25,810 research outputs found
DELVE-ing into the Jet: a thin stellar stream on a retrograde orbit at 30 kpc
No full textWe perform a detailed photometric and astrometric analysis of stars in the Jet stream using data from the first data release of the DECam Local Volume Exploration Survey (DELVE) DR1 and Gaia EDR3. We discover that the stream extends over ∼ 29 • on the sky (increasing the known length by 18 •), which is comparable to the kinematically cold Phoenix, ATLAS, and GD-1 streams. Using blue horizontal branch stars, we resolve a distance gradient along the Jet stream of 0.2 kpc/deg, with distances ranging from D ∼ 27−34 kpc. We use natural splines to simultaneously fit the stream track, width, and intensity to quantitatively characterize density variations in the Jet stream, including a large gap, and identify substructure off the main track of the stream. Furthermore, we report the first measurement of the proper motion of the Jet stream and find that it is well-aligned with the stream track suggesting the stream has likely not been significantly perturbed perpendicular to the line of sight. Corresponding author: Peter Ferguson [email protected] 2 DELVE Collaboration Finally, we fit the stream with a dynamical model and find that the stream is on a retrograde orbit, and is well fit by a gravitational potential including the Milky Way and Large Magellanic Cloud. These results indicate the Jet stream is an excellent candidate for future studies with deeper photometry, astrometry, and spectroscopy to study the potential of the Milky Way and probe perturbations from baryonic and dark matter substructure
DELVE-ing into the Jet: a thin stellar stream on a retrograde orbit at 30 kpc
No full textWe perform a detailed photometric and astrometric analysis of stars in the Jet stream using data from the first data release of the DECam Local Volume Exploration Survey (DELVE) DR1 and Gaia EDR3. We discover that the stream extends over ∼ 29 • on the sky (increasing the known length by 18 •), which is comparable to the kinematically cold Phoenix, ATLAS, and GD-1 streams. Using blue horizontal branch stars, we resolve a distance gradient along the Jet stream of 0.2 kpc/deg, with distances ranging from D ∼ 27−34 kpc. We use natural splines to simultaneously fit the stream track, width, and intensity to quantitatively characterize density variations in the Jet stream, including a large gap, and identify substructure off the main track of the stream. Furthermore, we report the first measurement of the proper motion of the Jet stream and find that it is well-aligned with the stream track suggesting the stream has likely not been significantly perturbed perpendicular to the line of sight. Corresponding author: Peter Ferguson [email protected] 2 DELVE Collaboration Finally, we fit the stream with a dynamical model and find that the stream is on a retrograde orbit, and is well fit by a gravitational potential including the Milky Way and Large Magellanic Cloud. These results indicate the Jet stream is an excellent candidate for future studies with deeper photometry, astrometry, and spectroscopy to study the potential of the Milky Way and probe perturbations from baryonic and dark matter substructure
The Structure of Scientific Collaboration Networks in Scientometrics
No full textThe structure of scientific collaboration networks in scientometrics was investigated at the level of individuals by using bibliographic data of all papers published in the international journal Scientometrics retrieved from the Science Citation Index (SCI) during 1978 to 2004. Combined analysis of social network analysis (SNA), co-occurrence analysis, cluster analysis and frequency analysis of words was explored to reveal: (1) The microstructure of the collaboration network on scientists’ aspects of scientometrics; (2) The major collaborative fields of the collaborative sub-networks; (3) The collaborative center of the collaboration network in scientometrics
Collaboration in Iranian Scientific Publications
No full textThis study looks at international collaboration in Iranian scientific publications through the ISI Science Citation Index® (SCI) for the years 1995-1999, inclusive. These results are compared to and contrasted with the earlier findings for the periods covering 1985-1994 (Osareh & Wilson 2000). The results of Iran's increasing productivity over a 15-year period are presented. Iran doubled its output in the first two five-year periods and increased 2.8-fold from the second to the third five-year period. The rise in Iran's scientific publication output is due mainly to factors such as the ending of the war, better economic conditions, recent changes in the Iranian government's policy, basic changes in the political environment brought about by the Reformers, expansion of the Iranian presses for national publications, and the recent return of a large number of students trained overseas through government scholarships. External changes also account for the increased productivity, e.g., the acceptance of three Iranian source journals by the SCI, increased access to international databases through the Internet and better electronic communication facilities for international collaboration. One of the most important and significant factors that caused this dramatic rise seems to be the government's research policies in the last few years. Since 1999, the Iran Science, Research and Technology Ministry, has encouraged researchers to publish their non-Farsi language articles in highly ranked international scientific journals, for example, by giving prizes to researchers who publish their articles in ISI-ranked journals
The methodological status of co-authorship networks
No full textA powerful strategy within the study of collaboration
in science is to posit that co-authorship patterns
represent social networks.
