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Plate Waves Structural Health Monitoring of Composite Structures

AIP conference Proceedings 2005年第1期760卷

作者： R. Jaganathan B. v. Somasekhar Krishnan Balasubramaniam C. v. Krishnamurthy Center for Nondestructive Evaluation Indian Institute of Technology Madras Chennai 600 036 INDIA Department of Mechanical Engineering Indian Institute of Technology Madras Chennai 600 036 INDIA

Layered composite plate‐like structures are finding an increasing range of applications in the aerospace industry. Structural Health Monitoring (SHM) of such structures is seen as a paradigm that will embrace efficient non‐destructive testing/evaluation techniques. The present study demonstrates two techniques that have the potential for the SHM of multi‐layered composite structures. The first technique is based on multi‐transmitter‐multi‐receiver (MTMR) technique with tomographic methods used for data reconstruction. In the MTMR, the possibility of SHM using algebraic reconstruction techniques (ART) for tomographic imaging with Lamb wave data measured in realistic materials is examined. Commercially available narrow bandwidth PZT crystals were used as sensors on multi‐layered quasi‐isotropic and cross‐ply composite plates with and without defects. Defects (through holes and low velocity impact delaminations) were synthetic and have been chosen to simulate impact damage in composite plates. To achieve reasonable image quality, conventional cross‐hole configuration is replaced by a new modified cross‐hole configuration that also optimizes the number of sensors. The second technique is a single‐transmitter‐multi‐receiver (STMR) technique that is more compact and uses reconstruction techniques that are analogus to synthetic aperture techniques. Here, the phase shifting is performed on the individual signals based on the guided wave dispersion relationships. The reconstruction algorithm uses summation of the phase shifted signals to image the location of defects, portions of the plate edges, and any reflectors from inherent structural features of the component.

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Application of pattern spectra and convolutional neural networks to the analysis of simulated Cherenkov Telescope Array data 37

Application of pattern spectra and convolutional neural netw...

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37th International Cosmic Ray conference, ICRC 2021

