# Presentation: 637733

## SNAP Monte Carlos

**Alex Kim **
**Lawrence Berkeley National Lab**
**July 2002**
**R. Amanullah (Stockholm), L. Bergstrom (Stockholm), G. Bernstein (Pennsylvania), A. Bonissent (CPPM, France), M. Eriksson (Stockholm), A. Goobar (Stockholm), D. Huterer (CWRU), J. Lamoureux (LBNL), E. Linder (LBNL), S. McKee (Michigan), R. Miquel (LBNL),  E. Mortsell (Stockholm), N. Mostek (Indiana), C. Spitzer (LBNL)**

## Outline

**Purpose of Monte Carlos**
**Simulation Tools**
**SNAPfast**
**Sources**
**Photon Propagation**
**Observatory Site**
**Telescope & Instruments**
**Observing Plan**
**Simulated Data**
**Data Analysis**
**Framework**
**Summary**

## I.  Purpose of Monte Carlos

**Mission performance**
**Error budget**
**Mission optimization**
**Mission comparison**

## II.  Simulation Tools

**A breadth of tools simulate the mission**
** **
**Supernova Observation Calculator (SNOC)**
**Many publications, e.g. Goobar et al. A&A 384:1, Dahlen & Goobar PASP 114:284, Goliath et al. A&A 380:6, Mortsell et al. ApJ 561:106, Mortsell et al. ApJ 559:53**
**Calibration Monte Carlo**
**note “Effect of Calibration Errors in the Determination of Cosmological Parameters”, note “Quick Calibration Requirements”**
**K-correction error analyzer**
**note “K-correction Errors”**
**Non- Ia Contamination Monte Carlo**
**note “SNAP Type Ia Supernovae Trigger”**
**Simultaneous light-curve and spectrum Fisher analysis**
**note “Determining Supernova Distances from Observed Quantities”**
**Weak Lensing simulation**
**note “SNAP Weak Lensing Survey Strategy”**

## II.  Simulation Tools

**Cosmological parameter fitter**
**notes “Cosmological parameters as a function of z”, “Cosmology Fitting: A Progress Report”**
**Cosmology parameter Fisher analysis**
**Huterer et al. PRD 64:123527**
**Exposure Time Calculator**
**Bernstein PASP 114:98**
**PSF simulator**
**note “SNAP PSF’s”**
**SNAPfast**
**note “SNAP Monte Carlo”**
**SNAPsim**

##  III.  SNAPfast

**Focus in depth on one of these tools – SNAPfast**
**The key elements and systematics addressed by SNAPfast**
**Sources**
**SNe Ia**
**Non-Type Ia sources**
**Photon Propagation**
**Host dust**
**Gray dust**
**Gravitational lensing**
**Milky Way dust**
**Observatory Site**
**Telescope and Instruments**
**Observing Plan**
**Simulated Data**
**Calibration error**
**K-correction**
**Data Analysis**
**Malmquist bias**

##  III.  SNAPfast

**A schematic view of SNAPfast**

## III.A.  Sources

**SNe Ia**
**Fundamental parameters and observational consequences.**
**Redshift evolution of the fundamental supernova parameters.**
**Metallicity**
**Main-sequence Mass**
**Effect on magnitude drift in redshift.**
**Population distribution.**
**Host galaxy properties as a function of redshift.**
**The relevant properties of dust and surface brightness.**
**Non-SN Ia variable sources.**