It is prerequisite to an application of Social
Network Analysis (SNA) to define the network
entities. A network analysis of the inter-institutional
collaboration in COLLNET on the basis
of co-authorships was conducted. The study reveals
that it is crucial whether the co-authorship
itself is seen as an author's relational property or
as a social event that brings the authors together.
The former possibility is represented by a onemode
network in which each author can be related
to each other author. Quite distinct from
that are two-mode networks, the latter approach.
They consist of two single data sets in which relations
are only possible between different sets.
Different modes of representations require
different network approaches. One is that co-authorship
networks are seen as one-mode networks,
which has the advantage of the application
of a variety of measures. In contrast, twomode
networks, the other option, cannot be analysed
by standard techniques but its distinctive
features demand a new conceptualisation of
measures. In conclusion, the two-mode perspective
is more promising because it allows a dual
perspective on collaboration in science which includes
researchers as well as their scientific output
Co-authorship Network of Scientometrics Research Collaboration
No full textThis paper examines the co-authorship network in the field of scientometrics using social network analysis techniques with the aim of developing an understanding of research collaboration in this scientific community. Using co-authorship data from 3125 articles published in the journal Scientometrics with a time span of more than three decades (1980-2012), we construct an evolving co-authorship network and calculate three centrality measures (closeness, betweenness, and degree) for 3024 authors, 1207 institutions, 68 countries and 22 academic fields in this network. This paper also discusses the usability of centrality measures in author ranking, and suggests that centrality measures can be useful indicators for impact analysis. Findings revealed that scientometrics was not dominated by a couple of key researchers as quite a significant number of popular researchers were identified. The United States occupies the topmost position in all measures except for degree centrality. The most active, central and collaborative academic discipline in scientometrics is Information & Library Science
The DECam Local Volume Exploration Survey Data Release 2
No full textWe present the second public data release (DR2) from the DECam Local Volume Exploration survey (DELVE). DELVE DR2 combines new DECam observations with archival DECam data from the Dark Energy Survey, the DECam Legacy Survey, and other DECam community programs. DELVE DR2 consists of ∼160,000 exposures that cover >21,000 deg2 of the high-Galactic-latitude (∣b∣ > 10°) sky in four broadband optical/near-infrared filters (g, r, i, z). DELVE DR2 provides point-source and automatic aperture photometry for ∼2.5 billion astronomical sources with a median 5σ point-source depth of g = 24.3, r = 23.9, i = 23.5, and z = 22.8 mag. A region of ∼17,000 deg2 has been imaged in all four filters, providing four-band photometric measurements for ∼618 million astronomical sources. DELVE DR2 covers more than 4 times the area of the previous DELVE data release and contains roughly 5 times as many astronomical objects. DELVE DR2 is publicly available via the NOIRLab Astro Data Lab science platform
Measuring author influence in scientific collaboration networks
No full text
Purpose: The purpose of this paper is to measure author influence in scientific collaboration networks by considering the combined effects of multiple indexes. In the meanwhile, we intend to explore a method to avoid assigning subjective weights.
Design/methodology/approach: We applied four centrality measures (degree centrality, betweenness centrality, closeness centrality and eigenvector centrality) and authors' published papers to the scientific collaboration network. The grey relational analysis (GRA) method based on information entropy was used to measure an author's impact in the collaboration network. The weight of each evaluation index was determined based on information entropy. The ACM SIGKDD collaboration network was selected as an example to demonstrate the practicality and effectiveness of our method.
Findings: Author influence was not always positively correlated with evaluation indexes such as degree centrality and betweenness centrality. This implies that combined effects of multiple indexes should be considered in author impact analysis. The introduction of the GRA method based on information entropy can reduce the interference of human factors in the evaluation process.
