# Presentation: 642138

## Optimal Photometry of Faint Galaxies

- Kenneth M. Lanzetta
- Stony Brook University

## Collaborator:

- Stefan Gromoll (Stony Brook University)

## Outline

- scientific motivation
- data
- photometric redshift technique
- optimal photometry and photometric redshifts of faint galaxies

## Cosmic chemical evolution

- We are interested in the quantities of cosmic chemical evolution...
- Ω*g* (damped Ly absorbers)
** (rest-frame ultraviolet, H emission)
*Z* (damped Ly absorbers)
- Ω*s* (rest-frame near-infrared emission)
- ...which are the quantities of galactic chemical evolution averaged over cosmic volumes

## Comoving mass density of gas

- Compiled by Rao et al. 2005

## Comoving star formation rate density

- Compiled by Lanzetta et al. 2003

## Cosmic metallicity

- Compiled by Prochaska et al. 2004

## Outstanding issues

- very limited statistics
- cosmic variance
- selection biases
- damped Ly absorbers:  obscuration by dust of QSOs behind high-column-density absorbers
- ultraviolet emission:  dust extinction, cosmological surface brightness dimming

## Equations of cosmic chemical evolution

## Comoving mass density of stars

- existing surveys target very large numbers of galaxies (statistics) across many fields (cosmic variance)
- measurement is based upon rest-frame near-infrared emission (dust extinction)
*objective:  determine the comoving mass density of stars versus cosmic epoch with the accuracy needed to obtain a statistically meaningful time derivative*

## Our program

- measure optimal photometry (at observed-frame near-ultraviolet through mid-infrared wavelengths) and photometric redshifts of faint galaxies in GOODS and SWIRE surveys
- use rest-frame near-infrared luminosities and rest-frame optical and near-infrared colors to estimate stellar mass densities
- construct comoving mass density of stars versus cosmic epoch

## GOODS survey

- two fields spanning 320 arcmin2
- Spitzer IRAC images at 3.6, 4.5, 5.8, and 8.0 μm and MIPS images at 24 μm
- HST and ground-based images at observed-frame optical and near-infrared wavelengths
- roughly 10,000 IRAC images and 10,000 MIPS images
- roughly 200,000 galaxies at *z* ≈ 0 – 6

## SWIRE survey

- six fields spanning 49 deg2
- Spitzer IRAC images at 3.6, 4.5, 5.8, and 8.0 μm and MIPS images at 24, 70, and 160 μm
- ground-based images at observed-frame optical wavelengths
- roughly 100,000 IRAC images and 500,000 MIPS images
- roughly 8,000,000 galaxies at *z* ≈ 0 – 2

## Why the measurement is difficult

- characteristic scale of high-redshift galaxies:  0.1 arcsec
- characteristic scale of Spitzer PSF:  2.5 arcsec (or larger at longer wavelengths)
- Spitzer images are undersampled
- almost all galaxies overlap other galaxies
- how to measure faint galaxies that overlap bright galaxies?

## Slide 15

## Photometric redshift technique

- Determine redshifts by comparing measured and modeled broad-band photometry
- Six galaxy spectrophotometric templates
- Effects of intrinsic (Lyman limit) and intervening (Lyman-alpha forest and Lyman limit) absorption
- Redshift likelihood functions
- Demonstrated accurate (∆*z */ (1 + *z*) < 6%) and reliable (no outliers) at redshifts *z = *0 through 6

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## “Redshift spatial profile fitting” technique

- “deconvolve” a sequence of “source” images (typically higher-resolution images at optical wavelengths) to obtain photometric redshifts and spatial models of galaxies
- use spatial models to fit for energy fluxes in a sequence of “target” images (typically lower-resolution images at near- or mid-infrared wavelengths)

## Slide 21

## “Deconvolving” source images

- build one spatial model image on a fine pixel scale
- relate spatial model image to each data image via geometric transformation, convolution, and scaling by spectral templates on a galaxy-by-galaxy basis
- simultaneously determine spatial models and photometric redshifts

## Fitting target images

- do not “add” target images (undersampling, correlated noise)
- instead, relate spatial model image to each data image via geometric transformation, convolution, and scaling by unknown energy flux on a galaxy-by-galaxy basis
- determine energy fluxes

## Slide 24

## Computational requirement

- each step of “deconvolving” or fitting requires transformation and convolution of the spatial model image to each data image
- registration of each data image must be fitted for as part of the process
- since there are a lot of data images, this is computationally very expensive

## Computer setup

- 50 Xeon 3.06 GHz processors (donated by Intel Corporation)
- 20 cluster nodes, four workstations, one file server
- two Itanium 1.4 GHz processors (donated by Ion Computers)
- one database server
- 2 TB disk storage, 10 TB local disk caches
- custom job control and database software

## Slide 27

## What is needed to measure faint galaxies in deep Spitzer images

- accurate image alignment
- geometric distortion, registration
- better than 0.01 pixel
- accurate spatial models
- deconvolution of source images
- convolution of target images
- “color segmentation”
- segment galaxy profiles by color

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## Image alignment

- Geometric distortion and registration––how to calibrate?
- S/N = 500 for a typical SST “pixel”
- Required image alignment accuracy better than 0.01 pixel
*More or less solved*

## Noise in source images

- S/N = 500 for a typical SST pixel
- S/N = 200 over a comparable region of sky for ACS
*noise in source images is the limiting systematic effect in measuring SST images*
*SST images cannot be measured to within noise given current ACS images*

## Slide 47

## Summary

- We believe that faint galaxies can be measured in deep Spitzer images only with...
- ...accurate spatial models (alignment, deconvolution and convolution, color segmentation)...
- ...and computational expense