The Virtual Meteor Observatory

An internal research seminar for the European Space Agency's Research and Scientific Support Department

Transcript

1. 2008 June 27 Virtual Meteor Observatory (VMO) 1 The Virtual Meteor Observatory 2.5 million meteors in one database Geert Barentsen 27 June 2008 ESA/RSSD Internal Seminar MET-RSSD-HO-026
2. 2008 June 27 Virtual Meteor Observatory (VMO) 2 This talk 1. Meteor Science Why and how? 1. Meteor Science Why and how? 3. Virtual Observatories State-of-the-art databases in astronomy 3. Virtual Observatories State-of-the-art databases in astronomy 4. Virtual Meteor Observatory State-of-the-art database in meteor science 4. Virtual Meteor Observatory State-of-the-art database in meteor science Context Problem Inspiration Solution 2. Meteor data mining Meteors as a statistical science 2. Meteor data mining Meteors as a statistical science Part of my YGT project at ESA
3. 2008 June 27 Virtual Meteor Observatory (VMO) 3 Meteor Science Meteoroid – a solid object moving in interplanetary space, of a size considerably smaller than an asteroid and considerably larger than an atom or molecule. (IAU, 1961) (Better definition: object in space between 100 μm and 10 m.) Interesting because… • they teach us about asteroids and comets (poorman’s Rosetta); • they are potentially harmful to spacecraft; • they look cool when they hit us!
4. 2008 June 27 Virtual Meteor Observatory (VMO) 4 Meteoroid stream formation
5. 2008 June 27 Virtual Meteor Observatory (VMO) 5 Meteors
7. 2008 June 27 Virtual Meteor Observatory (VMO) 7 Meteor showers at Earth (Jenniskens, 1994) Southern Hemisphere Northern Hemisphere
8. Poynting-Robertson Meteoroid flux density Q(m 0 =10-3 g) Mass index s Mass-segregated stream Age (Belkovich, 2005)
9. 2008 June 27 Virtual Meteor Observatory (VMO) 9 What we see and what we want to know (Vaubaillon, 2005) (Top: Arlt, 2001. Bottom: Asher, 1999.) Meteor rate in Earth’s atmosphere 55P Dust trails near Earth orbit Simulated dust trails of 67P Churyumov-Gerasimenko
10. 2008 June 27 Virtual Meteor Observatory (VMO) 10 Observing meteoroids beyond Earth’s orbit 10P/Tempel 2 7P/Pons-Winnecke 2P/Encke (dust trail) 73P/Schwassmann-Wachmann (IRAS, 1983; Mark Sykes, William Reach) (Spitzer)
11. 2008 June 27 Virtual Meteor Observatory (VMO) 11 Observing meteoroids beyond Earth’s orbit (Vaubaillon, Christou, 2006) 45P near Venus In situ (Stardust, 2006) But… only observations of meteors in the atmosphere of the Earth currently provide us with a statistically significant set of data.
12. 2008 June 27 Virtual Meteor Observatory (VMO) 12 Earth-based observing Radar Camera Visual (MSX satellite, Jenniskens, 1998)
13. 2008 June 27 Virtual Meteor Observatory (VMO) 13 Infalling mass Radar Visual Camera (Jenniskens, 2006)
14. 2008 June 27 Virtual Meteor Observatory (VMO) 14 Radar & Forward Scatter (Verbelen, 2008) Not very precise, but works without clear sky!
15. 2008 June 27 Virtual Meteor Observatory (VMO) 15 Visual observing • Meteor counts in intervals per shower • Magnitude distributions per shower • Observing conditions: ◦ Limiting magnitude ◦ Field of view obstruction ◦ Number of cows Since 1982: • 2.2 million meteors • 5500 observers • 80 countries • Uniform observing method! Still outperforms video cameras for determining flux profiles.