作者： Aschersleben, J. Peletier, R.F. vecchi, M. Wilkinson, M.H.F. Abdalla, H. Abe, H. Abe, S. Abusleme, A. Acero, F. Acharyya, A. Acín Portella, v. Ackley, K. Adam, R. Adams, C. Adhikari, S.S. Aguado-Ruesga, I. Agudo, I. Aguilera, R. Aguirre-Santaella, A. Aharonian, F. Alberdi, A. Alfaro, R. Alfaro, J. Alispach, C. Aloisio, R. Alves Batista, R. Amans, J.-P. Amati, L. Amato, E. Ambrogi, L. Ambrosi, G. Ambrosio, M. Ammendola, R. Anderson, J. Anduze, M. Angüner, E.O. Antonelli, L.A. Antonuccio, v. Antoranz, P. Anutarawiramkul, R. Aragunde Gutierrez, J. Aramo, C. Araudo, A. Araya, M. Arbet-Engels, A. Arcaro, C. Arendt, v. Armand, C. Armstrong, T. Arqueros, F. Arrabito, L. Arsioli, B. Artero, M. Asano, K. Ascasíbar, Y. Ashley, M. Attinà, P. Aubert, P. Singh, C.B. Baack, D. Babic, A. Backes, M. Baena, v. Bajtlik, S. Baktash, A. Balazs, C. Balbo, M. Ballester, O. Ballet, J. Balmaverde, B. Bamba, A. Bandiera, R. Baquero Larriva, A. Barai, P. Barbier, C. Barbosa Martins, v. Barcelo, M. Barkov, M. Barnard, M. Baroncelli, L. Barres de Almeida, U. Barrio, J.A. Bastieri, D. Batista, P.I. Batkovic, I. Bauer, C. Bautista-González, R. Baxter, J. Becciani, U. Becerra González, J. Becherini, Y. Beck, G. Becker Tjus, J. Bednarek, W. Belfiore, A. Bellizzi, L. Belmont, R. Benbow, W. Berge, D. Bernardini, E. Bernardos, M.I. Bernlöhr, K. Berti, A. Berton, M. Bertucci, B. Beshley, v. Bhatt, N. Bhattacharyya, S. Bhattacharyya, W. Bhattacharyya, S. Bi, B. Bicknell, G. Biederbeck, N. Bigongiari, C. Biland, A. Bird, R. Bissaldi, E. Biteau, J. Bitossi, M. Blanch, O. Blank, M. Blazek, J. Bobin, J. Boccato, C. Bocchino, F. Boehm, C. Bohacova, M. Boisson, C. Boix, J. Bolle, J.-P. Bolmont, J. Bonanno, G. Bonavolontà, C. Bonneau Arbeletche, L. Bonnoli, G. Bordas, P. Borkowski, J. Bórquez, S. Bose, R. Bose, D. Bosnjak, Z. Bottacini, E. Böttcher, M. Botticella, M.T. Boutonnet, C. Bouyjou, F. Bozhilov, v. Bozzo, E. Brahimi, L. Braiding, C. Brau-Nogué, S. Kapteyn Astronomical Institute University of Groningen PO Box 800 Groningen9700 AV Netherlands Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen PO Box 407 GroningenNL-9700 AK Netherlands Centre for Space Research North-West University Potchefstroom2520 South Africa Institute for Cosmic Ray Research University of Tokyo 5-1-5 Kashiwa-no-ha Chiba Kashiwa277-8582 Japan Pontificia Universidad Católica de Chile Av. Libertador Bernardo O'Higgins 340 Santiago Chile AIM CEA CNRS Université Paris-Saclay Université Paris Diderot Sorbonne Paris Cité CEA Paris-Saclay IRFU DAp Bat 709 Orme des Merisiers Gif-sur-Yvette91191 France Centre for Advanced Instrumentation Dept. of Physics Durham University South Road DurhamDH1 3LE United Kingdom Port d'Informació Científica Edifici D Carrer de l'Albareda Bellaterrra Cerdanyola del Vallès08193 Spain School of Physics and Astronomy Monash University MelbourneVIC3800 Australia Laboratoire Leprince-Ringuet École Polytechnique UMR 7638 CNRS/IN2P3 Institut Polytechnique de Paris Palaiseau91128 France Department of Physics Columbia University 538 West 120th Street New YorkNY10027 United States University of Oslo Department of Physics Sem Saelandsvei 24 - PO Box 1048 Blindern OsloN-0316 Norway EMFTEL Department IPARCOS Universidad Complutense de Madrid Madrid28040 Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Campus UAB Carrer de Can Magrans s/n Cerdanyola del Vallés08193 Spain Instituto de Física Teórica UAM/CSIC Departamento de Física Teórica Universidad Autónoma de Madrid c/Nicolás Cabrera 13-15 Campus de Cantoblanco UAM Madrid28049 Spain Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin 2 Ireland Universidad Nacional Autónoma de México Delegación Coyoacán Ciudad de México04510 Mexico University of Geneva Département de Physique Nucléaire et Corpusculaire 24 rue d

The Cherenkov Telescope Array (CTA) will be the next generation gamma-ray observatory and will be the major global instrument for very-high-energy astronomy over the next decade, offering 5 − 10 × better flux sensitivity than current generation gamma-ray telescopes. Each telescope will provide a snapshot of gamma-ray induced particle showers by capturing the induced Cherenkov emission at ground level. The simulation of such events provides images that can be used as training data for convolutional neural networks (CNNs) to determine the energy of the initial gamma rays. Compared to other state-of-the-art algorithms, analyses based on CNNs promise to further enhance the performance to be achieved by CTA. Pattern spectra are commonly used tools for image classification and provide the distributions of the shapes and sizes of various objects comprising an image. The use of relatively shallow CNNs on pattern spectra would automatically select relevant combinations of features within an image, taking advantage of the 2D nature of pattern spectra. In this work, we generate pattern spectra from simulated gamma-ray events instead of using the raw images themselves in order to train our CNN for energy reconstruction. This is different from other relevant learning and feature selection methods that have been tried in the past. Thereby, we aim to obtain a significantly faster and less computationally intensive algorithm, with minimal loss of performance. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

