Asteroid color photometry with Gaia and synergies with other space - PowerPoint PPT Presentation

Asteroid color photometry with Gaia and synergies with other space missions Marco Delbo UNS-CNRS-Observatoire de la Cˆ ote d’Azur & Gaia DPAC Coordination Unit 4 (Solar System Objects) May 23, 2011 Pisa, Italy

Collaborators Philippe Bendojya (UNS-CNRS-Observatoire de la Cˆ ote d’Azur) Antony Brown (Leiden Observatory, the Netherlands) Giorgia Busso (Leiden Observatory, the Netherlands) Alberto Cellino (INAF-Osservatorio Astronomico di Torino, Italy) Laurent Galluccio (UNS-CNRS-Observatoire de la Cˆ ote d’Azur) Julie Gayon-Markt (UNS-CNRS-Observatoire de la Cˆ ote d’Azur) Christophe Ordenovic (UNS-CNRS-Observatoire de la Cˆ ote d’Azur) Paola Sartoretti (Observatoire de Paris) Paolo Tanga (UNS-CNRS-Observatoire de la Cˆ ote d’Azur)

Outline of the presentation Introduction Asteroid spectral classes and mineralogy Modern CCD based asteroid spectroscopy and its limitations Asteroid spectral classification using Gaia The BP-RP photometers on board of Gaia Expected peformances of the BP-RP Data products for asteroid color photometry Asteroid spectral classification algorithm Unsupervised clustering algorithm for asteroid spectral classification Combination of Gaia photometry with AUXILIARY Data

Asteroid spectral types Asteroids are assigned a type based on spectral shape. These types are thought to correspond to an asteroid’s surface composition. Bus and Binzel spectral types: 1.2 S-type X-type ◮ C-group (carbonaceus) with a 1.15 B-type C-type S 1.1 featureless spectrum 1.05 X ◮ B-type (featureless and blue) Reflec C 1 tance B ◮ S-group (stony) with silicate 0.95 0.9 absorption bands 0.85 ◮ X-group of mostly metallic 0.8 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 objects including Wavelength (microns) enstatite-chondrite like spectra from the SMASS web site, by R. Binzel and collaborators

Modern CCD based asteroid spectroscopy DeMeo et al., 2009 Bus and Binzel 2002 ◮ However, all spectra do not go shortwards 450nm. ◮ Most available data in the blue region (340-550nm) are very poor in quality.

Sloan Digital Sky Survey (SDSS); Parker et al. (08) colors SDSS: color photometry of more than 100,000 asteroids. Example from the SDSS Moving Object Catalog 4 (MOC4). 1.2 S-type 1.1 1 Reflectivity 0.9 0.8 0.7 0.6 0.5 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Wavelength (um) bands: u’:354, g’:477, r’:623, i’:763, z’:913 (mn) with a ∗ = 0 . 89( g ′ − r ′ ) + 0 . 45( r ′ − i ′ ) − 0 . 57.

Asteroid spectral classes and mineralogy of the main belt ◮ Investigation of the mineralogy of families. ◮ Comparison of spectra of NEAs with those of families near the NEA source regions... with the help of dynamical models; see e.g. De Leon et al. 2010; Campins et al. 2010; Jenniskens et al. 2010; Walsh et al. 2011; Gayon-Markt et al. 2011; etc..)

The photometers on the focal plane of Gaia 1 pixel 60 x 180 mas 106 CCDs (4.5 x 2 kpix) = 1 Gpixel SM1-2 BP RP AF1 - 9 RVS 420 mm 0.69° WFS WFS BAM BAM sec FOV1 0s 10.6 15.5 30.1 49.5 56.3 64.1 sec FOV2 0s 5.8 10.7 25.3 44.7 51.5 59.3 disclaimer: in the Gaia community, BP-RP data is called color phometry; it is low resolution ( R ∼ 20 − 90) slit-less spectroscopy, though.

λ The photometers: resolving power ( R = ∆ λ ) ◮ Sampling is such to have about 18 independent bands in the BP-RP domain (A. Brown, spring 2011) R~20 ◮ Sampling is 60 pixel per photometer, signal is in general contained in 40 pixel per photometer. ◮ Telescope PSF FWHM is about 2 R~70 pixels AL (40/2 ∼ 20 independent bands) and 1 pixel AC. R~90 R~70 ◮ 80% of asteroid observations have velocities ≤ 15 mas/s. Beacuse a CCD transit lasts 4 s → ≤ 1 pixel widening of the PSF: this is not too bad.