16. 2008 June 27 Virtual Meteor Observatory (VMO) 16 Visual Report Form Online DB XML http://www.imo.net/visual/report
17. 2008 June 27 Virtual Meteor Observatory (VMO) 17 Quicklook results
18. 2008 June 27 Virtual Meteor Observatory (VMO) 18 Adaptive Averaging Algorithm 1. Group data intervals into adaptive bins. Parameters: ◦ Optimum number of meteors (200) ◦ Minimum bin width (5 minutes) ◦ Maximum bin width (5 hours) 2. Estimate ZHR for each bin using weighted averaging:
19. 2008 June 27 Virtual Meteor Observatory (VMO) 19 Evidence for dust trails from naked-eye counts • Leonids 2006 ◦ 19 November 2006, 4h46m UT ±6m ◦ 75 ±8 meteors/hour ◦ 55P/Tempel-Tuttle ◦ Trail from 1932 (2 rev) • Alpha-Aurigids 2007 ◦ 1 September 2007, 11h17m UT ±8m ◦ 175 ±40 meteors/hour ◦ C/1911 N1 Kiess ◦ Trail from 4 ±40 AD (1 rev!) (Arlt & Barentsen, 2006) (Barentsen, 2007)
20. 2008 June 27 Virtual Meteor Observatory (VMO) 20 Camera observing: MetRec
21. 2008 June 27 Virtual Meteor Observatory (VMO) 21 Camera observing relevance (Bruenjes, 2004) (Ryabova, 2006) Principal method for obtaining accurate photometry, astrometry and orbits. Coverage not good enough for fluxes (yet). Radiant structure (model)
22. 2008 June 27 Virtual Meteor Observatory (VMO) 22 Camera observing relevance Light curves provide information on the bulk properties of the meteoroid (when compared to ablation models). (Beech, 2003)
23. 2008 June 27 Virtual Meteor Observatory (VMO) 23 MetRec files
24. 2008 June 27 Virtual Meteor Observatory (VMO) 24 IMONET Since 1993: 400 000 meteors (Molau, Barentsen, 2008)
25. 2008 June 27 Virtual Meteor Observatory (VMO) 25 Meteor Airborne Campaigns (MAC) (ESA/SPOSH, 2008) (Townsend, 2008)
26. 2008 June 27 Virtual Meteor Observatory (VMO) 26 CILBO Canary Islands Long Baseline Observatory (CILBO)
27. Teide La Palma Google Earth image
28. Google Earth image
29. 2008 June 27 Virtual Meteor Observatory (VMO) 29 CILBO Lower aim point 100 km height Upper aim point 160 km height PC PC PC PC WinXP control machine DOS machine with MetRec DOS machine with MetRec DOS machine with MetRec Trigger for grabbing Internet, ESTEC ca. 140 km IAC network grating (Koschny, April)
30. 2008 June 27 Virtual Meteor Observatory (VMO) 30 CILBO • The Canary Island Long-Baseline meteor Observatory (CILBO) is currently being implemented • First light before the end of 2008? • Expect >1000 meteoroid orbits per year, plus spectra for several 100s of meteors.
31. 2008 June 27 Virtual Meteor Observatory (VMO) 31 This talk 1. Meteor Science Why and how? 1. Meteor Science Why and how? 3. Virtual Observatories State-of-the-art databases in astronomy 3. Virtual Observatories State-of-the-art databases in astronomy 4. Virtual Meteor Observatory New advance in meteor science 4. Virtual Meteor Observatory New advance in meteor science Context Problem Inspiration Solution 2. Meteor data mining Meteors as a statistical science 2. Meteor data mining Meteors as a statistical science
32. 2008 June 27 Virtual Meteor Observatory (VMO) 32 Data handling problem • Great potential in cross-correlating radar-visual-camera observations and comparing results and models, but not practical ◦ Different data formats and units ◦ Codes in different languages ◦ Data quality not clear ◦ Limited online availability Text CSV XML FITS Excel dBase Access Binary … (Barentsen, 2008) Meteor Scientist C/C++ Fortran IDL Perl … We need a proper (active) data archive
33. 2008 June 27 Virtual Meteor Observatory (VMO) 33 Virtual Meteor Observatory Architecture VMO Virtual Meteor Observatory SQL Database Visual … Converter Tool XML MetRec Web Interface Internal Libraries Quality check User Existing / Incoming data
34. 2008 June 27 Virtual Meteor Observatory (VMO) 34 http://vmo.imo.net 400 000 camera meteors
35. 2008 June 27 Virtual Meteor Observatory (VMO) 35 http://vmo.imo.net
36. 2008 June 27 Virtual Meteor Observatory (VMO) 36 VMODB PL/SQL & C Virtual Meteor Observatory System Components VMOTools Java VO Application s VO Application s Users Users Conversion & validation of existing archives Data archiving & analysis Web interfaces & services Drupal framework Apache HTTP Server PostgreSQL Database HTML VOTable VMOWeb PHP pgVMOMath pgVMOAstro pgVMOOrbit MetRecConverter MetRecValidator Component: Libraries: Task: SQL Call
37. 2008 June 27 Virtual Meteor Observatory (VMO) 37 Relational Database 50 tables 300 parameters
38. Example: camera system XML Config Files Model Interfaces generated automatically (VMOWeb library) - Name - Datatype - Constraints - Description - …. No manual programming!
