Transcript ppt
Science in the
Spanish Virtual Observatory
Enrique Solano
LAEFF-CAB / INTA-CSIC
SVO Principal Investigator
Outline
How can VO help astronomical research?
What kind of research is easier/more efficient in the
VO framework?
Science and the VO projects
(Spanish)-VO papers
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VO and astronomical research
What can VO do for science?
Science
Foster new and/or more efficient science.
How?
Tools
Facilitating the work of astronomers in fields like:
Data discovery, access, retrieval and analysis
Stds
– VO standards and tools
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Science that benefits from VO
Multiwavelength astronomy
Identification of “exotic” objects
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Science that benefits from VO
Multiwavelength astronomy
Searching the pieces.
Retrieving the pieces.
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Science that benefits from VO
Multiwavelength astronomy
Putting the pieces together.
EuroVO-AIDA workshop. ESAC Dec 08
Science that benefits from VO
“Exotic” objects
Cross-correlation among archives with millions of observations.
Data transfer for their local analysis unfeasible
Limitations in bandwidth
Storage --> Definition of a backup policy
Need for databases. Searched based on indexes.
Remote analysis. Transfer of results instead of data.
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Science that benefits from VO
“Exotic” objects and the VO
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Science and the VO projects
A concern for VO projects
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SVO: Support to VO Science
VO is something new.
If the services developed by the VO are not scientifically
oriented, they will not be used by the scientific community.
Goal: Liaison between SVO and research groups who have
identified scientific drivers to motivate the adoption of a Virtual
Observatory methodology in their science cases
SVO role:
Evaluation of the science case from the VO point of view.
Provision of information about the existing VO tools.
If needed, develop new tools.
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SVO: On-going projects
Identification and characterization of ultracool objects.
Characterization of stellar & substellar population in clusters.
Identification of hot subdwarfs.
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Brown dwarfs: link between low-mass stars and planets.
The formation process is still a matter of debate:
Scenarios:
Photo-erosion of massive prestellar cores by nearby ionising stars
(Whitworth & Zinnecker 2004)
– Only operate in the vicinity of a massive star. Cannot be a
major source of BDs.
Ejection theory (Reipurth & Clarke AJ, 2001): BDs are leftovers of a
prematurely interrupted accretion process. BUT
– High velocity dispersions No disks
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Star-like (fragmentation and collapse of a molecular cloud.)
– Supersonic turbulence in molecular clouds may generate
a complex density field with large contrast BD mass
cores gravitationally unstable (Padoan & Nordlun, ApJ
2004).
Fragmentation of massive extended discs around Sun-like stars of
the outer parts of the stellar disks (Stamatellos et al.,
MNRAS’07,’08)
– Released into the field by interactions among themselves.
» Low velocity dispersion (< 2 km/s) ability of
retaining the disk IR excess and accretion.
» Can explain the brown dwarf desert, i.e. the lack of
close BD companions to Sun-like stars.
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Search young BD by cross-correlating IPHAS and 2MASS
Outside SFRs (for the first time)
1600 square degrees
Filtering using appropriate color criteria.
Methodology: Aladin in script mode
Handling large area regions leads to memory problems.
Mosaic of 30’ radius regions.
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Follow-up of 113 candidates. 33 spectroscopically confirmed.
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New cross-match using the IPHAS Initial Data Release
(González-Solares et al. 2008).
PhotoObjBest: observations in good photometric conditions and
with good photometric calibration.
Follow-up spectroscopic observations have been requested in
two telescopes.
Spatial distribution: association with molecular clouds
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λOri SFR: 400 pc, low extinction (Av~0.36 mag) in the
inner area.
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Goal: Physical parameters (Teff, bolom. luminosity, mass
and age) of candidate members using SED fitting.
Compilation of the available multi-λ information.
Comparison with different collection of models.
– Tedious and time consuming
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Upper-limit of 12.3-16 Myr
(consistent with the
estimated ages for the
cluster by other authors).
Other physical parameters
also in good agreement with
prev. determinations.
20 minutes for the whole
workflow.
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Similar study in Taurus using UKIDSS, Spitzer and optical data.
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Identification and characterization of new regions to search substellar
objects.
BDs are much brighter when younger… BUT
Young clusters have variable extinction
σ Ori: young (3 Myr), close (390 pc) and free of extinction
mag)
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(Av < 0.3
Tycho-2 / 2MASS correlation
Members: Signsposts of youth
O,B, LiI in absorption, strong X-ray or Hα emission, IR excess.
Unknown status:
Proper motions and small deviations from the young-star seq.
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DENIS / 2MASS correlation
Boundary determination
Candidate characterization. Removal of:
Foreground and reddened background sources
Extended objects
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Catalogue of 509 confirmed and candidate young stars and
brown dwarfs.
Characterization of the mass function from 15 to 0.07 solar masses.
Excellent starting-point for further, follow-up dedicated studies.
Spatial distribution, presence of disks,…
Intensive use of VO for data discovery and characterization
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And as a bonus…
CPD-20 1123 (1896)
TYC 5940 962 1 (2000)
2MASS J06061339-2021072
(2003).
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Hot subdwarfs
• Originally classified as the brightest white dwarf in the last 40 years, it turned
out to be a very rare object: a bright He-B subdwarf.
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Hot subdwarfs
“(He-sdB) stars are extremely rare hot
subluminous stars whose existence cannot
be explained by canonical stellar evolution
theories nor by normal sdB evolution.”
(Ahmad, 2004)
SEA Scientific Meeting, July’08
Identification of new hot subdwarfs.
Data Mining tools to distinguish them from WDs and CVs
Characterisation using asteroseismological information from COROT
and Kepler.
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Conclusions
The Virtual Observatory
Discovery tool
Capable of analyzing large, worldwide-distributed data sets
SVO: 4 / 12 (2007-08)
Now it’s time to demonstrate that it works!!
Strength the links between the VO community and the
astronomical community is compulsory.
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10 FTEs + Network of almost 100 researches from 17 centres.
The Spanish VO
Lines of work:
http://svo.laeff.inta.es
Management of VO-compliant archives
Development of VO-tools (including Data Mining tools).
Support to VO-Science Key aspect in the project.
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