BP-RP response for point like sources G=15 point source with different colors. sample sample 40 30 20 10 0 60 50 40 30 20 10 0 3.0 3.0 BP counts (10 3 photons) RP counts (10 3 photons) 2.5 2.5 2.0 2.0 1.5 1.5 1.0 1.0 0.5 0.5 0.0 0.0 400 500 680 900 640 700 800 9001000 ! (nm) ! (nm) G2V star is the middle green curve. Credits: Busso, G. & Brown, A. 2009

BP-RP SNR for an asteroid with G=17 14 BP RP 12 10 8 SNR 6 4 2 0 300 400 500 600 700 800 900 1000 1100 Wavelength (nm) � Photon Noise limited in general. So SNR= N photons

BP-RP SNR as function of magnitude (1 transit) 1000 Min SNR Peak SNR SNR [400-1000] nm 100 10 1 10 12 14 16 18 20 G magnitude ◮ Minimum and Peak SNR in the range 400-1000 nm per transit. ◮ Best fit to min SNR: SNR=17631 × 10 − 0 . 201317 ∗ G

Average SNR for BP-RP at the end of mission 10000 Min SNR Peak SNR SNR [400-1000] nm 1000 ◮ The large majority of asteroids (main belt) are observed at least 60 times [Mignard, F. 2001 100 (SAGFM09)] ◮ The SNR of the accumulated (avarage spectrum) is 8 times 10 larger 10 12 14 16 18 20 G magnitude Minimum SNR at the end of the mission assuming 50 transit/asteroid For asteroids with G=19-20 spectral classification will be difficult. Solution:?!: Spectral binning.

Spectral Shape Coefficients: 8-colors asteroid survey 160 BP RP 140 BP-SSC RP-SSC 120 Signal (e - /transit) ◮ Spectral Shape Coefficients (SSCs; 100 4 for BP and 4 for RP; 8 colors for 80 each source) are calculated by 60 IDT (Initial Data Treatment) . 40 ◮ SSCs calculated also by PhotPipe and refined at every cycle. 20 ◮ Potentially very interesting for 0 300 400 500 600 700 800 900 1000 1100 performing an 8-colors asteroid Wavelength (nm) survey. Example of SSC values calculated for a BP-RP signal of a G=20 asteroid (solar-like spectrum).

Data products for asteroid color photometry ◮ The spectral energy distribution (SED) is obtained from accumulated BP-RP data. ◮ Average SEDs is produced (1 per asteroid). ◮ Epoch SEDs is also produced where possible (for SNR ≥ 20 per transit ∼ G ≤ 15). ◮ Smearing due to proper motion is taken into account. ◮ Asteroid reflectivity is calculated from the SED. ◮ BP and RP SEDs are combined into one SED. ◮ The SED is divided by the solar spectrum and the results normalized at 0.55 microns. ◮ The asteroid reflectivity is used to determine the asteroid spectral class. ◮ Unsupervised clustering algorithm. ◮ Comparison with other classifications (e.g. Bus & Binzel).

Clustering method based on Minimal Spanning Tree (MST) Example of MST in R 2 !"# Galluccio et al. (2008 ) !"$ Method for partitioning a set V of N data !"% !"& points ( V ∈ R L ) into K non-overlapping !"' clusters with: ! ! !"' ◮ the inter-cluster variance is maximized; ! !"& ! !"% ◮ the intra-cluster variance is minimized. ! !"$ ! !"# ! !"# ! !"# Identification of the number of clusters: !"!&# ◮ The lenght of the edge at each addition of !"!& a vertex of the MST is recorded. !"!'# ◮ Then by identifying valleys in this curve, !"!' we can estimate the number and positions !"!!# of high density regions of points → i.e. ! the clusters. ! #! '!! '#! &!! &#! %!! %#! $!!

Test of the classification algorithm ◮ Spectra of asteroids belonging to all spectral classes were obtained at the Telescopio Nazionale Galileo (TNG) under Gaia-like observing geometry. PI Paolo Tanga; Data analysis in progress. photo credits: P. Tanga ◮ See next talk by Julie Gayon-Markt.

Removal of spectral classification degeneracies There are some well known degeneracies in the mineralogical But asteroids (46) Hestia, (55) interpretation of asteroid spectral Pandora, and (317) Roxane have classes. different albedos. For instance, asteroids (46) 1.2 (55) Pandora - M-type Hestia, (55) Pandora, and (317) (46) Hestia - P-type (317) Roxane - E-type Reflectance Normalized to the albedo 1 Roxane have very similar spectra. 0.8 1.2 0.6 1 0.4 Normalized Reflectance 0.8 0.2 0.6 0 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Wavelength (um) 0.4 0.2 Albedo + spectra → removal of (55) Pandora - M-type (46) Hestia - P-type (317) Roxane - E-type 0 spectral class degeneracies. 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Wavelength (um)

Asteroid spectral classification (Gaia + WISE data) ◮ NASA WISE has observed 100,000 asteroids in the thermal IR. ◮ Albedos will be obtained from WISE data. ◮ First data (IR images) already released. ◮ Albedo + spectra → removal of spectral class degeneracies. ◮ Albedo and spectra can be classified using our non supervised classification algorithm.

ExploreNEOs with Warm Spitzer: PI D. Trilling (NAU) Albedos from Warm Spitzer 1.0 0.8 0.6 e 0.4 Albedo (%) 0.2 0 5 10 15 20 25 30 0.0 0.5 1.0 1.5 2.0 2.5 3.0 a (AU)

Conclusions DPAC products (from Gaia observations only): ◮ Gaia will obtain R ∼ 20 − 90 visible spectra of asteroids. ◮ Average spectra (reflectancies) will be published. ◮ Epoch spectra for the brighter asteroids. ◮ Spectral classes of asteroids will be also published. Gaia + Auxiliary data (e.g. WISE albedos): ◮ Albedo from WISE or Spitzer will allow spectral classes degeneracies to be removed → mineralogical map of the main belt.