关键词： Gamma rays

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A fast gradient projection method for 3D image reconstruction from limited tomographic data

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Journal of Physics: conference Series 2017年第1期904卷

作者： v.L. Coli E. Loli Piccolomini E. Morotti L. Zanni Department of Physics Informatics and Mathematics University of Modena and Reggio Emilia Department of Mathematics University of Bologna Department of Mathematics University of Padova

We consider in this paper the problem of reconstructing 3D Computed Tomography images from limited data. The problem is modeled as a nonnegatively constrained minimization problem of very large size. In order to obtain an acceptable image in short time, we propose a scaled gradient projection method, accelerated by exploiting a suitable scaling matrix and efficient rules for the choice of the step-length. In particular, we select the step-length either by alternating Barzilai-Borwein rules or by exploiting a limited number of back gradients for approximating second-order information. Numerical results on a 3D Shepp-Logan phantom are presented and discussed.

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Development of the “polar map” method in nuclear cardiology

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AIP conference Proceedings 2021年第1期2351卷

作者： I. P. Kolinko N. v. Denisova 1Novosibirsk State University 630090 Novosibirsk Russia 2Khristianovich Institute of Theoretical and Applied Mechanics SB RAS 630090 Novosibirsk Russia

Despite the widespread use of the SPECT method for the diagnosis of cardiovascular diseases, there are still unresolved problems with this method, which can lead to misinterpretation of the data, and, as a result, wrong diagnosis. At the moment, in diagnostic nuclear medicine there is an active development of an approach based on the method of mathematical modeling. It includes a mathematical model of the distribution of a radiopharmaceutical (RFP) in the body of a virtual patient, a mathematical model for collecting raw data, and a mathematical program for creating a reconstructed image. Using the patient model, we can program the heart disease and present the examination results in the same way as the clinical data. Further, by verifying the obtained images with images obtained from real patients, the error of the reconstruction algorithms can be estimated. In medical practice, the image of the myocardium of the left ventricle (Lv) of the heart in the mode of tomographic slices is often used. This paper presents the results of studies aimed at developing the representation of a mathematical model of the Lv myocardium in the form of a polar map “bulleye”.

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Recovering structure from many low-information 2-D images of randomly-oriented samples

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Journal of Physics: conference Series 2013年第19期425卷

作者： H T Philipp K Ayyer M W Tate v Elser S M Gruner Laboratory of Atomic and Solid State Physics Cornell University Ithaca NY 14853 USA Cornell High Energy Synchrotron Source Cornell University Ithaca NY 14853 USA Laboratory for Elementary Particle Physics Cornell University Ithaca NY 14853 USA

New sources and detectors are allowing scientists to look at matter with finer spatial and temporal resolutions. These experiments can produce data that are a series of severely Poisson limited snap-shots of randomly oriented samples. An extreme case of this is destructive imaging of single particles with an x-ray free-electron laser – many frames are needed for a reconstruction, but there is no a priori information associated with the frames about particle orientation. We use Cornell's Pixel Array Detectors (PADs) to examine the practical limits of an expectation maximization (EM) algorithm designed to deal with extremely low-fluence data, having just a few photons per frame. We demonstrate image reconstruction of a high-contrast sample using hundreds of thousands of randomly oriented frames with an average x-ray photon occupancy as low as 2.5 photons per frame. Practical aspects of reducing low-fluence data, such as thresholding and noise limits, will be discussed for high- and low-contrast samples; and data collected in the presence of significant background signal.

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Analysis of the Cherenkov Telescope Array first Large-Sized Telescope real data using convolutional neural networks 37

Analysis of the Cherenkov Telescope Array first Large-Sized ...