Research limitations: We only analyzed author influence from the perspective of scientific collaboration, but the impact of citation on author influence was ignored.
Practical implications: The proposed method can be also applied to detect influential authors in bibliographic co-citation network, author co-citation network, bibliographic coupling network or author coupling network. It would help facilitate scientific collaboration and enhance scholarly communication.
Originality/value: This paper proposes an analytical method of evaluating author influence in scientific collaboration networks, in which combined effects of multiple indexes are considered and the interference of human factors is reduced in the evaluation process.
Purpose: The purpose of this paper is to measure author influence in scientific collaboration networks by considering the combined effects of multiple indexes. In the meanwhile, we intend to explore a method to avoid assigning subjective weights.
Design/methodology/approach: We applied four centrality measures (degree centrality, betweenness centrality, closeness centrality and eigenvector centrality) and authors' published papers to the scientific collaboration network. The grey relational analysis (GRA) method based on information entropy was used to measure an author's impact in the collaboration network. The weight of each evaluation index was determined based on information entropy. The ACM SIGKDD collaboration network was selected as an example to demonstrate the practicality and effectiveness of our method.
Findings: Author influence was not always positively correlated with evaluation indexes such as degree centrality and betweenness centrality. This implies that combined effects of multiple indexes should be considered in author impact analysis. The introduction of the GRA method based on information entropy can reduce the interference of human factors in the evaluation process.
Research limitations: We only analyzed author influence from the perspective of scientific collaboration, but the impact of citation on author influence was ignored.
Practical implications: The proposed method can be also applied to detect influential authors in bibliographic co-citation network, author co-citation network, bibliographic coupling network or author coupling network. It would help facilitate scientific collaboration and enhance scholarly communication.
Originality/value: This paper proposes an analytical method of evaluating author influence in scientific collaboration networks, in which combined effects of multiple indexes are considered and the interference of human factors is reduced in the evaluation process.</div
Six More Ultra-faint Milky Way Companions Discovered in the DECam Local Volume Exploration Survey
No full textFull list of the authors: Cerny, W.; Martínez-Vázquez, C. E.; Drlica-Wagner, A.; Pace, A. B.; Mutlu-Pakdil, B.; Li, T. S.; Riley, A. H.; Crnojević, D.; Bom, C. R.; Carballo-Bello, J. A.; Carlin, J. L.; Chiti, A.; Choi, Y.; Collins, M. L. M.; Darragh-Ford, E.; Ferguson, P. S.; Geha, M.; Martínez-Delgado, D.; Massana, P.; Mau, S.; Medina, G. E.; Muñoz, R. R.; Nadler, E. O.; Noël, N. E. D.; Olsen, K. A. G.; Pieres, A.; Sakowska, J. D.; Simon, J. D.; Stringfellow, G. S.; Tollerud, E. J.; Vivas, A. K.; Walker, A. R.; Wechsler, R. H.; Delve CollaborationWe report the discovery of six ultra-faint Milky Way satellites identified through matched-filter searches conducted using Dark Energy Camera (DECam) data processed as part of the second data release of the DECam Local Volume Exploration (DELVE) survey. Leveraging deep Gemini/GMOS-N imaging (for four candidates) as well as follow-up DECam imaging (for two candidates), we characterize the morphologies and stellar populations of these systems. We find that these candidates all share faint absolute magnitudes (M ≥ −3.2 mag) and old, metal-poor stellar populations (τ > 10 Gyr, [Fe/H] 15 pc), while the other three are compact (r < 10 pc). From these properties, we infer that the former three systems (Boötes V, Leo Minor I, and Virgo II) are consistent with ultra-faint dwarf galaxy classifications, whereas the latter three (DELVE 3, DELVE 4, and DELVE 5) are likely ultra-faint star clusters. Using data from the Gaia satellite, we confidently measure the proper motion of Boötes V, Leo Minor I, and DELVE 4, and tentatively detect a proper-motion signal from DELVE 3 and DELVE 5; no signal is detected for Virgo II. We use these measurements to explore possible associations between the newly discovered systems and the Sagittarius dwarf spheroidal, the Magellanic Clouds, and the Vast Polar Structure, finding several plausible associations. Our results offer a preview of the numerous ultra-faint stellar systems that will soon be discovered by the Vera C. Rubin Observatory and highlight the challenges of classifying the faintest stellar systems. © 2023. The Author(s). Published by the American Astronomical Society.We thank the anonymous referee for providing specific and constructive feedback that has improved this work. It is a pleasure to thank Simon Smith, Alan McConnachie, and