## III.A.1.a  SN Ia Model

**Type Ia supernova intrinsic and observable properties**
**Reference notes**
**We adopt a four-parameter SN model from Hoeflich and Nugent**
**Intrinsic parameters:**
**Transition density from deflagration to detonation**
**Metallicity of main-sequence progenitor**
**Mass of main-sequence progenitor**
**White dwarf central density**
**which are related to observable parameters:**
**Light-curve stretch**
**Light-curve rise time**
**Light-curve peak-to-tail ratio**
**Multi-band peak magnitudes**
**Effective temperature**
**Line velocities**
**Metallicity**

|  observable
/ intrinsic | Transition density | Metallicity (dex) | Main-sequence mass | Central density |
| --- | --- | --- | --- | --- |
| L-C stretch | 1. | 0. | 0. | 0.67 |
| L-C rise time (days) | 0. | 0. | 0.1 | 0. |
| L-C peak-to-tail | 0. | 0. | 1. | -1. |
| Peak magnitudes | -1.74 | U: 0.063
B: 0.015
Rest: 0 | 1. | 0. |
| Effective Temp (K) | 5451. | 0. | -500. | 0. |
| Line Velocities (c) | 0. | 0. | 2X10-4 | -2X10-4 |
| Metallicity (dex) | 0. | 1. | 0. | 0. |

## III.A.1.a  SN Ia Model

## III.A.1.b  SN Ia evolution

**Redshift evolution of Type Ia supernovae**
**"Supernova evolution" refers to possible drift in redshift of the supernova population and environment, not cosmic evolution.  Although representatives of each SN Ia subclass may exist at each redshift,  their relative numbers can change.  Failure to recognize these evolving parameters, either by correction or homogenization, can lead to redshift-dependent bias in supernova peak brightnesses.**
**Among our set of intrinsic supernova parameters, mean population metallicity and progenitor main-sequence mass are clearly expected to evolve with redshift.**

## III.A.1.b  SN Ia evolution

**Metallicity**
**	****Galaxies at high redshift have on average undergone fewer cycles of star formation.  Lower metallicity is expected at high redshift.**
**	****For our model, we take the results of Ng & Bartelli (1997) whose results well represent the internally consistent use of field stars for the AMR relation.**
**	****	****	****	******** ****dex = -1.7 -0.03 ******** t (Gyr)**
**	****Measurements of the age-metallicity relation have yielded inconsistent results.  Much of the problem comes from the high dispersion in measured metallicities of stars with the same age.****  **

## III.A.1.b.i.  Metallicity Evolution

- Mean metallicity (Z) as a function of redshift (z) for the SNAPfast model.

**Z**

## III.A.1.b.ii  Main-sequence Mass Evolution

**Main-sequence mass**
**	****Stars that have evolved off the main sequence into white dwarves provide the population of candidate Type Ia progenitor systems.  For a stellar population of a given age, departure from the main sequence only occurs to stars above a main-sequence mass cutoff.  The average progenitor main sequence mass is expected to be greater at high redshifts where stars have had less time to evolve.**
**	****We derive the mass-sequence mass evolution from combining various sources.  Ruiz-Lapuente, Burket, & Canal (1995) have calculated the mass of the white-dwarf progenitor as a function of time after star formation.  Dominguez, Hoeflich, & Straniero (2001) relate MMS and white dwarf masses.  The star formation of a galaxy is not fixed at a single epoch so the combination of these relations can be considered as an extrema.  We ad hoc divide the deviation by a factor 5 to account for this.**

## III.A.1.b.iii  SN Ia Magnitude Drift

- Changes in the means of the parameters produce a bias in mean magnitude.  This bias for the SNAPfast model is shown here.
- A drift in supernova color produces another magnitude bias, through extinction correction.  This is seen in the two green Metallicity curves.

## III.A.1.c  SN Ia Parameter Distribution

**The intrinsic supernova parameters are assumed to have a Gaussian distribution about the mean for all redshifts.**
****** ****(stretch) = 0.07**
****** ****(log Z) = 0.03**
****** ****(Main sequence) = 0.05 in magnitude**
****** ****(Central density) = 0.05 in peak-to-tail ratio**

## III.A.1.d  Host Galaxy Properties

**The morphological type is split between Elliptical/S0 and Spirals**
**Ellipticals****	****0.3-0.1z**
**Spirals****	****0.7+0.1z**
**Surface brightness evolution**
**Ellipticals****	****SB=22-0.97 (z/0.92)****	****	****Schade et al. ApJ 525:31**
**Spirals ****	****SB=21.3-1.5z****	****	****Schade et al. ApJ 464:79**
**Spectral energy distribution**
**The weak-lensing group galaxy model based on HDF to be implemented.**

