Models of the CO background via Measurements of the Cosmic Infrared - - PowerPoint PPT Presentation

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Models of the CO background via Measurements of the Cosmic Infrared - - PowerPoint PPT Presentation

Oct 19, 2023 114 likes •352 views

Models of the CO background via Measurements of the Cosmic Infrared Background Marco Viero KIPAC/Stanford w/ Lorenzo Moncelsi, Jason Sun (Caltech), Dongwoo Chung (KIPAC/Stanford) and the COMAP and TIME Collaborations 1 Motivation:

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Models of the CO background via Measurements of the Cosmic Infrared Background

Marco Viero — KIPAC/Stanford

w/ Lorenzo Moncelsi, Jason Sun (Caltech), Dongwoo Chung (KIPAC/Stanford) and the COMAP and TIME Collaborations

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marco.viero@stanford.edu Intensity Mapping Meeting — JHU — June 13 2017

Motivation: Typical CO Model Design

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Tony Li et al. 2016

Not all halos the same (assembly bias): Add scatter. Not all galaxies star-forming: Add scatter.

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marco.viero@stanford.edu Intensity Mapping Meeting — JHU — June 13 2017

Motivation: Typical CO Model Design

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Tony Li et al. 2016

Stellar Mass — M*

Use empirically derived LIR(z,M*)

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marco.viero@stanford.edu Intensity Mapping Meeting — JHU — June 13 2017

Challenge

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GOODS-S Half 1

da Cunha+2010

  • Infrared/Submillimeter

emission reprocessed starlight by dust

  • IR/Submm traces star

formation

  • Half the emission is

tied up in dust

  • Want to know:

➡what about the

Optical SED predicts the Thermal Infrared

LIR

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marco.viero@stanford.edu Intensity Mapping Meeting — JHU — June 13 2017

Optical v. Infrared Background

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250um z-band Challenge: Source Confusion

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marco.viero@stanford.edu Intensity Mapping Meeting — JHU — June 13 2017

Solution

Use:

  • The fact that intensity

fluctuations contain signal

  • Ancillary Data
  • Creativity + Statistics

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GOODS-S Half 1 GOODS-S Half 2

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Optical v. Infrared Background

  • Half the emission is tied up in dust
  • ad

SPIRE Contour

  • Difficult to attribute an individual

submillimeter “source” to any single galaxy

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Optical v. Infrared Background

  • Half the emission is tied up in dust
  • ad

SPIRE Contour

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  • Key is to identify galaxies with

similar physical properties, and then rely on statistics to fit fluctuations

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marco.viero@stanford.edu Intensity Mapping Meeting — JHU — June 13 2017

SIMSTACK: Simultaneous Stacking Algorithm

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SIMSTACK code publicly available (see arXiv:1304.0446): IDL (old) — https://web.stanford.edu/~viero/downloads.html Python — https://github.com/marcoviero/simstack

make hits map from catalog of similar objects convolve with instrument p.s.f. regress to find mean flux density, S

Formalism developed w/ Lorenzo Moncelsi (Caltech); also see Kurczynski & Gawiser (2010), Roseboom et al. (2010)

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marco.viero@stanford.edu Intensity Mapping Meeting — JHU — June 13 2017

SIMSTACK: Simultaneous Stacking Algorithm

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× S1 × S2 × SN

+ …+ +

≈

sky map

…

bin 1 bin 2 bin N ➜ ➜ ➜ Formalism developed w/ Lorenzo Moncelsi (Caltech)

SIMSTACK code publicly available (see arXiv:1304.0446): Python — https://github.com/marcoviero/simstack

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×S1 ×S2 ×SN

+ …+ + …

➜ ➜ ➜

M = 9.5-10 X Y 996 1009 55 1011 187 1010 501 1011 336 1012 127 1011 M = 10.5-11 X Y 345 1029 340 1029 517 1027 805 1031 805 1031 238 1032 359 1033 841 1034 M = 10-10.5 X Y 535 1026 345 1029 340 1029 517 1027 805 1031 805 1031

z=1.0 to 1.5

… … …

≈

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marco.viero@stanford.edu Intensity Mapping Meeting — JHU — June 13 2017