the UNIONS collaboration for pleasant and productive discussions during which we coordinated the submission of our independent manuscripts reporting the discovery of Booetes V. We thank the staff of Gemini Observatory North and Cerro Tololo Inter-American Observatory for their support in the execution of our observations, and we are grateful to the directors of each observatory for granting our requests for Director's Discretionary time to study some of the candidates presented here. This project is partially supported by the NASA Fermi Guest Investigator Program Cycle 9 No. 91201. This work is partially supported by Fermilab LDRD project L2019-011. W.C. gratefully acknowledges support from a Gruber Science Fellowship at Yale University. A.B.P. acknowledges support from NSF grant AST-1813881. A.H.R. acknowledges support from an NSF Graduate Research Fellowship through grant DGE-1746932 and a Research Fellowship from the Royal Commission for the Exhibition of 1851. R.R.M. gratefully acknowledges support by the ANID BASAL project FB210003 and ANID Fondecyt project 1221695.This work was enabled in part by observations made from the Gemini North telescope, located within the Maunakea Science Reserve and adjacent to the summit of Maunakea. We are grateful for the privilege of observing the universe from a place that is unique in both its astronomical quality and its cultural significance. This work is based in part on observations obtained at the international Gemini Observatory, a program of NSFs NOIRLab (processed using DRAGONS (Data Reduction for Astronomy from Gemini Observatory North and South), which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation on behalf of the Gemini Observatory partnership: the National Science Foundation (United States), National Research Council (Canada), Agencia Nacional de Investigacion y Desarrollo (Chile), Ministerio de Ciencia, Tecnologia e Innovacion (Argentina), Ministerio da Ciencia, Tecnologia, Inovacoes e Comunicacoes (Brazil), and Korea Astronomy and Space Science Institute (Republic of Korea). C.E.M-V thanks Kathleen Labrie and Chris Simpson for useful advice regarding the GMOS DRAGONS data reduction. This project used data obtained with the Dark Energy Camera (DECam), which was constructed by the Dark Energy Survey (DES) collaboration. Funding for the DES Projects has been provided by the DOE and NSF (USA), MISE (Spain), STFC (UK), HEFCE (UK), NCSA (UIUC), KICP (U. Chicago), CCAPP (Ohio State), MIFPA (Texas A & M University), CNPQ, FAPERJ, FINEP (Brazil), MINECO (Spain), DFG (Germany), and the collaborating institutions in the Dark Energy Survey, which are Argonne Lab, UC Santa Cruz, University of Cambridge, CIEMAT-Madrid, University of Chicago, University College London, DES-Brazil Consortium, University of Edinburgh, ETH Zuerich, Fermilab, University of Illinois, ICE (IEEC-CSIC), IFAE Barcelona, Lawrence Berkeley Lab, LMU Muenchen, and the associated Excellence Cluster Universe, University of Michigan, NSF's National Optical-Infrared Astronomy Research Laboratory, University of Nottingham, Ohio State University, OzDES Membership Consortium University of Pennsylvania, University of Portsmouth, SLAC National Lab, Stanford University, University of Sussex, and Texas A & M University.
This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. Based on observations at Cerro Tololo Inter-American Observatory, NSF's National Optical-Infrared Astronomy Research Laboratory (2019A-0305; PI: Drlica-Wagner), which is operated by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. This manuscript has been authored by Fermi Research Alliance, LLC, under contract No. DE-AC02-07CH11359 with the US Department of Energy, Office of Science, Office of High Energy Physics. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Legacy Surveys consist of three individual and complementary projects: the Dark Energy Camera Legacy Survey (DECaLS; Proposal ID 2014B-0404; PIs: David Schlegel and Arjun Dey), the Beijing-Arizona Sky Survey (BASS; NOAO Prop. ID 2015A-0801; PIs: Zhou Xu and Xiaohui Fan), and the Mayall z-band Legacy Survey (MzLS; Prop. ID 2016A-0453; PI: Arjun Dey). DECaLS, BASS, and MzLS together include data obtained, respectively, at the Blanco telescope, Cerro Tololo Inter-American Observatory, NSFs NOIRLab; the Bok telescope, Steward Observatory, University of Arizona; and the Mayall telescope, Kitt Peak National Observatory, NOIRLab. Pipeline processing and analyses of the data were supported by NOIRLab and the Lawrence Berkeley National Laboratory (LBNL). The Legacy Surveys project is honored to be permitted to conduct astronomical research on Iolkam Duag (Kitt Peak), a mountain with particular significance to the Tohono Oodham Nation. NOIRLab is operated by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. LBNL is managed by the Regents of the University of California under contract to the U.S. Department of Energy. This project used data obtained with the Dark Energy Camera (DECam), which was constructed by the Dark Energy Survey (DES) collaboration. Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S.
