GUASOM: An Adaptive Visualization Tool for Unsupervised Clustering in Spectrophotometric Astronomical Surveys

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] We present an adaptive visualization tool for unsupervised classification of astronomical objects in a Big Data context such as the one found in the increasingly popular large spectrophotometric sky surveys. This tool is based on an artificial intelligence technique, Kohonen’s self-organizing maps, and our goal is to facilitate the analysis work of the experts by means of oriented domain visualizations, which is impossible to achieve by using a generic tool. We designed a client-server that handles the data treatment and computational tasks to give responses as quickly as possible, and we used JavaScript Object Notation to pack the data between server and client. We optimized, parallelized, and evenly distributed the necessary calculations in a cluster of machines. By applying our clustering tool to several databases, we demonstrated the main advantages of an unsupervised approach: the classification is not based on pre-established models, thus allowing the “natural classes” present in the sample to be discovered, and it is suited to isolate atypical cases, with the important potential for discovery that this entails. Gaia Utility for the Analysis of self-organizing maps is an analysis tool that has been developed in the context of the Data Processing and Analysis Consortium, which processes and analyzes the observations made by ESA’s Gaia satellite (European Space Agency) and prepares the mission archive that is presented to the international community in sequential periodic publications. Our tool is useful not only in the context of the Gaia mission, but also allows segmenting the information present in any other massive spectroscopic or spectrophotometric database.This work made use of the infrastructures acquired with grants provided by the State Research Agency (AEI) of the Spanish Government and the European Regional Development Fund (FEDER), RTI2018-095076-B-C22. We acknowledge support from CIGUS-CITIC, funded by Xunta de Galicia and the European Union (FEDER Galicia 2014-2020 Program) through grant ED431G 2019/01 and research consolidation grant ED431B 2021/36. 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. Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions. SDSS-IV acknowledges support and resources from the Center for High Performance Computing at the University of Utah. The SDSS website is www.sdss.org. SDSS-IV is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration. We also want to acknowledge Alhambra survey funded by the Spanish Goverment under Grant AYA2006-14056. Open Access funding provided thanks to the Universidade da Coruña/CISUG agreement with Springer NatureXunta de Galicia; ED431G 2019/01Xunta de Galicia; ED431B 2021/3

Gaia Early Data Release 3: Summary of the Contents and Survey Properties

This article has an erratum: [https://doi.org/10.1051/0004-6361/202039657e][Abstract] Context. We present the early installment of the third Gaia data release, Gaia EDR3, consisting of astrometry and photometry for 1.8 billion sources brighter than magnitude 21, complemented with the list of radial velocities from Gaia DR2. Aims. A summary of the contents of Gaia EDR3 is presented, accompanied by a discussion on the differences with respect to Gaia DR2 and an overview of the main limitations which are present in the survey. Recommendations are made on the responsible use of Gaia EDR3 results. Methods. The raw data collected with the Gaia instruments during the first 34 months of the mission have been processed by the Gaia Data Processing and Analysis Consortium and turned into this early third data release, which represents a major advance with respect to Gaia DR2 in terms of astrometric and photometric precision, accuracy, and homogeneity. Results. Gaia EDR3 contains celestial positions and the apparent brightness in G for approximately 1.8 billion sources. For 1.5 billion of those sources, parallaxes, proper motions, and the (GBP − GRP) colour are also available. The passbands for G, GBP, and GRP are provided as part of the release. For ease of use, the 7 million radial velocities from Gaia DR2 are included in this release, after the removal of a small number of spurious values. New radial velocities will appear as part of Gaia DR3. Finally, Gaia EDR3 represents an updated materialisation of the celestial reference frame (CRF) in the optical, the Gaia-CRF3, which is based solely on extragalactic sources. The creation of the source list for Gaia EDR3 includes enhancements that make it more robust with respect to high proper motion stars, and the disturbing effects of spurious and partially resolved sources. The source list is largely the same as that for Gaia DR2, but it does feature new sources and there are some notable changes. The source list will not change for Gaia DR3. Conclusions. Gaia EDR3 represents a significant advance over Gaia DR2, with parallax precisions increased by 30 per cent, proper motion precisions increased by a factor of 2, and the systematic errors in the astrometry suppressed by 30–40% for the parallaxes and by a factor ~2.5 for the proper motions. The photometry also features increased precision, but above all much better homogeneity across colour, magnitude, and celestial position. A single passband for G, GBP, and GRP is valid over the entire magnitude and colour range, with no systematics above the 1% levelXunta de Galicia; ED431B-2018/42Xunta de Galicia; ED481A-2019/155Xunta de Galicia; ED431G-2019/01Generalitat de Catalunya; 2014-SGR-1051https://doi.org/10.1051/0004-6361/202039657