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37th International Cosmic Ray conference, ICRC 2021

作者： vuillaume, Thomas Jacquemont, Mikaël de Bony de Lavergne, Mathieu Sanchez, David A. Poireau, vincent Maurin, Gilles Benoit, Alexandre Lambert, Patrick Lamanna, Giovanni Abe, H. Aguasca, A. Agudo, I. Antonelli, L.A. Aramo, C. Armstrong, T. Artero, M. Asano, K. Ashkar, H. Aubert, P. Baktash, A. Bamba, A. Baquero Larriva, A. Baroncelli, L. Barres de Almeida, U. Barrio, J.A. Batkovic, I. Becerra González, J. Bernardos, M.I. Berti, A. Biederbeck, N. Bigongiari, C. Blanch, O. Bonnoli, G. Bordas, P. Bose, D. Bulgarelli, A. Burelli, I. Buscemi, M. Cardillo, M. Caroff, S. Carosi, A. Cassol, F. Cerruti, M. Chai, Y. Cheng, K. Chikawa, M. Chytka, L. Contreras, J.L. Cortina, J. Costantini, H. Dalchenko, M. De Angelis, A. Deleglise, G. Delgado, C. Delgado Mengual, J. della volpe, D. Depaoli, D. Di Pierro, F. Di venere, L. Díaz, C. Dominik, R.M. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Elsässer, D. Emery, G. Escudero, J. Fiasson, A. Foffano, L. Fonseca, M.v. Freixas Coromina, L. Fukami, S. Fukazawa, Y. Garcia, E. Garcia López, R. Giglietto, N. Giordano, F. Gliwny, P. Godinovic, N. Green, D. Grespan, P. Gunji, S. Hackfeld, J. Hadasch, D. Hahn, A. Hassan, T. Hayashi, K. Heckmann, L. Heller, M. Herrera Llorente, J. Hirotani, K. Hoffmann, D. Horns, D. Houles, J. Hrabovsky, M. Hrupec, D. Hui, D. Hütten, M. Inada, T. Inome, Y. Iori, M. Ishio, K. Iwamura, Y. Jimenez Martinez, I. Jouvin, L. Jurysek, J. Kagaya, M. Karas, v. Katagiri, H. Kataoka, J. Kerszberg, D. Kobayashi, Y. Kong, A. Kubo, H. Kushida, J. Lamastra, A. Le Flour, T. Longo, F. López-Coto, R. López-Moya, M. López-Oramas, A. Luque-Escamilla, P.L. Majumdar, P. Makariev, M. Mandat, D. Manganaro, M. Mannheim, K. Mariotti, M. Marquez, P. Marsella, G. Martí, J. Martinez, O. Martínez, G. Martínez, M. Marusevec, P. Mas, A. Mazin, D. Mestre Guillen, E. Micanovic, S. Miceli, D. Miener, T. Miranda, J.M. Miranda, L.D.M. Mirzoyan, R. Mizuno, T. Molina, E. Montaruli, T. Monteiro, I. Moralejo, A. Morcuende, D. Moretti, E. Laboratoire d'Annecy de Physique des Particules Univ. Grenoble Alpes Univ. Savoie Mont Blanc CNRS-IN2P3 LAPP 9 chemin de bellevue Annecy74940 France LISTIC Université Savoie Mont-Blanc Polytech Annecy-Chambéry 5 chemin de bellevue Annecy74940 France Institute for Cosmic Ray Research University of Tokyo Japan Departament de Física Quàntica i Astrofísica Institut de Ciències del Cosmos Universitat de Barcelona IEEC-UB Spain Instituto de Astrofísica de Andalucía CSIC Spain INAF - Osservatorio Astronomico di Roma Italy INFN Sezione di Napoli Italy Aix Marseille Univ CNRS IN2P3 CPPM France The Barcelona Institute of Science and Technology Spain IRFU CEA Université Paris-Saclay France Universität Hamburg Institut für Experimentalphysik Germany Graduate School of Science University of Tokyo Japan EMFTEL department IPARCOS Universidad Complutense de Madrid Spain INAF - Osservatorio di Astrofisica e Scienza dello spazio di Bologna Italy Centro Brasileiro de Pesquisas Físicas Brazil INFN Sezione di Padova Università degli Studi di Padova Italy Instituto de Astrofísica de Canarias Departamento de Astrofísica Universidad de La Laguna Spain Max-Planck-Institut für Physik Germany Department of Physics TU Dortmund University Germany Saha Institute of Nuclear Physics India INFN Sezione di Trieste Università degli Studi di Udine Italy INFN Sezione di Catania Italy Italy University of Geneva Département de Physique Nucléaire et Corpusculaire Switzerland Palacky University Olomouc Faculty of Science Czech Republic CIEMAT Spain Port d'Informació Científica Spain INFN Sezione di Torino Italy Dipartimento di Fisica Universitá degli Studi di Torino Italy INFN Sezione di Bari Università di Bari Italy University of Rijeka Department of Physics Croatia Institute for Theoretical Physics and Astrophysics Universität Würzburg Germany Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Japan INFN Sezione di Bari Politecnico di Bari