## III.A.2  Non Type Ia Supernovae

**Template Type II, Ib, and Ic’s are available.**
**Their effects on contamination is a subject of continued work.**
**note “SNAP Type Ia Supernovae Trigger”**

## III.B  Photon Propagation

**Cosmological Parameters**
**Host dust**
**Gray dust**
**Gravitational lensing**
**Milky Way dust**
**To be modeled by SNAPsim via SNOC**

## III.B.1-2  Cosmology & Host Dust

**Cosmological Parameters**
**Concordance model : Flat, ************=0.72, Cosmological constant**
**Host-galaxy extinction**
**Monte Carlo for dust in line-of-sight (reference note)**
**Dust law of Cardelli, Clayton, & Mathis (ApJ 345:245) **
** ****R****v****=3.1 +/- 0.3**
**No R****v**** redshift evolution implemented**

## III.B.3  Gray Dust

**Gray dust is a proposed form of inter-galactic dust whose extinction is nearly wavelength-independent in optical wavelengths**
**Gray-dust extinction**
**Law**
**Distribution**
****** ****+/- 0.1**
**R****v****=7.5 +/- 1.0**
**A****v****=1 +/- 0.3**

## III.B.4  Gravitational Lensing

**Weak Lensing**
**	****The magnification of each supernova is drawn from the weak-lens probability distribution for LCDM calculated by ray tracing in N-body simulations. (Wang, Holz, Munshi (2002),  Wambsganss et al. (1997)) **

## III.B.4  Gravitational Lensing

**Magnitude bias and added dispersion**
**Clump lensing to be added (SNOC e.g. ApJ 559:106)**

- 0.02 mag difference between the mean and mode

- A 0.03 mag contribution to the magnitude dispersion

## III.B.5  Milky Way Extinction

**An R****V**** and A****V**** are taken to be known as a function of position.**
**Uncertainties in R****V**** and A****V**** on small angular scales need to be modeled and added.**
**Ref. Aldering talk.**

## III.C  Observatory Site

**SNAP - Space observing of fixed fields provide low and stable zodiacal background flux.  Cosmic-ray rate 2X10****-4**** /s/pix.**
**Ground observing is sensitive to several variable conditions**
**Seeing**
**Weather**
**Atmospheric emission**
**Atmospheric absorption**

## III.C.1  Zodiacal Light

**Zodiacal Light is the source of sky background for SNAP**
**Appropriate for SNAP field, from the STScI website and note “SNAP Sky Background at the North Ecliptic Pole”**

**Notes:**
Shielding of Earth shine

## III.C.2.a  Seeing

**Consider two of the premier sites in the world: Mauna Kea and Paranal**
**Reference to notes, e.g. “Image Quality at Selected Astronomical Observatories”**
**Seeing**
**Use the relation**
**Seeing statistics with CISCO and Suprime-Cam at the Subaru Telescope at Mauna Kea have been compiled (Motohara, Ando).**
**VLT seeing statistics from EMMI exposure time calculator **

- Mauna Kea

- Paranal

**Notes:**
Mention SNAP diffraction

## III.C.2.b Weather

**Weather**
**From the ESPAS Site Summary Series & ESO website**
**Mauna Kea****	****45/67% of photometric/no cloud cover**
**Paranal****	****	****78/87% of photometric/no cloud cover**
**Correlated weather pattern to be implemented.**

## III.C.2.c Sky Emission

**Atmospheric emission is significantly brighter from the ground, particularly in the NIR**
**Use the standard Walker (1987) sky brightness for photometric filters at different lunations extended into the NIR from the ESO website for La Silla.**
**Background flux 2-3 orders of magnitude brighter than the zodiacal light in the NIR.**

## III.C.2.c Sky Emission

**Atmospheric emission – high resolution**
**Highly variable night-to-night**
**Comparison**

## III.C.2.d Atmospheric Transmission

- Rayleigh & Aerosol scattering reduce transmission in the optical while water & O2 absorption leave reduced visibility windows in the NIR.