Phosphorus-Rich Stars With Unusual Abundances Are Challenging Theoretical Predictions

[Abstract] Almost all chemical elements have been made by nucleosynthetic reactions in various kind of stars and have been accumulated along our cosmic history. Among those elements, the origin of phosphorus is of extreme interest because it is known to be essential for life such as we know on Earth. However, current models of (Galactic) chemical evolution under-predict the phosphorus we observe in our Solar System. Here we report the discovery of 15 phosphorus-rich stars with unusual overabundances of O, Mg, Si, Al, and Ce. Phosphorus-rich stars likely inherit their peculiar chemistry from another nearby stellar source but their intriguing chemical abundance pattern challenge the present stellar nucleosynthesis theoretical predictions. Specific effects such as rotation or advanced nucleosynthesis in convective-reactive regions in massive stars represent the most promising alternatives to explain the existence of phosphorus-rich stars. The phosphorus-rich stars progenitors may significantly contribute to the phosphorus present on Earth today.We acknowledge support from the State Research Agency (AEI) of the Spanish Ministry of Science, Innovation and Universities (MCIU) and the European Regional Development Fund (FEDER) under grants AYA2017-88254-P and RTI2018-095076-B-C22. We also wish to acknowledge the support received from the Centro de Investigación de Galicia CITIC, funded by Xunta de Galicia and the European Union (FEDER Galicia 2014-2020 Program) by grant ED431G 2019/01, the research group fund ED431B 2018/42 and the scholarship D481A-2019/155. This article is based on observations made in the Observatorios de Canarias del IAC with the Nordic Optical Telescope (NOT) operated on the island of La Palma by NOTSA in the Observatorio de Los Muchachos (ORM). This publication makes use of VOSA, developed under the Spanish Virtual Observatory project supported by the Spanish MINECO through grant AYA2017-84089. This work has made use of data from the European Space Agency (ESA) mission Gaia, processed by the Gaia Data Processing and Analysis Consortium (DPAC,). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions. SDSS acknowledges support and resources from the Center for High-Performance Computing at the University of Utah. SDSS is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration including the Brazilian Participation Group, the Carnegie Institution for Science, Carnegie Mellon University, the Chilean Participation Group, the French Participation Group, Harvard-Smithsonian Center for Astrophysics, Instituto de Astrofísica de Canarias, The Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU) / University of Tokyo, the Korean Participation Group, Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik (MPE), National Astronomical Observatories of China, New Mexico State University, New York University, University of Notre Dame, Observatório Nacional/MCTI, The Ohio State University, Pennsylvania State University, Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Autónoma de México, University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University, and Yale UniversityXunta de Galicia; ED431G 2019/01Xunta de Galicia; ED431B 2018/42Xunta de Galicia; ED481A-2019/15

Integrating pressure sensor control into semi-solid extrusion 3D printing to optimize medicine manufacturing

