Optical Galactic Plane Surveys

a seminar for Cardiff University on 29 May 2013

Transcript

1. Optical Galactic Plane Surveys a seminar by Geert Barentsen for Cardiff University on 29 May 2013 Image: NGC 3293/3324 in VPHAS (credit: Hywel Farnhill)
2. Hi. I’m Geert Barentsen @GeertMcTwit blog.barentsen.be github.com/barentsen
3. My favourite picture of our Galaxy (Credit: Nick Risinger / skysurvey.org)
4. Stars
5. INT/IPHAS & UVEX VST/VPHAS UKIDSS/GPS VISTA/VVV SCUBA-2/JPS & SASSy Herschel/Hi-GAL Janet Drew Phil Lucas Mark Thompson In Hertfordshire, we <3 Milky Way surveys
6. This talk 1. Introduction The IPHAS/UVEX/VPHAS surveys are mapping the entire Galactic Plane in visible light. 2. Scientific rationale These projects are a necessary counterpart to infrared surveys. Also a key complement to Gaia. 3. Demonstration Discovery and parameter inference of young stars using survey photometry.
7. IPHAS+UVEX+VPHAS = EGAPS • EGAPS stands for “European Galactic Plane Surveys”; key members are from institutes in UK/NL/Spain. • Composed of two surveys in the North (IPHAS/UVEX) and one survey in the South (VPHAS+). • Covers the entire Galactic Plane at |b| < 5° in u’, g’, r’, i’, Hα (near-simultaneous). • 5σ-depth typically at g’ > 22 / r’ > 21. (complete to r’ ~ 19; saturated at r’ ~ 13.)
8. IPHAS/UVEX surveys (Northern Plane) Wide Field Camera Isaac Newton Telescope (La Palma)
9. Isaac Newton Telescope • Originally located in Herstmonceux, Sussex (1967-1979). • “Moved” to La Palma in 1984 (new dome, mount & primary). • Surveys apply for time each semester, like anyone else.
10. IPHAS INT Photometric Hα Survey (Northern Plane) r’ i’ Hα www.iphas.org
11. U g’ r’ astro.ru.nl/uvex UVEX UV Excess Survey (Northern Plane)
12. = 15 270 pointings
14. NGC 2244 (3°×2°) IPHAS H-alpha Credit: Nick Wright
15. NGC 2237 (30’×20’) IPHAS H-alpha Credit: Nick Wright
16. IC 1396 (30’×20’) IPHAS Ha+r+i Credit: Nick Wright
17. Omegacam VLT Survey Telescope (Paranal) VPHAS+ VST Photometric Hα Survey (ESO Public Survey) www.vphasplus.org
18. g’ r’ u’ i’ Hα VPHAS+ VST Photometric Hα Survey www.vphasplus.org
19. VPHAS+ Footprint
20. NGC 6611 (60’×40’) VPHAS H-alpha
21. Eta Carinae (7°×4°) VPHAS H-alpha Credit: Hywel Farnhill
22. NGC 3293 (40’×30’) VPHAS H-alpha
23. NGC 3293 (40’×30’) VPHAS u’
24. NGC 3293 / NGC 3324 VPHAS u’+g’+H-alpha Credit: Hywel Farnhill
25. NGC 6530 VPHAS H-ALPHA Credit: Hywel Farnhill
26. VPHAS+ status • Data taking started early 2012. • Nearly 20% done so far. • Data quality is looking superb • 0.8” median seeing in r’ (unguided); • 0.05 median ellipticity. • Reduced data of the first semester was handed over to ESO on 30 April - release imminent.
27. IPHAS/UVEX status • Initial data releases in 2008 (IPHAS) and 2011 (UVEX). • IPHAS Data Release 2 (DR2) is imminent • 95% of the Northern Plane; • 159 milion sources (80% detected at >2 epochs); • photometric calibration consistent with SDSS at the level of 3%.
30. Single-band catalogues Band-merged catalogues Source catalogue Images Flag duplicate detections Cross-match Catalogue generation Quality control Reduction pipeline (Cambridge / CASU) Recalibrate
31. Isaac
32. Isaac
33. Let Δij be the magnitude offset between exposures i and j; minimise ∑∑(Δij + ZPi - ZPj)2 Photometric re-calibration Exploit the overlaps between exposures (cf. Glazebrook et al. 1994)
34. Let Δij be the magnitude offset between exposures i and j; minimise ∑∑(Δij + ZPi - ZPj)2 Photometric re-calibration Exploit the overlaps between exposures (cf. Glazebrook et al. 1994) Solve
35. Validation against SDSS Galactic Longitude Galactic Latitude Photometric residuals (IPHASr’ - SDSSr’) = 0.03
36. Secret weapon #!/usr/bin/env python import numpy import multiprocessing import astropy.io.fits import astropy.wcs cf. http:/ /github.com/barentsen/iphas-dr2
37. IPHAS source density near Cygnus Credit: Hywel Farnhill
38. Depth
39. Depth
40. Completeness
41. Scientific rationale
42. r’-i’ / r’-Hα diagram H-alpha emission is common for a wide range of rare objects. (Corradi et al. 2008)
43. r’-i’ / r’-Hα diagram 2005MNRAS.362..753D (Drew et al. 2005) Main sequence Reddening Breaks degeneracy between spectral type and reddening (e.g. Sale et al. 2009). Complement to Gaia: need to constrain extinction to get luminosities!
44. u’-g’ / g’-r’ diagram Credit: Michael Smith Powerful tool to reveal O/B-types and white dwarfs.