## III.D  Telescope & Instruments

**Telescope and instruments**
**We have modeled SNAP, LSST, and other telescopes**
**For comparing SNAP vs. ground photometry we focus on the LSST**
**Telescope Specifications**
**Instrument Specifications**
**Imagers**
**Spectrographs**
**Detectors**
**Filters**
**The SNAP PSF is determined by these specifications, unlike ground telescopes whose PSF’s depend mostly on the atmosphere.**

## III.D.2 Telescope Specifications

**We adopt the specified SNAP mission.**
**LSST information is primarily from the Dark Matter Telescope website, an LSST candidate.**

|  | SNAP | LSST | 10-m |
| --- | --- | --- | --- |
| Aperture (m) | 2 | 8.5 | 10 |
| Secondary Aperture (m) | 0.4 | 5.48 | 4 |
| Obscuration | 0.16 | 0.55 | 0.16 |
| Spot diagram RMS (arcsec) | 0.05 | 0.22 | 0 |
| Jitter RMS (arcsec) | 0.01 | 0 | 0 |
| Throughput | 0.984 (Silver coating) | 0.863*0.984 (Aluminum coating + corrector + window)
 | See Spectrometer table |
| Observatory | Space | Mauna Kea or Paranal | Mauna Kea or Paranal |

## III.D.3.a Imagers

**For comparison, we give the LSST a SNAP-type imager.**

|  | SNAP | LSST |
| --- | --- | --- |
| f/# | 10 | 1.5 |
| Throughput | 0.7 | 0.7 |
| Filters | 9 redshifted B filters, z=1.15n-1, n={0,...,8} | 9 redshifted B filters, z=1.15n-1, n={0,...,8}
No NIR filters planned for LSST |
| Detectors | LBNL CCD
HgCdTe | LBNL CCD
HgCdTe
 |

## III.D.3.b Spectrometers

|  | SNAP | 10-m |
| --- | --- | --- |
| Pixel spatial resolution (arcsec) | 0.15 | 0.15 - Optical
0.03 – NIR |
| IFU slice (arcsec) | 0.15 | 0.15 - Optical
0.03 - NIR |
| Spectral pixel (dlog) | 0.01 | 0.01 |
| Throughput | 0.4 | 0.4 * 0.65 (OH-suppression) *0.8 (AO)
0.4 includes OTA, camera, HgCdTe QE’s |
| R | 100 | 100 |
| Detectors | LBNL CCD
HgCdTe | LBNL CCD
HgCdTe
 |
| Effective seeing | Diffraction | 0.5 –Optical
Diffraction - NIR |
| Strehl ratio | N/A | 0.3 - NIR |

## III.D.4  Detectors

|  | LBNL CCD | HgCdTe |
| --- | --- | --- |
| Readout Noise (e/pix) | 4 | 5 |
| Dark Current (e/s/pix) | 0.002  | 0.02 |
| Pixel Size (microns) | 10.5 | 18 |
| Diffusion (microns) | 4 | 5 |
| Peak QE | 92% | 60% |

## III.D.4  Detector QE

- LBNL CCD

- HgCdTe

## III.D.5  SNAP Filters

**Sampling meets K-correction requirements (reference to note).**
**9 redshifted B-band filters distributed logarithmically.**
**The bluest 6 filters are observed with CCD’s, the reddest 3 filters are observed with HgCdTe**

## III.D.6  SNAP PSF

**The PSF for ground-missions determined by atmospheric conditions.**
**For SNAP we consider**
**diffraction pattern of the obscured primary mirror**
**telescope jitter**
**wavelength-dependent silicon absorption depth**
** ****CCD diffusion**
** ****telescope spot diagrams**
** ****f/#, pixel size**
**non-normally incident photons (not yet implemented)**
**Differing metering structures are modeled**