Semi-solid extrusion (SSE) is a three-dimensional printing (3DP) process that involves the extrusion of a gel or paste-like material via a syringe-based printhead to create the desired object. In pharmaceuticals, SSE 3DP has already been used to manufacture formulations for human clinical studies. To further support its clinical adoption, the use of a pressure sensor may provide information on the printability of the feedstock material in situ and under the exact printing conditions for quality control purposes. This study aimed to integrate a pressure sensor in an SSE pharmaceutical 3D printer for both material characterization and as a process analytical technology (PAT) to monitor the printing process. In this study, three materials of different consistency were tested (soft vaseline, gel-like mass and paste-like mass) under 12 different conditions, by changing flow rate, temperature, or nozzle diameter. The use of a pressure sensor allowed, for the first time, the characterization of rheological properties of the inks, which exhibited temperature-dependent, plastic and viscoelastic behaviours. Controlling critical material attributes and 3D printing process parameters may allow a quality by design (QbD) approach to facilitate a high-fidelity 3D printing process critical for the future of personalized medicine

Identification of new hot subdwarf binary systems by means of Virtual Observatory tools

The estimation of the binary fraction of hot subdwarfs is key to shed light on the different evolution scenarios proposed to explain the loss of the hydrogen envelope during the red giant branch phase. In this paper, we analyse the spectral energy distribution of the hot subdwarfs included in a recent and comprehensive catalogue with the aim of identifying companions. Our methodology shows a performance superior to the photometric criteria used in that study, identifying 202 objects wrongly classified as binaries according to their spectral energy distributions, and finding 269 new binaries. Out of an initial sample of 3186 objects, we classified 2469 as single and 615 as binary hot subdwarfs. The rest of the objects (102) were not classified because of their inadequate spectral energy distribution fitting due, in turn, to poor quality photometry. Effective temperatures, luminosities, and radii were computed for 192 singles and 42 binaries. They, in particular the binary sample, constitute an excellent data set to further perform a more careful spectroscopic analysis that could provide detailed values for the chemical composition, masses, ages, rotation properties, or reflection effects for the shortest period systems. The results obtained in this paper will be used as a reference for a forthcoming work where we aim to generalize binary and single hot subdwarf classification using Artificial Intelligence-based techniques.Agencia Estatal de Investigación | Ref. RTI2018-095076-B-C22Agencia Estatal de Investigación | Ref. PID2020-112949GB-I00Agencia Estatal de Investigación | Ref. MDM-2017-0737Xunta de Galicia | Ref. ED431G 2019/01Xunta de Galicia | Ref. ED481A-2019/155Gobierno de la Comunidad de Madrid | Ref. 2018-T2/TIC-1169

Mineralized alginate hydrogels using marine carbonates for bone tissue engineering applications

The search for an ideal bone tissue replacement has led to the development of new composite materials designed to simulate the complex inorganic/organic structure of bone. The present work is focused on the development of mineralized calcium alginate hydrogels by the addition of marine derived calcium carbonate biomineral particles. Following a novel approach, we were able to obtain calcium carbonate particles of high purity and complex micro and nanostructure dependent on the source material. Three different types of alginates were selected to develop inorganic/organic scaffolds in order to correlate alginate composition with scaffold properties and cell behavior. The incorporation of calcium carbonates into alginate networks was able to promote extracellular matrix mineralization and osteoblastic differentiation of mesenchymal stem cells when added at 7 mg/ml. We demonstrated that the selection of the alginate type and calcium carbonate origin is crucial to obtain adequate systems for bone tissue engineering as they modulate the mechanical properties and cell differentiation

The Neuro-Subject: A Living Entity with Learnability

In the context of an academic subject, students and teachers acquire knowledge and experience, but we must ensure that this experience will be shared and managed. In this way, the learning, acquired in the subject, remains in the subject. A proven way to manage the experience, which has been validated in previous works, is based on considering two dimensions: the conversion of individual knowledge into organizational and the use of a knowledge management system that allows classifying, organizing and finding knowledge based on ontologies and inferences between them. The primary objective of this research work is to join the two dimensions and apply an active method to manage the experience acquired by the teaching staff and students. The combination of the models RT-CICLO, as an active method, and ACCI 3.0 to transform individual and organizational knowledge can be applied so that organizational knowledge and learning are produced in a subject. In this work we have identified the actions in which the students create knowledge, as well as the type of knowledge that is created in each case. Organizational knowledge can be generated from each action, which can also be used to promote individual student learning. In the experience also have been acquired a high perception of usefulness on the part of students with regard to all types of organizational knowledge created