275,841 research outputs found

    Adaptive clustering procedure for continuous gravitational wave searches

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    In hierarchical searches for continuous gravitational waves, clustering of candidates is an important postprocessing step because it reduces the number of noise candidates that are followed-up at successive stages [1][7][12]. Previous clustering procedures bundled together nearby candidates ascribing them to the same root cause (be it a signal or a disturbance), based on a predefined cluster volume. In this paper, we present a procedure that adapts the cluster volume to the data itself and checks for consistency of such volume with what is expected from a signal. This significantly improves the noise rejection capabilities at fixed detection threshold, and at fixed computing resources for the follow-up stages, this results in an overall more sensitive search. This new procedure was employed in the first Einstein@Home search on data from the first science run of the advanced LIGO detectors (O1) [11].Comment: 11 pages, 9 figures, 2 tables; v1: initial submission; v2: journal review, copyedited version; v3: fixed typo in Fig

    Global survey of star clusters in the Milky Way: III. 139 new open clusters at high Galactic latitudes

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    Context. An earlier analysis of the Milky Way Star Cluster (MWSC) catalogue revealed an apparent lack of old (t � 1 Gyr) open clusters in the solar neighbourhood (d � 1 kpc). Aims. To fill this gap we undertook a search for hitherto unknown star clusters, assuming that the missing old clusters reside at high Galactic latitudes | b | > 20°. Methods. We were looking for stellar density enhancements using a star count algorithm on the 2MASS point source catalogue. To increase the contrast between potential clusters and the field, we applied filters in colour-magnitude space according to typical colour-magnitude diagrams of nearby old open clusters. The subsequent comparison with lists of known objects allowed us to select thus far unknown cluster candidates. For verification they were processed with the standard pipeline used within the MWSC survey for computing cluster membership probabilities and for determining structural, kinematic, and astrophysical parameters. Results. In total we discovered 782 density enhancements, 524 of which were classified as real objects. Among them 139 are new open clusters with ages 8.3 < log (t [yr]) < 9.7, distances d< 3 kpc, and distances from the Galactic plane 0.3 <Z< 1 kpc. This new sample has increased the total number of known high latitude open clusters by about 150%. Nevertheless, we still observe a lack of older nearby clusters up to 1 kpc from the Sun. This volume is expected to still contain about 60 unknown clusters that probably escaped our detection algorithm, which fails to detect sparse overdensities with large angular size

    A Deep Halpha Survey of Galaxies in the Two Nearby Clusters Abell1367 and Coma: The Halpha Luminosity Functions

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    We present a deep wide field Halpha imaging survey of the central regions of the two nearby clusters of galaxies Coma and Abell1367, taken with the WFC at the INT2.5m telescope. We determine for the first time the Schechter parameters of the Halpha luminosity function (LF) of cluster galaxies. The Halpha LFs of Abell1367 and Coma are compared with each other and with that of Virgo, estimated using the B band LF by Sandage et al. (1985) and a L(Halpha) vs M_B relation. Typical parameters of phi^* ~ 10^0.00+-0.07 Mpc^-3, L^* ~ 10^41.25+- 0.05 erg sec^-1 and alpha ~ -0.70+-0.10 are found for the three clusters. The best fitting parameters of the cluster LFs differ from those found for field galaxies, showing flatter slopes and lower scaling luminosities L^*. Since, however, our Halpha survey is significantly deeper than those of field galaxies, this result must be confirmed on similarly deep measurements of field galaxies. By computing the total SFR per unit volume of cluster galaxies, and taking into account the cluster density in the local Universe, we estimate that the contribution of clusters like Coma and Abell1367 is approximately 0.25% of the SFR per unit volume of the local Universe.Comment: 19 pages, 11 figures, accepted for publication in A&

    The volume and Chern-Simons invariant of a Dehn-filled manifold

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    학위논문 (박사)-- 서울대학교 대학원 : 자연과학대학 수리과학부, 2019. 2. 박종일.Based on the work of Neumann, Zickert gave a simplicial formula for computing the volume and Chern-Simons invariant of a boundary-parabolic \psl-representation of a compact 3-manifold with non-empty boundary. Main aim of this thesis is to introduce a notion of deformed Ptolemy assignments (or varieties) and generalize the formula of Zickert to a representation of a Dehn-filled manifold. We also generalize the potential function of Cho and Murakami by applying our formula to an octahedral decomposition of a link complement in the 3-sphere. Also, motivated from the work of Hikami and Inoue, we clarify the relation between Ptolemy assignments and cluster variables when a link is given in a braid position. The last work is a joint work with Jinseok Cho and Christian Zickert.1 Introduction 1 1.1 Deformed Ptolemy assignments . . . . . . . . . . . . . . . . . . . 1 1.1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2 Potential functions . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.3 Cluster variables . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2 Preliminaries 12 2.1 Cocycles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2 Obstruction classes . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3 Ptolemy varieties 16 3.1 Formulas of Neumann . . . . . . . . . . . . . . . . . . . . . . . . 16 3.2 Deformed Ptolemy varieties . . . . . . . . . . . . . . . . . . . . . 19 3.2.1 Isomorphisms . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.2.2 Pseudo-developing maps . . . . . . . . . . . . . . . . . . . 27 3.3 Flattenings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.3.1 Main theorem . . . . . . . . . . . . . . . . . . . . . . . . . 36 4 Potential functions 43 4.1 Generalized potential functions . . . . . . . . . . . . . . . . . . . 43 4.1.1 Proof of Theorem 4.1.1 . . . . . . . . . . . . . . . . . . . 45 4.2 Relation with a Ptolemy assignment . . . . . . . . . . . . . . . . 50 4.2.1 Proof of Theorem 4.2.1 . . . . . . . . . . . . . . . . . . . 54 4.3 Complex volume formula . . . . . . . . . . . . . . . . . . . . . . . 57 4.3.1 Proof of Theorem 4.3.1 . . . . . . . . . . . . . . . . . . . 59 5 Cluster variables 70 5.1 The Hikami-Inoue cluster variables . . . . . . . . . . . . . . . . . 70 5.1.1 The octahedral decomposition . . . . . . . . . . . . . . . 70 5.1.2 The Hikami-Inoue cluster variables . . . . . . . . . . . . . 71 5.1.3 The obstruction cocycle . . . . . . . . . . . . . . . . . . . 74 5.1.4 Proof of Theorem 1.3.2 . . . . . . . . . . . . . . . . . . . 75 5.2 The existence of a non-degenerate solution . . . . . . . . . . . . . 79 5.2.1 Proof of Proposition 5.2.1 . . . . . . . . . . . . . . . . . . 81 5.2.2 Explicit computation from a representation . . . . . . . . 83Docto

