Crurent and Future Massive Redshift Surveys - LASTRO Light on the Dark - ISDC
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Galaxy Power Spectrum Size of the Horizon: mass-radiation equilibrium 3D mapping of the position of Dark Energy: galaxies non-gaussian initial fluctuations Baryonic Acoustic Oscillation Dark Matter Warm/ Cold Neutrinos Masses Galaxy halo occupation Growth of distribution (HOD) structure Distribution of galaxies (SDSS) EPFL/LASTRO - J.-P. Kneib 2
Neutrinos impact on LSS How sum of neutrino masses affect the density field 28 10 29 10 Density (g/cm3) 30 10 31 10 X X m⌫ = 0 eV m⌫ = 1eV eV EPFL/LASTRO - J.-P. Kneib Figure Credit: Agarwal & Feldman 3
Broad BandPower Broadband Power Spectrum Spectrum 2025 • Redshig Space DistorPons: Since P is smaller on scales of interest, noise (nP)-1 is larger and geqng greater density than DESI would pay off. • Different tracers might miPgate cosmic variances. • Higher density tracers might trace the dark maBer more efficiently • Might get more objects at lower S/N by exploiPng Future projects: photo-z’s MSE, SSSI (DOE), EPFL/LASTRO - J.-P. Kneib China, ESO 4
Massive Redshift Surveys • Hubble (1930): expanding Universe • CfA Redshift Survey (1985): first large scale structures • 2dF (2000-6): 1500 deg2 • SDSS (2002-9): 5700 deg2 • zCOSMOS (2005-10): 2 deg2 MOONS • WiggleZ (2011): 800 deg2 (BAO) DESI • SDSS-III/BOSS (2014): 10,000 deg2 eBOSS BAO/LSS (BAO) • e-BOSS (2014-2020): BAO/LSS: 7,500deg2 w/ LRG+QSO & 1,500deg2 of ELGs • DESI (2019), 4MOST (2021) Optical • MOONS (2020) Infrared • Euclid (2020) Space mission • SKA (2025) Radio Telescope • future projects ... EPFL/LASTRO - J.-P. Kneib 5
The Sloan Telescope & Spectrograph • 90 cm aluminium plate with 1000 holes for fibers, • 45 min to plug for typical 1 hour observation on sky • up to 9 plates observed per (good) night • 1.5 millions redshifts in ~4 years • The best multiplexing spectroscopic facility still EPFL/LASTRO - J.-P. Kneib 6
eBOSS (2014-2020) • eBOSS = cosmology survey of SDSS-IV ★ Fully funded for 5yr (likely 6) ★ 1/4 of the telescope time ★ Uses BOSS spectrograph, targets from SDSS+WISE imaging ★ ~150 participating scientists • extend BOSS to unexplored redshift window 0.6
DESI (2019-2024) 5000 fiber actuators New 3° field-of-view corrector 10 New spectrographs Mayall 4m Telescope 35 Millions redshifts • 30 4x4k detectors Most of them will be ELGs •100 Tb/year •BAO at sub-% level •Complex data/algo •Gravity •Neutrino Masses •Inflationary model EPFL/LASTRO - J.-P. Kneib 9
Euclid (2020-2025) • Euclid is a major wide-field imaging and spectroscopy space mission, lead by ESA (+NASA participation). Launched in 2020. • Strong implication of Swiss Astronomers (UniGe, EPFL, UniZH, FHNW). • 15’000 sq.deg to be covered with space-images: 260 Gb/day; ~100 Tb/ year. • Euclid data will also included other ground-based images/spectroscopy (including LSST) ~10 Pb/year. • Switzerland will host one of the Euclid Data Centre. • Pierre Dubath presentation for details. EPFL/LASTRO - J.-P. Kneib 10
4MOST (2021-2026) ! Cosmology!and!galaxy!evolu0on • Approved by ESO council - Southern Sky ! Euclid spectroscopic survey to start in 2021 • Complementary to DESI in the North ! High4energy!sky ' eROSITA • Complementary to Euclid (focus on 1
Number of spectra Number of spectra 4MOST DESI Euclid BOSS eBOSS SDSSI/II WiggleZ 2MRS CFA Year experiment finishes Shirley Ho EPFL/LASTRO - J.-P. Kneib 12
DESI Data Needs • Example for DESI (4MOST/Euclid-spectro will be similar) ★ Data Volume (2016-2023): ✦ DESI Imaging Data: 850 Tb ✦ DESI Data: 800 Tb => 100 Tb (once data reduced) ✦ Simulations (for analysis): 150 Tb ✦ Total: 1’800 Tb ~ 2 Pb => 1 Pb (once reduced) ★ CPU hours: ✦ operation 2019-2023 : 10-20 millions CPU-hours/year (mostly for main pipeline 2D images => spectra - data analysis/mining will likely be less) ✦ simulations ~20 millions CPU-hours • DESI approach is to use NERSC supercomputer facility (not distributed) EPFL/LASTRO - J.-P. Kneib 13
EPFL/LASTRO - J.-P. Kneib 14
Example of Data Usage • Computing 1D and 2D correlation function (number of pairs of galaxies separated by a distance of ‘r’ as a function of ‘r’) on data and on mocks (complexity: handling selection function and completness information) • Full mocks, including simulated observation strategy (100s of mocks compare to 1 set of cosmological data) • Extracting information from spectra & other data: ★ double objects spectra (lenses, AGN and SN signatures) - mining spectra ★ variability spectra - comparison across database ★ combining spectra information with imaging/lensing - combining data information across database EPFL/LASTRO - J.-P. Kneib 15
EPFL/LASTRO - J.-P. Kneib 16
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