39. 2008 June 27 Virtual Meteor Observatory (VMO) 39 Dataset Finder
40. 2008 June 27 Virtual Meteor Observatory (VMO) 40 Double-Station Meteor Finder
41. 2008 June 27 Virtual Meteor Observatory (VMO) 41 Virtual Meteor Observatory VMO Virtual Meteor Observatory SQL Database Visual … VOTable Global Virtual Observatory Global Virtual Observatory Converter Tool XML MetRec Web Interface Internal Libraries Other Astronomers VOTable Other VO’s Quality check User Existing archives
42. 2008 June 27 Virtual Meteor Observatory (VMO) 42 The Virtual Observatory: abstract example Archive A Archive A Processing A Processing A Archive B Archive B Archive C Archive C Data Retrieval Procedure A Data Retrieval Procedure B Processing B Processing B Science Science Shocks (X-ray) Dust (IR) e- (Radio) Example: Cassiopeia A Data Retrieval Procedure C Processing C Processing C Archive A Archive A Archive B Archive B Archive C Archive C Self-defining data standard Generic Processing Generic Processing Science Science Virtual Observatory = set of standards to query, retrieve and process data in a generic way. Standard Web-service Check units! e.g. http://esa.int/voquery?OBJECT=CasA
43. 2008 June 27 Virtual Meteor Observatory (VMO) 43 VOSpec
44. 2008 June 27 Virtual Meteor Observatory (VMO) 44 Aladin
45. 2008 June 27 Virtual Meteor Observatory (VMO) 45 VOTable Before Virtual Observatory Galaxy Distance Estimations RA DEC NAME R 010.68 +42.27 N 224 0.7 287.43 -63.85 N 6744 10.4 023.48 +30.66 N 598 0.7 After Virtual Observatory (VOTable) <VOTABLE version="1.1"> <COOSYS ID="J2000" equinox="J2000." epoch="J2000." system="eq_FK5"/> <RESOURCE name="myFavouriteGalaxies"> <TABLE name="results"> <DESCRIPTION>Distance estimations</DESCRIPTION> <PARAM name="Telescope" datatype="float" ucd="phys.size;instr.tel" unit="m" value="3.6"/> <FIELD name="RA" ucd="pos.eq.ra" ref="J2000" datatype="float" width="6" precision="2" unit="deg"/> <FIELD name="Dec" ucd="pos.eq.dec" ref="J2000" datatype="float" width="6" precision="2" unit="deg"/> <FIELD name="Name" ucd="meta.id" datatype="char" arraysize="8*"/> <FIELD name="R" ucd="phys.distance" datatype="float" width="4" precision="1" unit="Mpc"> <DESCRIPTION>Distance of Galaxy, assuming H=75km/s/Mpc</DESCRIPTION> </FIELD> <DATA> <TABLEDATA> <TR><TD>010.68</TD><TD>+41.27</TD><TD>N 224</TD><TD>0.7</TD></TR> <TR><TD>287.43</TD><TD>-63.85</TD><TD>N 6744</TD><TD>10.4</TD></TR> <TR><TD>023.48</TD><TD>+30.66</TD><TD>N 598</TD><TD>0.7</TD></TR> </TABLEDATA> </DATA> </TABLE> </RESOURCE> </VOTABLE> What does it mean? Data needs to be self-defining to be inter-operable!
46. 2008 June 27 Virtual Meteor Observatory (VMO) 46 Semantic Web Example: online bike shop… Non-Semantic Data (Web 1.0 & 2.0): human-interpretable. <table> <tr><td>Item</td><td>Price</td></tr> <tr><td>SuperBike 2000</td><td>200 eu</td></tr> <tr><td>UltraBike 3000</td><td>250 eu</td></tr> </table> Semantic Data (Web 3.0): human- and machine-interpretable. <catalog type='Shop'> <item type='Bike'><name>SuperBike 2000</name><price unit='euro'>200</price></item> <item type='Bike'><name>UltraBike 3000</name><price unit='euro'>250</price></item> </catalog>
47. 2008 June 27 Virtual Meteor Observatory (VMO) 47 VO Standards • International Virtual Observatory Alliance ◦ Standard data models and formats ◦ Standard data query and access protocols • SIA: Simple Image Access • SSP: Simple Spectral Access • Designed for astronomy, not planetary science ◦ Example: data access protocols take RA and DEC as input. ◦ Planetary science community needs to adapt standards • Planetary VO is part of EuroPlanet FP7 proposal. • VO for meteors is part of proposal. • Workshop will be held at ISSI (Bern) in November devoted to meteor science in the VO.
48. 2008 June 27 Virtual Meteor Observatory (VMO) 48 The Virtual Observatory
49. 2008 June 27 Virtual Meteor Observatory (VMO) 49 Summary • Virtual Meteor Observatory (VMO) makes meteor science data more easily accessible. • Basic functionality implemented, extensions and improvements to follow in later versions. • VMO 1.0 will be released on 20 september 2008 (International Meteor Conference). • Definition of Planetary VO ongoing, we aim to participate.