The Euclid view on galaxy and AGN evolution

Coordinators: Jarle Brinchmann (Leiden) David Elbaz (CEA), Gianni Zamorani (Bologna) Elena Zucca, Sandro Bardelli, Olga Cucciati, Gabriella De Lucia, Olivier Ilbert, Julien Zoubian, Giovanni Cresci, Thierry Contini, Filippo Mannucci, Alessandro Marconi, Guido Risaliti, Mario Radovich, Alessandro Pizzella, Lucia Pozzetti, Michele Moresco, Andrea Cimatti, Michele Cirasuolo, Emanuele Daddi, Helena Domínguez Sanchez, Chris Conselice, Olivier Le Fevre, David Clements, Nathan Roche, Manuela Magliocchetti, Peder Norberg, Pascale Jablonka, Dave Bonfield, Carlotta Gruppioni, Matt Jarvis, Stephen Serjeant, Luigi Spinoglio, Cristian Vignali, Benjamin Joachimi, Mara Salvato, Dan Smith, Fabio Fontanot, ++++ HighThe strengths quality imaging of Euclid  formorphology the study offor galaxy 2 billion and galaxies AGN evolution  morphology, mergers , environment effects  role of mergers vs cold-flow induced dynamical instabilities vs non-disturbed morphologies  role of mergers in SFR / AGN activity Large area/vol  multi-parameters (SFR, M*, Z) & DM halo  origin of scaling laws: SFR-M*, mass-metallicity, SFR-M*-Z  correlation functions : DM halo vs light, tracing back the progenitors of today’s galaxies  rare objects  statistics of red-dead galaxies (quenching ?)

NIR images  M*, red sequence & photo-z  determination of stellar mass  photometric  identification of red-dead massive distant galaxies as a constrain on gal. formation theory  identify dense environments NIR spectroscopy  1

Environment  how do galaxy properties depend on their surroundings ? Galaxy mergers Galaxy groups, clusters and filaments Galaxy clustering

The properties of galaxies  what is the make-up of galaxies at z > 1 ? rates The multi-dimensional nature of galaxies (stellar mass, luminosity, metallicity, colour, morphology, SFRs, ...) The relationship between dark and luminous matter.

Galaxies & AGNs  co-evolution of stars and black holes Tracing obscured AGN to high-z Inventory of nuclear activity and the relationship to galaxy properties The strengths of Euclid for the study of galaxy and AGN evolution High quality imaging  2 billion galaxies with morphological information Spatial resolution > 5-10 times better than ground based wide-field imaging FWHM~0.16"  1.3 kpc resolution at ~all z 10 As compared to e.g. the ½-light radius of a 5x10 M at z~2 of 3-4 kpc “M51”: SDSS @ z=0.1 Euclid @ z=0.1 Euclid @ z=0.7

Euclid images of z~1 galaxies will have the same resolution as SDSS images at z~0.05 and be at least 3 magnitudes deeper.

 role of mergers vs cold-flow induced dynamical instabilities vs non-disturbed morphologies Galaxy mergers A crucial ingredient in any hierarchical Yet theoretical models differ by an order of magnitude in their predictions - the main obstacle: Baryon physics (Hopkins et al 2010) How efficient are mergers intriggering star formation ?

Current surveys (zCOSMOS, VVDS, etc)are too small to help constrain the physics.

Euclid will: Increase the sample size of spectroscopically known mergers by ~3-4 orders of magnitude (much more when combined with photo-zs) Provide high-resolution imaging which is invaluable in understanding the nature of these systems. Provide physical properties for the systems by combining spectroscopic and photometric information. Allow us to study mergers as a function of environment, mass, nuclear activity, ... Scaling laws : SFR-M*, mass-metallicity, SFR-M*-Z multi-parameter require large statistics to differentiate the relative roles of mass / environment / metallicity / morphology / mergers in the triggering / quenching of star formation SFR-M* : observed at z=0  high-z Mass - metallicity Arp220 z=0

high-z

M82 MW

Elbaz +07 Mannucci +10

Elbaz +11 ααα SFR from H ααα (6563Å, 0.4

OH atmospheric lines Slitless spectroscopy : relative roles of ( SFR, M*, environment) in galaxy mass assembly

~10 6 per ∆z=0.1 in z spectroscopic (>10 7 with phot-z’s) 50 million redshifts (completeness >45%) >> SDSS : ~10 6 spectra in total -16 -1 -2 > 3x10 erg s cm  Euclid= 1 SDSS / ∆z=0.1

Based on Geach et al (2010) Slitless spectroscopy : relative roles of ( SFR, M*, environment) in galaxy mass assembly

~10 6 per ∆z=0.1 in z spectroscopic (>10 7 with phot-z’s) 50 million redshifts (completeness >45%) >> SDSS : ~10 6 spectra in total -16 -1 -2 > 3x10 erg s cm  Euclid= 1 SDSS / ∆z=0.1

Based on Geach et al (2010)

Few % are AGNs, ~1 million

50% type 1 and type 2 Slitless spectroscopy : relative roles of ( SFR, M*, environment) in galaxy mass assembly

50 million redshifts (completeness >45%) Galaxy evolution in very narrow bins -16 -1 -2 > 3x10 erg s cm of mass, type, star-formation and environment  main mechanisms of galaxy mass assembly Based on Geach et al (2010) > 10 -16 erg s -1 cm -2

> 2x10 -16 erg s -1 cm -2

SFR > 5x10 -16 erg s -1 cm -2 Passive galaxies

The typical formation of massive galaxies/spheroids is at z~2 and above.

Finding the most massive, passive galaxies at high-z offers strong constraints on galaxy evolution theory but is challenging : 2 11 ~1 gal/deg M*>4x10 M  spectroscopically identified by Euclid at z>1.8 (redshifted D4000)

Moresco et al (2010)

Need very large areas with deep NIR imaging ⇒ Euclid is perfectly suited, ground-based facilities can not compete. Finding dwarf galaxies

Euclid will be exceptionally well suited to detect low surface brightness dwarfs

Courtesy Ivan Baldry (independent mission scientist) Euclid legacy in numbers What Euclid Before Euclid

Galaxies at 1

Massive galaxies (11

Type 2 AGN (0.7

Dwarf galaxies ~10 5

2 Teff ~400K Y dwarfs ~few 10 <10

Strongly lensed galaxy-scale ~300,000 ~10-100 lenses

z > 8 QSOs ~30 None In summary The large number of sources allows us to study the evolution of distribution functions in detail: Euclid= 1 SDSS / ∆z=0.1 Multi-dimensional distribution functions. Probe volume ~20 Gpc 3 at z~2 ±0.05 [SDSS out to z~0.2 probes ~0.3 Gpc 3], thus reducing the impact of cosmic variance and will be very efficient to find rare objects .

SFR, M*, metallicity, ionization properties, environment etc. - all with ~WFPC-2 class imaging.

• Correlation functions can be used to trace the dark matter halo mass  trace the progenitors of present- day galaxies and study their morphology. An SDSS survey for the 1