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)
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
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.
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.)
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.
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.
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%.
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)
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
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!
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.
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
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)
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.
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.
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.
MCMC Computing the posterior with “brute force” is often intractable. Instead, perform a “biased random walk” => Markov Chain Monte Carlo (MCMC) sampling methods
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.
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.
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