## III.D.6  SNAP PSF

- Slice plot of the PSF’s

- Curves of growth

- Units in microns

## III.E  Observing Plans

**Use the SNAP reference mission observing plan. **
**For the LSST**
**We have looked at a variety of potential observing plan to optimize LSST.**
**LSST is inadequate for SNe studies without a NIR camera or spectrograph**
**We construct a new strawman LSST mission assuming the addition of a wide-field NIR camera to examine the *****depth***** that can be achieved from the ground for a single field.  A NIR camera is not planned for LSST.**
**Constraints**
**The field must be visible all night, half-year.  All-night visibility allows full use on integration time on the target.  Half-year visibility allows all supernovae exploding in the field to be followed continually for 3 months.**
**Observations are made in all SNAP filters.**
**From these constraints we fix the position of the field and the cadence allowable for all-night exposures in the reddest filters.**
**For the LSST 2**
**Get a better field of view by relaxing all-night all-year visibility condition.**
**Can obtain a low-airmass, low extinction field visible at least 2 hours/night over 6 months.**

## III.E Observing Plan - Photometry

|  | SNAP | LSST 1 | LSST 2 |
| --- | --- | --- | --- |
| Cadence (days) | 4 | 6 | 4 |
| Minimum number of exposures per pointing | 4 | 2 | 2 |
| Maximum exposure time (s) | 200 | 54000 | 54000 |
| Total integration time per pointing | Filters 0-5 : 1200 sec
Filters 6-8 : 2400 sec | {1,2,3,6,6,9,9,9,9} hours | 1 hour |
| Solid-angle monitored (sq deg) | 15 | 7 | 28 |
| SN Mission lifetime (years) | 2 | 2 | 2 (6 month contiguous) |
| Galactic E(B-V) | 0 | 0.18 | 0 |
| Airmass | N/A | 2.9 | 1.3 |

## III.E Observing Plan - Spectroscopy

|  | SNAP | 10-m |
| --- | --- | --- |
| SN-frame epoch of observation (days) | 0-4 | When good weather and seeing are available |
| Pixels per spectral sample | 7 | 7 |
| Spectral Interlacing | 2 | 2 |
| Exposure time per readout (s) | 1000 | 1000 |
| Total integration time per pointing | α (1+z)4 | α (1+z)4 |

## III.F  Simulated Data

**Signal-to-noise of data points determined by an exposure time calculator.**
**Captures all the elements described previously.**
**SNAPsim to include photometry extracted from simulated images.**
**Normalization of data points determined by calibration.**
**Two models considered.**
**Sample light curves.**

## III.F.1 Exposure Time Calculator

**The signal-to-noise for a measurement is determined from an exposure time calculator.**
**A sophisticated ETC has been developed for the SNAP mission. See talk by Gary Bernstein (PASP  114:98)**
**PSF from**
**Diffraction and aberrations**
**Charge Diffusion**
**Pixel response function**
**Undersampling**
**Dithering**
**Host-galaxy subtraction**
**Poisson noise from source**
**Zodiacal background**
**Detector noise**
**Flatfield error (10****-4**** of flux)**
**Overheads**
**Cosmic Rays**

## III.F.2 Calibration

**We model two approaches to the calibration.**
**A single calibration source, e.g. a hot white dwarf or a NIST standard,  calibrates the entire SNAP wavelength range.**
**Two independent calibrators calibrate the SNAP wavelength range; one in the optical and the other in the NIR.**
**The calibration errors are parameterized by temperature errors and correlations of the primary standards.**
**More detailed error models being constructed in conjunction with the calibration group.**

## III.F.2.a One Calibrator Model

**The cases of a white dwarf and NIST standard are considered.  (reference my note)**
**T=2856 +/- 4 K**
**T=30000 +/4000 K **
**Need a research program to obtain this.**