45. This talk 1. Introduction The IPHAS/UVEX/VPHAS surveys are mapping the entire Galactic Plane in visible light. 2. Scientific rationale These projects are a necessary counterpart to infrared surveys. Also a key complement to Gaia. 3. Demonstration Discovery and parameter inference of young stars using survey photometry.
46. Demonstration: T Tauri stars Shock emission (UV/optical excess) Hot inner disk (near-infrared excess) Warm dust & gas (infrared/radio) Hot gas; emission lines (including H-alpha) = young, solar-like stars; < 10 Myr; < 2 Msun
47. 2 degrees = 30 pc (d = 900 pc) IC 1396 in IPHAS Massive star (O6V)
48. 30”
49. Area investigated using spectroscopy (Sicilia-Aguilar et al. 2003, 2004) Area investigated using IPHAS (Barentsen et al. 2011)
50. r’-i’ / r’-Hα diagram
52. Image: Nick Risinger Observation bias: 88% of T Tauri stars known by SIMBAD are located at |b| > 5, where spectrocopic surveys are cheaper b = +5 b = -5 Orion
53. Different environments at larger distances 150 pc 1000 pc >> 1000 pc Taurus log N = 2 IC 1396 log N = 3 Tarantula Nebula log N = 6? Sun’s birth environment thought to be log N = 3-4? (Adams 2010)
54. Spectroscopy is the gold standard, but survey photometry is cheap & deep: • Readily available up to 20th mag; • Homogeneous: few biases between regions; • Narrow-band filters provide “a low-res spectrum”. => Use photometric surveys to analyse objects across environments in a homogeneous way.
55. NGC 2264 (Barentsen et al. 2013)
56. NGC 2264
57. T Tauri stars Goal Infer age/mass/extinction using r’ / i’ / J Infer accretion rate using H-alpha
58. T Tauri spectra ordered by accretion rate:
59. H-alpha luminosity traces the accretion luminosity
60. r’ - i’ / r - Ha diagram traces the H-alpha luminosity
61. H-alpha equivalent width can be quantified from the diagram AV = 5
62. ... albeit only if you know the extinction
63. Similarly, colour-magnitude diagrams trace ages and masses; but masses are also degenerate with extinction
64. Typical reddening law (Cardelli et al. 1989)
65. r’ - i’ / i’ -J diagram: extinction can be constrained for low-mass stars (Black line: NextGen model track)
67. Data Generative model Data Physics what you want: Physics what you know: Inference Parameter estimation
68. P(physics | data) ∝ P(data|physics) P(physics) P(data | physics) Bayes’ theorem “posterior” “likelihood” “prior”
69. Application Given IPHAS/UKIDSS photometry {r', Hα, i', J, σr', σHα, σi', σJ} we aim to constrain {extinction, mass, age, accretion rate } taking “nuisance parameters” into account uncertain distance (760 ± 5 pc) uncertain inner disc radius (5 ± 2 R*) uncertain log LHα ~ log Laccretion (±0.43 dex)
70. Application (cont.) We might assume that the model residuals are Gaussian, i.e.: P(data | physics) ∝ exp[ ∑(SEDmodel - SEDobs)2 / σ2 ] ... and assume a uniform prior: P(physics) ∝ 1 We want to know the parameter-space regions where the posterior is high... In this case, the peak correspond to a chi-squared fit.
71. So, chi-squared fitting is great?
72. Chi-squared fitting is usually not what you want! • Your model is rarely ever “just Gaussian”. There are often a bunch of nuisance parameters with known distributions. • Astronomical data is sparse and hence there is often a family of degenerate solutions. A maximum-likelihood fit does not capture this. • Generic solution: write down your posterior and compute its distribution in full.
73. Probabilistic Graphical Model (Barentsen et al. 2013)
74. MCMC Computing the posterior with “brute force” is often intractable. Instead, perform a “biased random walk” => Markov Chain Monte Carlo (MCMC) sampling methods
75. Example result Mass Extinction Mass Age
76. Conclusions • IPHAS/UVEX/VPHAS are mapping the entire Galactic Plane in u’, g’, r’, i’, H-alpha. • Get in touch if you would like to use the data, or keep an eye out for “IPHAS DR2”. • Take-away message: Python and Graphical Probabilistic Models are key tools for mining surveys.
77. Survey team Consortium: University of Hertfordshire (IPHAS/VPHAS PI) University of Nijmegen (UVEX PI) University of Cambridge (pipeline) University of Graz Other members: Instituto de Astrofísica de Canarias, Harvard/Smithsonian CfA, University College London, Imperial College London, University of Warwick, University of Manchester, University of Southampton, Armagh Observatory, Macquarie University, Tautenburg Observatory, ESTEC, University of Valencia. Key individuals: Janet Drew, Hywel Farnhill, Geert Barentsen, Robert Greimel, Mike Irwin, Eduardo Gonzalez-Solares, Romano Corradi, Paul Groot (UVEX lead), Danny Steeghs.