The Cherenkov Telescope Array (CTA) is the future ground-based gamma-ray observatory and will be composed of two arrays of imaging atmospheric Cherenkov telescopes (IACTs) located in the Northern and Southern hemispheres respectively. The first CTA prototype telescope built on-site, the Large-Sized Telescope (LST-1), is under commissioning in La Palma and has already taken data on numerous known sources. IACTs detect the faint flash of Cherenkov light indirectly produced after a very energetic gamma-ray photon has interacted with the atmosphere and generated an atmospheric shower. reconstruction of the characteristics of the primary photons is usually done using a parameterization up to the third order of the light distribution of the images. In order to go beyond this classical method, new approaches are being developed using state-of-the-art methods based on convolutional neural networks (CNN) to reconstruct the properties of each event (incoming direction, energy and particle type) directly from the telescope images. While promising, these methods are notoriously difficult to apply to real data due to differences (such as different levels of night sky background) between Monte Carlo (MC) data used to train the network and real data. The GammaLearn project, based on these CNN approaches, has already shown an increase in sensitivity on MC simulations for LST-1 as well as a lower energy threshold. This work applies the GammaLearn network to real data acquired by LST-1 and compares the results to the classical approach that uses random forests trained on extracted image parameters. The improvements on the background rejection, event direction, and energy reconstruction are discussed in this contribution. © 2022 Sissa Medialab Srl. All rights reserved.

关键词： Telescopes

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Processing potential and analysis of aspects influencing the results of analyses performed by digital image correlation

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AIP conference Proceedings 2023年第1期2976卷

作者： Martin Hagara Miroslav Pástor Jozef Bocko Technical University of Košice Faculty of Mechanical Engineering Department of Applied Mechanics and Mechanical Engineering Letná 1/9 042 00 Košice Slovakia

Digital image correlation belongs to the group of the modern full-field optical methods designed for 2D or 3D (depending on the configuration of the camera system) analysis of displacement or strain fields. This paper describes the processing capabilities of the experimental data acquired by Dantec Dynamics digital image correlation systems. For this purpose, a program called MOSTRAN v.1.0 has been specially developed at the authors’ workplace, enabling the processing of results from both deformation and motion analyses. The possibilities of using the mentioned program are described in the paper by means of several practical applications. In addition, the paper highlights chosen aspects influencing the results of experimental analyses, such as the method of generating a random speckle pattern required for the observation and reconstruction of the analyzed object contour, illumination conditions ensured during realization of the measurements, parameters obtained by the calibration of the cameras as well as the analysis of the appropriate facet size and corresponding method with properly set level of contour/displacement smoothing.

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Improving PET sensitivity with a Compton algorithm

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Journal of Physics: conference Series 2017年第1期931卷

作者： v. Ilisie v. Giménez-Alventosa L. Moliner A. Aguilar E. Lamprou A. González F. Sánchez J.M. Benlloch Instituto de Instrumentación para Imagen Molecular (I3M) Centro Mixto CSIC - Universitat Politècnica de València Camino de Vera S/N.

