Science with synthetic stellar surveys Robyn Sanderson Caltech - - PowerPoint PPT Presentation

Science with 
 synthetic stellar surveys

Robyn Sanderson Caltech ➤ UPenn/Flatiron CCA

OMG I’m on Twitter!? @astrorobyn

Science with 
 synthetic stellar surveys

Robyn Sanderson Caltech ➤ UPenn/Flatiron CCA

Synthetic survey of a cosmo-hydro simulation (Sanderson et al 2018) Milky Way (image credit:ESO)

OMG I’m on Twitter!? @astrorobyn

M31$

N185$ N147$

M33$

30$kpc$

90$kpc$ 150$kpc$

60$kpc$

N$ E$ M31$dSphs$

Giant$Southern$ Stream$ M31$Halo$Fields$

PAndAS$M31$Map$ (McConnachie$et$al.)$

Dwarf$Galaxy$Fields$

image courtesy Karoline Gilbert

150 kpc

(SEGUE K giants, Xue+2014; PanSTARRS RR Lyr, Sesar+2017)

35 kpc

(MSTO stars, Sesar+2011)

85 kpc

(F stars, Pila-Diéz+2015)

300 kpc

(Rvir?)

The Milky Way (and M31) in 2018

274 kpc

(Most distant M giant, Bochanski, Willman, Caldwell, RES+2014)

2017 2018 2019 2020 2021 2022 2023 2024 2025 LSST Subaru PFS Gaia Ext WFIRST 4MOST DESI WEAVE GMT TMT SDSS-V

The next decade will see a Galactic renaissance1

Astrometric + spectroscopic Photometric + astrometric Spectroscopic: <4-m class Spectroscopic: >4-m class

• 1E. Kirby, 2017

By 2028, we will have 6+D information for stars to the MW’s virial radius and beyond (~300 kpc)… ..and resolved stellar maps of the ~100 nearest MW-like galaxies

Euclid

300 kpc (=Rvir?) 150 kpc (extent of current samples) LSST coadded depth (m=26.7) MSTO stars BHB stars M giants, RRLe

5 Mpc

The Milky Way in 2028

Latte (m21i), Wetzel et al. 2016

Wetzel+2016 4MOST; De Jong 2011 (AAO) for FMOS (Akiyama et al., 2008) LAMOST) Telescope (Hu et al., 2004). Effjcient full-sky surveying requires at multiplex of > 3000 to create a unique, world-class facility. Most fjbres will lead fjbres will permanently go to a spectro a number of trade-off studies to fjnd the

05 14 12 10 8 6 4 2 –2 –4 –6 –8 –10 B5 10 M☉ star 5 M☉ star Sun M a i n s e q u e n c e (V ) White dwarfs (WD) A0 F0 G0 K0 M0 4M0ST 100 pc 1 kpc 10 kpc 100 kpc 10 kpc 1 kpc 100 pc Gaia 1 Mpc Absolute magnitud (V–band) Effective temperature (K) 30 000 10 000 7000 6000 4000 Supergiants (I) Giants (II, III) AGB AGB - Asymptotic Giant Branch HB HB - Horizontal Branch RGB - Red Giant Branch RGB Helium flash Limiting distance Spectral class Left: The 4MOST goal for radial velocity limits of 4MOST to the astrometric limits of Gaia, 4MOST (black horizontal) overlaid on a Hertzsprung– Russell diagram. 4MOST can measure Sun-like stars to nearly the centre of the Milky Way, RGB stars to Group, substantially expanding on Gaia’s spectro scopic view. Distance limits for the 4MOST high res

The Milky Way in 2028: spectroscopy

but…what do we DO with all this data?

Babusiaux et al 2018 Gaia Collaboration Wetzel et al. 2016, movie credit: Phil Hopkins

Figure courtesy Andrew Wetzel Latte Simulation: arXiv:1602.05957

300 kpc (=Rvir?) 150 kpc (extent of current samples) LSST coadded depth (m=26.7) MSTO stars BHB stars M giants, RRLe

5 Mpc

The Milky Way in 2028

100 kpc

Cooper+2010, Helmi+2011

The accreted stellar halo is clumpy in constants-of-motion space

Galactic coordinates

Sanderson, Helmi, & Hogg 2015 Sanderson et al. 2017a

Constants of motion (using best-fit mass model for host galaxy)

Radial action (~energy) Z angular momentum

One particle = many stars

Radial action (~energy) Z angular momentum

Sanderson et al. 2017a

Best fit mass profile

The stellar halo constrains the MW's gravitational potential

Distances to stars used in fit

The choice of stellar tracers matters

K giants RR Lyrae

Sanderson 2016 Sanderson et al. 2017b

K giants RR Lyrae

20% “photometric distances” 5% standard candles

A route to untangling the stellar halo

Sanderson et al. 2017c

One particle = many stars

Making predictions for a 6+D galaxy

Galaxy Simulation (cosmology, DM model, gravity, gas physics, star formation, stellar feedback, …) Survey description (Magnitude/color limits, extinction/reddening, selection function, error model, instrument model, …)

Mock Catalog

• ne particle =
• ne synthetic star

Synthetic Survey

• ne particle = one “observed” star

Phase-space density estimation 
 (kernel dimension, smoothing scales, ages, accretion history, …) Stellar Populations (stellar structure, stellar evolution, convection models, isochrone mapping, IMF, …)

One particle = many “stars” …with same age, abundances

girder.hub.yt irsa.ipac.caltech.edu

Available for Gaia DR2 on: Ananke


Sanderson et al. 2018, 
 arXiv:1806.10564

• Cosmological sim with hydro —> realistic central MW
• 6D + 10 abundances + ages + …
• Complete stellar populations

Andrew Wetzel Sarah Loebman Sanjib Sharma

Ananke


Sanderson et al. 2018
 arXiv:1806.10564

• Cosmological sim with hydro —> realistic central MW
• 6D + 10 abundances + ages + …
• Complete stellar populations

girder.hub.yt irsa.ipac.caltech.edu

Available for Gaia DR2 on:

Babusiaux et al 2018 Mock Gaia Real Gaia Stars with:

• 10% or better parallax uncertainty
• extinction < 0.015 mag
• G mag uncertainty <0.22 mag
• GBp, GRp uncertainty < 0.054 mag

Ananke


Sanderson et al. 2018
 arXiv:1806.10564

• Cosmological sim with hydro —> realistic central MW
• 6D + 10 abundances + ages + …
• Complete stellar populations

girder.hub.yt irsa.ipac.caltech.edu

Available for Gaia DR2 on:

Mock Gaia Real Gaia m12i-lsr0 m12f-lsr2 Stars with:

• 10% or better parallax uncertainty
• extinction < 0.015 mag
• G mag uncertainty <0.22 mag
• GBp, GRp uncertainty < 0.054 mag

• Validate your Gaia analysis technique


(matching columns have same names)

• Train/interpret your machine-learning model
• Test & tune [some] search algorithms
• Investigate cosmic & viewpoint variance

Things to do with ananke

Things NOT to do with ananke

• Estimate backgrounds/foregrounds


(stellar mass/density are not MW calibrated)

• Study the edges of the selection function


(the built-in error model is way too simple)

• Estimate completeness


(crowding not treated, extinction function not MW)