    GANDALF - Graphical Astrophysics code for N-body Dynamics And Lagrangian Fluids

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    GANDALF is a new hydrodynamics and N-body dynamics code designed for investigating planet formation, star formation and star cluster problems. GANDALF is written in C++, parallelised with both OpenMP and MPI and contains a python library for analysis and visualisation. The code has been written with a fully object-oriented approach to easily allow user-defined implementations of physics modules or other algorithms. The code currently contains implementations of Smoothed Particle Hydrodynamics, Meshless Finite-Volume and collisional N-body schemes, but can easily be adapted to include additional particle schemes. We present in this paper the details of its implementation, results from the test suite, serial and parallel performance results and discuss the planned future development. The code is freely available as an open source project on the code-hosting website github at https://github.com/gandalfcode/gandalf and is available under the GPLv2 license.This research was supported by the DFG cluster of excellence "Origin and Structure of the Universe", DFG Projects 841797-4, 841798-2 (DAH, GPR), the DISCSIM project, grant agreement 341137 funded by the European Research Council under ERC-2013-ADG (GPR, RAB). Some development of the code and simulations have been carried out on the computing facilities of the Computational centre for Particle and Astrophysics (C2PAP) and on the DiRAC Data Analytic system at the University of Cambridge, operated by the University of Cambridge High Performance Computing Service on behalf of the STFC DiRAC HPC Facility (www.dirac.ac.uk); the equipment was funded by BIS National E-infrastructure capital grant (ST/K001590/1), STFC capital grants ST/H008861/1 and ST/H00887X/1, and STFC DiRAC Operations grant ST/K00333X/1

    Machine learning methods to estimate observational properties of galaxy clusters in large volume cosmological N-body simulations

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    This is a pre-copyedited, author-produced PDF of an article accepted for publication in Monthly Notices of the Royal Astronomical Society following peer review. The version of record Monthly Notices of the Royal Astronomical Society 518.1 (2023): 111-129 is available online at: https://academic.oup.com/mnras/article-abstract/518/1/111/6795309?redirectedFrom=fulltext#no-access-messageIn this paper, we study the applicability of a set of supervised machine learning (ML) models specifically trained to infer observed related properties of the baryonic component (stars and gas) from a set of features of dark matter (DM)-only cluster-size haloes. The training set is built from the three hundred project that consists of a series of zoomed hydrodynamical simulations of cluster-size regions extracted from the 1 Gpc volume MultiDark DM-only simulation (MDPL2). We use as target variables a set of baryonic properties for the intracluster gas and stars derived from the hydrodynamical simulations and correlate them with the properties of the DM haloes from the MDPL2 N-body simulation. The different ML models are trained from this data base and subsequently used to infer the same baryonic properties for the whole range of cluster-size haloes identified in the MDPL2. We also test the robustness of the predictions of the models against mass resolution of the DM haloes and conclude that their inferred baryonic properties are rather insensitive to their DM properties that are resolved with almost an order of magnitude smaller number of particles. We conclude that the ML models presented in this paper can be used as an accurate and computationally efficient tool for populating cluster-size haloes with observational related baryonic properties in large volume N-body simulations making them more valuable for comparison with full sky galaxy cluster surveys at different wavelengths. We make the best ML trained model publicly availableThe authors thank the anonymous referee for his/her invaluable comments and suggestions, without which this work would be in complete. D.d.A., W.C. and G.Y. would like to thank Ministerio de Ciencia e Innovación for financial support under project grant PID2021-122603NB-C21. WC is supported by the STFC AGP Grant ST/V000594/1 and the Atracción de Talento Contract no. 2020-T1/TIC-19882 granted by the Comunidad de Madrid in Spain. He also thanks the Ministerio de Ciencia e Innovación (Spain) for financial support under Project grant PID2021-122603NB C21. He further acknowledges the science research grants from the China Manned Space Project with NO. CMS-CSST-2021-A01 and CMS-CSST-2021-B01. G.M. acknowledges financial support from PID2019-106827GB-I00/AEI / 10.13039/501100011033 The CosmoSim database used in this paper is a service by the Leibniz Institute for Astrophysics Potsdam (AIP). The MultiDark database was developed in cooperation with the Spanish MultiDark Con solider Project CSD2009-00064. The authors acknowledge The Red Española de Supercomputación for granting computing time for running the hydrodynamical simulations of The300 galaxy cluster project in the Marenostrum supercomputer at the Barcelona Super-computing Cente
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