Current PET detectors have a very low sensitivity, of the order of a few percent. One of the reasons is that Compton events are being rejected. In this work we aim to prove that Compton events are a very rich source of additional information that can play a crucial role in the image reconstruction process. With this additional data, the detector sensitivity will be substantially improved and thus, the applied dose on the patient can also be reduced. This could be a really breaking point for PET detector technology as one should be able to obtain better image quality with less patient radiation. By means of Compton cone matching (the Compton cones coming from the same event should be compatible) one should be able to better recognize matching events and discard randoms and even events that have previously suffered scattering within the patient's body.

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Leveraging the checkpoint-restart technique for optimizing CPU efficiency of ATLAS production applications on opportunistic platforms

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Journal of Physics: conference Series 2018年第3期1085卷

作者： D Cameron J Elmsheuser L Heinrich W Lavrijsen P Nilsson v Tsulaia M vogel on behalf of theATLAS Collaboration University of Oslo P.b. 1048 Blindern 0316 Oslo Norway Brookhaven National Laboratory PO Box 5000 Upton NY 11973 USA New York University 70 Washington Square South New York NY 10012 USA Lawrence Berkeley National Laboratory 1 Cyclotron Road Berkeley CA 94720 USA University of Wuppertal Gausstrasse 20 42119 Wuppertal Germany

data processing applications of the ATLAS experiment, such as event simulation and reconstruction, spend considerable amount of time in the initialization phase. This phase includes loading a large number of shared libraries, reading detector geometry and condition data from external databases, building a transient representation of the detector geometry and initializing various algorithms and services. In some cases the initialization step can take as long as 10-15 minutes. Such slow initialization has a significant negative impact on overall CPU efficiency of the production job, especially when the job is executed on opportunistic, often short-lived, resources such as commercial clouds or volunteer computing. In order to improve this situation, we can take advantage of the fact that ATLAS runs large numbers of production jobs with similar configuration parameters (e.g. jobs within the same production task). This allows us to checkpoint one job at the end of its configuration step and then use the generated checkpoint image for rapid startup of thousands of production jobs. By applying this technique we can bring the initialization time of a job from tens of minutes down to just a few seconds. In addition to that we can leverage container technology for restarting checkpointed applications on the variety of computing platforms, in particular of platforms different from the one on which the checkpoint image was created. We will describe the mechanism of creating checkpoint images of Geant4 simulation jobs with AthenaMP (the multi-process version of the ATLAS data simulation, reconstruction and analysis framework Athena) and the usage of these images for running ATLAS Simulation production jobs on volunteer computing resources (ATLAS@Home) and on Supercomputers.

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MATISSE status report and science forecast 4

MATISSE status report and science forecast

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conference on Optical and Infrared Interferometry Iv

