Determination of Stellar Parameters for Ariel Targets: a Comparison
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Asteroseismology with Corot, Kepler, K2 and TESS: Impact on Galactic Archaeology Talk Miglio’S
Asteroseismology with CoRoT, Kepler, K2 and TESS: impact on Galactic Archaeology talk Miglio’s CRISTINA CHIAPPINI Leibniz-Institut fuer Astrophysik Potsdam PLATO PIC, Padova 09/2019 AsteroseismologyPlato as it is : a Legacy with CoRoT Mission, Kepler for Galactic, K2 and TESS: impactArchaeology on Galactic Archaeology talk Miglio’s CRISTINA CHIAPPINI Leibniz-Institut fuer Astrophysik Potsdam PLATO PIC, Padova 09/2019 Galactic Archaeology strives to reconstruct the past history of the Milky Way from the present day kinematical and chemical information. Why is it Challenging ? • Complex mix of populations with large overlaps in parameter space (such as Velocities, Metallicities, and Ages) & small volume sampled by current data • Stars move away from their birth places (migrate radially, or even vertically via mergers/interactions of the MW with other Galaxies). • Many are the sources of migration! • Most of information was confined to a small volume Miglio, Chiappini et al. 2017 Key: VOLUME COVERAGE & AGES Chiappini et al. 2018 IAU 334 Quantifying the impact of radial migration The Rbirth mix ! Stars that today (R_now) are in the green bins, came from different R0=birth Radial Migration Sources = bar/spirals + mergers + Inside-out formation (gas accretion) GalacJc Center Z Sun R Outer Disk R = distance from GC Minchev, Chiappini, MarJg 2013, 2014 - MCM I + II A&A A&A 558 id A09, A&A 572, id A92 Two ways to expand volume for GA • Gaia + complementary photometric information (but no ages for far away stars) – also useful for PIC! • Asteroseismology of RGs (with ages!) - also useful for core science PLATO (miglio’s talk) The properties at different places in the disk: AMR CoRoT, Gaia+, K2 + APOGEE Kepler, TESS, K2, Gaia CoRoT, Gaia+, K2 + APOGEE PLATO + 4MOST? Predicon: AMR Scatter increases towards outer regions Age scatter increasestowars outer regions ExtracGng the best froM GaiaDR2 - Anders et al. -
LISA, the Gravitational Wave Observatory
The ESA Science Programme Cosmic Vision 2015 – 25 Christian Erd Planetary Exploration Studies, Advanced Studies & Technology Preparations Division 04-10-2010 1 ESAESA spacespace sciencescience timelinetimeline JWSTJWST BepiColomboBepiColombo GaiaGaia LISALISA PathfinderPathfinder Proba-2Proba-2 PlanckPlanck HerschelHerschel CoRoTCoRoT HinodeHinode AkariAkari VenusVenus ExpressExpress SuzakuSuzaku RosettaRosetta DoubleDouble StarStar MarsMars ExpressExpress INTEGRALINTEGRAL ClusterCluster XMM-NewtonXMM-Newton CassiniCassini-H-Huygensuygens SOHOSOHO ImplementationImplementation HubbleHubble OperationalOperational 19901990 19941994 19981998 20022002 20062006 20102010 20142014 20182018 20222022 XMM-Newton • X-ray observatory, launched in Dec 1999 • Fully operational (lost 3 out of 44 X-ray CCD early in mission) • No significant loss of performances expected before 2018 • Ranked #1 at last extension review in 2008 (with HST & SOHO) • 320 refereed articles per year, with 38% in the top 10% most cited • Observing time over- subscribed by factor ~8 • 2,400 registered users • Largest X-ray catalogue (263,000 sources) • Best sensitivity in 0.2-12 keV range • Long uninterrupted obs. • Follow-up of SZ clusters 04-10-2010 3 INTEGRAL • γ-ray observatory, launched in Oct 2002 • Imager + Spectrograph (E/ΔE = 500) + X- ray monitor + Optical camera • Coded mask telescope → 12' resolution • 72 hours elliptical orbit → low background • P/L ~ nominal (lost 4 out 19 SPI detectors) • No serious degradation before 2016 • ~ 90 refereed articles per year • Obs -
Data Mining in the Spanish Virtual Observatory. Applications to Corot and Gaia
Highlights of Spanish Astrophysics VI, Proceedings of the IX Scientific Meeting of the Spanish Astronomical Society held on September 13 - 17, 2010, in Madrid, Spain. M. R. Zapatero Osorio et al. (eds.) Data mining in the Spanish Virtual Observatory. Applications to Corot and Gaia. Mauro L´opez del Fresno1, Enrique Solano M´arquez1, and Luis Manuel Sarro Baro2 1 Spanish VO. Dep. Astrof´ısica. CAB (INTA-CSIC). P.O. Box 78, 28691 Villanueva de la Ca´nada, Madrid (Spain) 2 Departamento de Inteligencia Artificial. ETSI Inform´atica.UNED. Spain Abstract Manual methods for handling data are impractical for modern space missions due to the huge amount of data they provide to the scientific community. Data mining, understood as a set of methods and algorithms that allows us to recover automatically non trivial knowledge from datasets, are required. Gaia and Corot are just a two examples of actual missions that benefits the use of data mining. In this article we present a brief summary of some data mining methods and the main results obtained for Corot, as well as a description of the future variable star classification system that it is being developed for the Gaia mission. 1 Introduction Data in Astronomy is growing almost exponentially. Whereas projects like VISTA are pro- viding more than 100 terabytes of data per year, future initiatives like LSST (to be operative in 2014) and SKY (foreseen for 2024) will reach the petabyte level. It is, thus, impossible a manual approach to process the data returned by these surveys. It is impossible a manual approach to process the data returned by these surveys. -