SIMSTACK: Flux Densities (M,z)

Data here: UDS catalog & Herschel SPIRE maps Each circle

  • ne bin

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marco.viero@stanford.edu Intensity Mapping Meeting — JHU — June 13 2017

Flux Density [mJy]

SIMSTACK: SEDs

Viero, Moncelsi, Quadri+ (2013) arXiv:1304.0446

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marco.viero@stanford.edu Intensity Mapping Meeting — JHU — June 13 2017

SIMSTACK: SEDs stellar mass slices redshift slices

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marco.viero@stanford.edu Intensity Mapping Meeting — JHU — June 13 2017

stellar mass slices redshift slices

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{

SIMSTACK: LIR(M,z,…)

  • Assuming only L(M,z),

i.e.; star-forming main sequence

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marco.viero@stanford.edu Intensity Mapping Meeting — JHU — June 13 2017

SIMSTACK: LIR(M,z,Av,Fagn)

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marco.viero@stanford.edu Intensity Mapping Meeting — JHU — June 13 2017

SIMSTACK: LIR(M,z,Av,Fagn)

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SPIRE 250um Simulated preliminary

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marco.viero@stanford.edu Intensity Mapping Meeting — JHU — June 13 2017

SIMSTACK: LIR(M,z,Av,Fagn)

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marco.viero@stanford.edu Intensity Mapping Meeting — JHU — June 13 2017

Applications

  • Signal

➡Connect to Halo properties (including assembly bias) to:

  • estimate CO levels,
  • construct covariances,
  • test different estimators (i.e., beyond power spectrum),
  • Details being discussed during this meeting!

➡Extend to other lines that correlate with thermal dust SED

  • CII, OII, OIII, NII
  • r.f. 850um as tracer of ISM Mass.
  • Foregrounds

➡Predict CO contamination in CII data cubes (e.g, Sun and the TIME

collaboration, 2017)

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marco.viero@stanford.edu Intensity Mapping Meeting — JHU — June 13 2017

Masking CO in CII line-intensity maps

  • Targeting CII at z = 6-10 means

separating signal from lower-z CO.

  • In deep fields (e.g., COSMOS,

UDS, GOODS), all potentially significant CO emitters (z=1-3) will be cataloged in the UV,

  • ptical, and NIR with great detail.

➡In these cases, we can construct

an estimator for CO from optical predictors of the mean LIR.

➡How much variance is there from

the mean, and how aggressively does masking need to be to play it safe?

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marco.viero@stanford.edu Intensity Mapping Meeting — JHU — June 13 2017

Masking CO in CII data: Sun et al. 2017

Variance in the LIR estimator determined by comparing scatter in the difference map with simulations.

  • Find sigma = 0.33

Sun, Moncelsi, Viero & TIME collaboration 2017, arXiv:1610.10095

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Guaocho (Jason) Sun Lorenzo Moncelsi

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marco.viero@stanford.edu Intensity Mapping Meeting — JHU — June 13 2017

Masking CO in CII data: Sun et al. 2017

Sun, Moncelsi, Viero & TIME collaboration 2017, arXiv:1610.10095

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marco.viero@stanford.edu Intensity Mapping Meeting — JHU — June 13 2017

Summary

  • CIB continuum intensities are key to empirically connecting
  • ptical features of typical galaxies to their FIR/submm

components

  • Applications for this model include:

➡Forecasting CO power for:

  • Survey design
  • Covariance construction
  • Testing Estimators
  • Measurement Interpretation
  • For this workshop, would like to…

➡Determine how best to populate halos ➡Explore Estimators

  • SIMSTACK is easy to use, and available at:

➡https://github.com/marcoviero/simstack

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