## III.F.2.b Two Calibrator Model

**A strawman error model is evaluated which effectively represents**
**A hot white dwarf with T=35000 +/- 1500, or equivalently a 2% flux bias in the optical from 350 – 1000 nm.**
**A solar analog with T=6000 +/- 150 or equivalently a 3% flux bias in the NIR from 1000 – 1700 nm.**
**A 0.9 correlation between the temperature errors.  This correlation can be thought to be a 4% flux error when joining the two calibrations.**
**note “Effect of Calibration Errors in the Determination of Cosmological Parameters”**
**Represents current state-of-the-art calibration. **

## III.F.2.b Two Calibrator Model

**Calibration model: We think we know the temperature of the calibrators T={35000, 6000} but the true value is drawn from a distribution.**
**1.  An optical calibration of a white dwarf with T=35000 +/- 1500K**
**2.  An IR calibration of the Sun with T=6000 +/- 150 K**
**The corresponding errors in flux space for four points on the error contour are shown.  This gives an idea of the range of the errors described by the model.**
**2% errors from 350 – 1000 nm**
**3% errors from 1000nm – 1700nm**
**5% relative float between optical and NIR**

## III.F.3 Simulated SNAP Light Curves

- Rest B-band

- Rest V-band

- z=0.8

- z=1.0

- Rest R-band

## III.F.3 Simulated SNAP Light Curves

- z=1.2

- z=1.4

- Rest B-band

- Rest V-band

## III.F.3 Simulated SNAP Light Curves

- z=1.6

- Rest B-band

- Rest V-band

## III.F.3 Simulated LSST+NIR Light Curves

- Rest B-band

- Rest V-band

- z=0.8

- z=1.0

- Rest R-band

## III.F.3 Simulated LSST+NIR Light Curves

- z=1.2

- z=1.4

- Rest B-band

- Rest V-band

## III.F.3 Simulated LSST+NIR Light Curves

- z=1.6

- Rest B-band

- Rest V-band

## III.F.3 Simulated LSST+NIR 2 Light Curves

- Rest B-band

- Rest V-band

- z=0.8

- z=1.0

- Rest R-band

## III.F.3 Simulated LSST+NIR 2 Light Curves

- z=1.2

- z=1.4

- Rest B-band

- Rest V-band

## III.F.3 Simulated LSST+NIR 2 Light Curves

- z=1.6

- Rest B-band

- Rest V-band

## III.G  Analysis of Simulated Data

**The efficiency of the telescopes as discovery instruments can  be determined given triggers.**
**The distance moduli and associated errors are determined from observed parameters from**
**Light-curve fitting**
**Depends on K-correction.**
**Spectral fitting**
**Spectral analysis currently done with the Fisher method only.**
***Or***** Fisher analysis for errors only.**
**The cosmological parameters are determined from the distance moduli.**

## III.G.1 Supernova Discovery

**The detection efficiency is determined given trigger values**
**Malmquist bias – the preferential discovery of intrinsically brighter objects is measured**

## III.G.2.a Light-curve Fitter

- tj

- texpl

- tp

- S

- Fmax

- tmax

- t/+b

**III.G.2.a Light-curve Fitter**

## III.G.2.a Supernova Analysis

- Actual fitting of the blue curve :
- texp, Fmax, tmax, s, t, and b are free parameters in B band

- tj = -12 days in restframe
- tp = 30 days in restframe

**III.G.2.a Supernova Analysis**

- Light-curve fitting

## III.G.2.a Light-curve Fitter

-  Stretch s, same for B and V bands
-  Flux max B
-  tmax B , tmax V,
-  Plateau slope : fixed
-  Plateau level
-   MB - MV
-  texpB, texpV
-  Baseline B, V
-  tj : fixed to -12 days
- For all filters :
- Baseline
- Fmax
- tmax

**III.G.2.a Light-curve Fitter**

## III.G.2.a Light-curve Fitter

- OUTPUT:
-  up to 11  fit parameters
-  covariance matrix of the fit parameters

- Minimization of:

- INPUT :
-  Fexp experimental points in B,V
-  s experimental covariance matrix
-  External conditions : fix/bound/constrain parameters