National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A & M University, Financiadora de Estudos e Projetos, Fundacao Carlos Chagas Filho de Amparo, Financiadora de Estudos e Projetos, Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Cientifico e Tecnologico and the Ministerio da Ciencia, Tecnologia e Inovacao, the Deutsche Forschungsgemeinschaft and the Collaborating Institutions in the Dark Energy Survey. The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Energeticas, Medioambientales y Tecnologicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgenossische Technische Hochschule (ETH) Zurich, Fermi National Accelerator Laboratory, the University of Illinois at Urbana-Champaign, the Institut de Ciencies de lEspai (IEEC/CSIC), the Institut de Fisica dAltes Energies, Lawrence Berkeley National Laboratory, the Ludwig Maximilians Universitat Munchen and the associated Excellence Cluster Universe, the University of Michigan, NSFs NOIRLab, the University of Nottingham, the Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, and Texas A & M University. BASS is a key project of the Telescope Access Program (TAP), which has been funded by the National Astronomical Observatories of China, the Chinese Academy of Sciences (the Strategic Priority Research Program The Emergence of Cosmological Structures grant No. XDB09000000), and the Special Fund for Astronomy from the Ministry of Finance. BASS is also supported by the External Cooperation Program of Chinese Academy of Sciences (grant No. 114A11KYSB20160057), and Chinese National Natural Science Foundation (grant Nos. 12120101003 and 11433005).The Legacy Survey team makes use of data products from the Near-Earth Object Wide-field Infrared Survey Explorer (NEOWISE), which is a project of the Jet Propulsion Laboratory/California Institute of Technology. NEOWISE is funded by the National Aeronautics and Space Administration. The Legacy Surveys imaging of the DESI footprint is supported by the Director, Office of Science, Office of High Energy Physics of the U.S. Department of Energy under contract No. DE-AC02-05CH1123, by the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility under the same contract; and by the U.S. National Science Foundation, Division of Astronomical Sciences under contract No. AST-0950945 to NOAO. Facilities: Blanco (DECam), Gemini:Gillett (GMOS-N), Gaia, Astro Data Lab, Astro Data Archive. Software: astropy (Astropy Collaboration et al. 2013; Price-Whelan et al. 2018), emcee (Foreman-Mackey et al. 2013), fitsio, 42 42 https://github.com/esheldon/fitsio HEALPix (Gorski et al. 2005), 43 43 http://healpix.sourceforge.net healpy (Zonca et al. 2019), 44 44 https://github.
com/healpy/healpy Matplotlib (Hunter 2007), numpy (van der Walt et al. 2011), scipy (Jones et al. 2001), ugali (Bechtol et al. 2015) 45 45 https://github.com/DarkEnergySurvey/ugali , gala (Price-Whelan 2017
Inter-institutional scientific collaboration: an approach from social network
No full textThis paper presents a tool that can be used to characterize, analyze and interpret the
patterns of collaboration among institutions by means of the visual display of scientific
information. These graphic representations allow for a combined analysis of a given
institution in the system of relations (network), and of the particular attributes of that
institution (indicators). The tool affords the possibility of regenerating the network to
make any number of aggregates appear or disappear, thus allowing one to focus on
institutional sectors, geographic regions, etc. It also allows for analysis of sectorial
interaction, institutional backing of research, and the influence of geographic proximity,
linguistic affinity, or regional politics. This is indeed a versatile analytical tool, and it is
bound to prove its potential for evaluating patterns of collaborative research, development
and innovation
- …