作者： Lopez, B. Lagarde, S. Jaffe, W. Petrov, R. Schoeller, M. Antonelli, P. Beckman, U. Berio, Ph. Bettonvil, F. Graser, U. Millour, F. Robbe-Dubois, S. venema, L. Wolf, S. Bristow, P. Glindemann, A. Gonzalez, J. -C. Lanz, Th. Henning, T. Weigelt, G. Agocs, T. Augereau, J. -C. Avila, G. Bailet, C. Behrend, J. Berger, J. -P. von Boekel, R. Bonhomme, S. Bourget, P. Brast, R. Bresson, Y. Clausse, J. M. Chesneau, O. Csepany, G. Connot, C. Crida, A. Danchy, W. C. Delbo, M. Delplancke, F. Dominik, C. Dugue, M. Elswijk, E. Fantei, Y. Finger, G. Gabasch, A. Girard, P. Girault, v. Gitton, P. Glazenborg, A. Gonte, F. Guitton, F. Guniat, S. De Haan, M. Haguenauer, P. Hanenburg, H. Heininger, M. Hofmann, K. -H. Hogerheijde, M. ter Horst, R. Hron, J. Hugues, Y. Ives, D. Jakob, G. Jasko, A. Jolley, P. Kragt, J. Koehler, R. Kroener, T. Kroes, G. Labadie, L. Laun, W. Lehmitz, M. Leinert, Ch. Lizon, J. L. Lucuix, Ch. Marcotto, A. Martinache, F. Matter, A. Martinot-Lagarde, G. Mauclert, N. Mehrgan, L. Meilland, A. Mellein, M. Menardi, S. Menut, J. L. Meisenheimer, K. Morel, S. Mosoni, L. Navarro, R. Neumann, U. Nussbaum, E. Ottogalli, S. Palsa, R. Panduro, J. Pantin, E. Percheron, I. Phan Duc, T. Pott, J. -U. Pozna, E. Przygodda, F. Richichi, A. Rigal, F. Rupprecht, G. Schertl, D. Stegmeier, J. Thiam, L. Tromp, N. vannier, M. vakili, F. van Belle, G. Wagner, K. Woillez, J. Univ Nice Sophia Antipolis UMR7293 Lab Lagrange CNRSObserv Cote dAzur F-06304 Nice France Max Planck Inst Astron D-69117 Heidelberg Germany Leiden Univ Leiden Observ NL-2300 RA Leiden Netherlands NOVA Opt IR Instrument Grp ASTRON NL-7990 AA Dwingeloo Netherlands Max Planck Inst Radioastron D-53121 Bonn Germany Univ Kiel Inst Theoret Phys & Astrophys D-24118 Kiel Germany Univ Vienna Inst Astrophys A-1180 Vienna Austria European So Observ D-85748 Garching Germany MTA Res Ctr Astron & Earth Sci Konkoly Thege Miklos Astron Inst H-1525 Budapest Hungary Univ Cologne Inst Phys 1 D-50937 Cologne Germany Univ Grenoble Alpes CNRS IPAG F-38000 Grenoble France Kernfys Versneller Inst NL-9747 AA Groningen Netherlands Univ Paris Diderot CNRS CEA DSM Lab AIMIRFUServ AstrophysCEA Saclay F-91191 Gif Sur Yvette France Deutsch Thomson OHG D-78048 Villingen Schweninger Germany Natl Astron Res Inst Thailand Chiang Mai 50200 Thailand Lowell Observ Flagstaff AZ 86001 USA NASA Goddard Space Flight Ctr Greenbelt MD 20771 USA

ISBN: (纸本)9780819496140

MATISSE is the mid-infrared spectrograph and imager for the very Large Telescope Interferometer (vLTI) at Paranal. This second generation interferometry instrument will open new avenues in the exploration of our Universe. Mid-infrared interferometry with MATISSE will allow significant advances in various fundamental research fields: studies of disks around young stellar objects where planets form and evolve, surface structures and mass loss of stars in late evolutionary stages, and the environments of black holes in active galactic nuclei. MATISSE is a unique instrument. As a first breakthrough it will enlarge the spectral domain used by optical interferometry by offering the L & M bands in addition to the N band, opening a wide wavelength domain, ranging from 2.8 to 13 mu m on angular scales of 3 mas (L/M band) / 10 mas (N band). As a second breakthrough, it will allow mid-infrared imaging - closure-phase aperture-synthesis imaging with up to four Unit Telescopes (UT) or Auxiliary Telescopes (AT) of the vLTI. MATISSE will offer various ranges of spectral resolution between R similar to 30 to similar to 5000. In this article, we present some of the main science objectives that have driven the instrument design. We introduce the physical concept of MATISSE including a description of the signal on the detectors and an evaluation of the expected performance and discuss the project status. The operations concept will be detailed in a more specific future article, illustrating the observing templates operating the instrument, the data reduction and analysis, and the image reconstruction software.

关键词： eInterferometry Spectroscopy Mid-Infrared vLTI

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