**III.G.2.a Light-curve Fitter**

## III.G.2.a Light-curve Fitter

-  Conjugate gradient method :

- Iterations : assume that d2Fi/daidaj = 0;
- After convergence, one more step with full derivatives for correct error matrix

**III.G.2.a Light-curve Fitter**

## III.G.2.a.i. K-correction

**The transformation between an observed flux of a redshifted object through a given filter to a given filter in the supernova rest frame.**
**The supernova K-corrections are dependent on supernova parameters and extinction, and are thus incorporated within the light-curve fitting.**
**K-corrections are derived from stretch-dependent spectra for now.  More sophisticated K-corrections will be implemented.**

## III.G.2.c Fisher Analysis

**Given a realized supernova’s brightness and an observing program, the Fisher analysis analytically determines the errors in the spectral parameters.**

## III.G.3 Cosmological Parameter Fitting

**Given the light curve & spectral parameters and covariance matrix for the N supernovae, fit**
***M*****=M-5log(h)****	****The supernova absolute magnitude**
******M****, ********w ****, ********k****	****	****The cosmological parameters**
**w****0****, w****′****	****	****The dark energy parameters**
**Gray dust parameters**
**Host dust parameters**
**Gravitational Lensing parameters (to be implemented)**

## III.G.3 Cosmological Parameter Fitting

**Priors are possible**
**Returns best fit values, their covariance, and error contours**
**Several ********2**** minimization codes that are extensively tested**
**Fisher analysis cross-checked with fitters**
**Study of biases**
**Give explicitly the fitting function**

## IV.  Framework for the SNAP MC

**The purpose of a framework is to decouple**
**Algorithms**
**File formats**
**Graphical displays**
**to maximize the flexibility of computations **

## IV.  Framework

**Modular Algorithms**
**Interface to algorithms**
**Initialization processing**
**Event processing**
**Termination processing**
**Interface for parameter parsing**
**Interface to event data**
**Access to data from other processing units**
**Write/remove event data**
**Supported Computing Environment**
**Architecture and dependency control**
**Document sharing**
**CVS**
**Platform support**

## IV.  SNAP Monte Carlo Elements

**SNAP Algorithms**
**The modules described in this talk**
**SNAP Graphical Displays**
**IDL**
**Paw & Root**
**SNAP File Formats**
**SNAP tables**
**Self-descriptive format that is compatible with any language**
**Currently ASCII although can be changed easily e.g. to RootIO**
**IDL restore**
**Paw vectors**
**FITS**

## V.  Summary of Monte Carlo “Systematic Errors”

| Systematic | Potential pitfalls | Challenge Set |
| --- | --- | --- |
| SN fundamental parameters | Some light-curve / spectral parameters unobserved.
No correction applied. | Redshift-dependent metallicity and main sequence mass. |
| Non-Ia contaminants | Failure to spectroscopically measure the defining SiII feature. | Include non-Ia SNe that have similar light curves and/or similar B spectra. |
| Host galaxy dust | Evolving dust properties not observed with SN data. | Redshift dependent RV |
| Gray dust | Signatures of gray dust not seen in NIR data. | Inclusion of gray dust.
 |
| Gravitational lensing | Increased dispersion and shift of the mode not seen without statistics. | Gravitational lensing included. |
| Calibration | An irriducible source of error for all missions. | Calibration drawn from a distribution. |

**We review systematic errors that are modeled in the Monte Carlo, potential pitfalls for non-SNAP missions, and a Challenge data set that can test missions.**

## V.  Summary

**The SNAP simulation group has produced a large number of Monte Carlo simulations to individually address different aspects of the SNAP mission.**
**SNAPfast is a powerful tool that models the key aspects of supernova cosmology experiments in a simultaneous self-consistent manner.**
**A summary of the first results of SNAPfast is presented in the talk of Saul Perlmutter.**
**SNAPfast is an excellent tool for upcoming mission optimization, trade studies, and error budget analysis.**