse, François Lazar, Ilin Levine, W. Garrett Li, Xiaolong Lu, Jing Luna, Gerardo Juan Manuel Mahabal, Ashish A. Malz, Alex I. Mao, Yao-Yuan Medan, Ilija Moeyens, Joachim Nicolić, Mladen Nikutta, Robert O’Dowd, Matt Olsen, Charlotte Pearson, Sarah Pedraza, Ilhuiyolitzin Villicana Popinchalk, Mark Popović, Luka Č. Pritchard, Tyler A. Quint, Bruno C. Radović, Viktor Ragosta, Fabio Riccio, Gabriele Riley, Alexander H. Rożek, Agata Sánchez-Sáez, Paula Sarro, Luis M. Saunders, Clare Savić, Ðorđe V. Schmidt, Samuel Scott, Adam Shirley, Raphael Smotherman, Hayden R. Stetzler, Steven Storey-Fisher, Kate Street, Rachel A. Trilling, David E. Tsapras, Yiannis Ustamujic, Sabina van Velzen, Sjoert Vázquez-Mata, José Antonio Venuti, Laura Wyatt, Samuel Yu, Weixiang Zabludoff, Ann Center for Computational Astrophysics Flatiron Institute 162 Fifth Ave New YorkNY10010 United States Department of Astronomy The DIRAC Institute University of Washington 3910 15th Avenue NE SeattleWA98195 United States Department of Science CUNY Borough Manhattan Community College 199 Chambers Street New YorkNY10007 United States Department of Astrophysics American Museum of Natural History New YorkNY10028 United States The Graduate Center The City University of New York 365 Fifth Avenue New YorkNY10016 United States McWilliams Center for Cosmology Department of Physics Carnegie Mellon University PittsburghPA15213 United States Department of Physics and Astronomy Northwestern University 1800 Sherman Road EvanstonIL60201 United States National Optical-Infrared Astronomy Research Laboratory 950 N. Cherry Ave. TucsonAZ85719 United States Rubin Observatory Project Office 950 N. Cherry Ave. TucsonAZ85719 United States The LSST Corporation Columbia University 533 W. 218th St. New YorkNY10034 United States Department of Astronomy and Astrophysics The Pennsylvania State University 525 Davey Lab University ParkPA16802 United States Institute for Computational & Data Sciences The Pennsylvania State University University ParkPA16802 United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States City University of New York New York City College of Technology United States University of Maryland College ParkMD20742 United States Department of Astrophysical Sciences Princeton University PrincetonNJ08544 United States University College London Gower St LondonWC1E 6BT United Kingdom Joint Space-Science Institute University of Maryland College ParkMD20742 United States Department of Astronomy University of Maryland College ParkMD20742 United States Astrophysics Science Division NASA Goddard Space Flight Center Mail Code 661 GreenbeltMD20771 United States Departam

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) dataset will dramatically alter our understanding of the Universe, from the origins of the Solar System to the nature of dark matter and dark energy. Much of this research will depend on the existence of robust, tested, and scalable algorithms, software, and services. Identifying and developing such tools ahead of time has the potential to significantly accelerate the delivery of early science from LSST. Developing these collaboratively, and making them broadly available, can enable more inclusive and equitable collaboration on LSST science. To facilitate such opportunities, a community workshop entitled "From data to Software to Science with the Rubin Observatory LSST" was organized by the LSST Interdisciplinary Network for Collaboration and Computing (LINCC) and partners, and held at the Flatiron Institute in New York, March 28-30th 2022. The workshop included over 50 in-person attendees invited from over 300 applications. It identified seven key software areas of need: (i) scalable cross-matching and distributed joining of catalogs, (ii) robust photometric redshift determination, (iii) software for determination of selection functions, (iv) frameworks for scalable time-series analyses, (v) services for image access and reprocessing at scale, (vi) object image access (cutouts) and analysis at scale, and (vii) scalable job execution systems. This white paper summarizes the discussions of this workshop. It considers the motivating science use cases, identified cross-cutting algorithms, software, and services, their high-level technical specifications, and the principles of inclusive collaborations needed to develop them. We provide it as a useful roadmap of needs, as well as to spur action and collaboration between groups and individuals looking to develop reusable software for early LSST science. Copyright © 2022, The Authors. All rights reserved.

关键词： Software testing

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