Feature Matrix

This summary table should give a high level overview of effects. For detailed information please make/link to a subpage or appropriate reference.

To add a validation page to an effect you can make a new page using this template for your text. See the Tree Ring Page for an example.

# Effect:

Include Introductory text

## Contact person(s) if any:
## Reference Material:
## Data Provenance:
## Model Details:
## Validation Criteria:
## Validation Results:
## Relevant Project Team for input if any:
## Release and approval log:

and link it to the effect in the last column.

Go directly to: Sensors - Sky - Throughputs - Atmospheric - Optics - Calibration Products

Sensor Table

This table is a list of current and not yet implemented sensor effects in imSim along with pointers to the techniques used to implement them and the internal validation tests that have been peformed.

Effect	Implementation	Data / Model Source	Short description	Validation Page and Notebooks
Brighter Fatter	GalSim Feature (Silicon.cpp)	Linear scaling of pixel edge vertices displacement derived with Poisson Solver. Pre-computed solutions available for both E2V and ITL sensors, with both 8 and 32 vertices per edge.	GalSim reads in vertex data from full electrostatic Poisson solver, scales them linearly with collected charges, and co-adds the effects from all pixels iteratively while collecting the image.	Page link
Diffusion	GalSim Feature (sensor.py / Silicon.cpp)	Diffusion Parameters estimated from first principles and validated with Fe55	GalSim applies random Gaussian displacement for every photon using temperature and voltage dependent amplitude, See page link	Page link
Tree Rings	GalSim Feature (Silicon.cpp) / imSim configuration (tree_rings.py)	Analytic model is used to pre-compute 189 unique sensor models with randomized parameters empirically based on BNL acquired data.	Radial displacement profile is modelled as a sum of 40 sinusoids modulated by a power law function.	Page link
CTE	camera_readout.py
Noise Rate	camera_readout.py			Noise rate is YYY
Xtalk	camera_readout.py	Currently Unknown		Currently xtalk values come from a PhoSim segmentation.txt. A notebook for parsing the file can be found here. The values used by imSim are a local copy.
Hot Pixels/Rows	See Note			We will mask locations without actually simulating them.
Fringing	Not yet			Some formalism and a standalone simulation here. The hooks to implement code for this in exist now in the GalSim API (more info needed)
Cosmic Rays	cosmic_rays.py: Approximately 10K cosmic rays which are randomly addd to the exposures can be be found in cosmic_ray_catalog. fits.gz	Template data taken from ITL test stands at UofA.		We should normalize to mountain level.
Edge rolloff	Not yet
Bleeding	Not yet	Test stand at Davis. Specialized bleed run.		Current tests are exploring behavior at the midline for ITL and E2V sensors

Sky Model

imSim uses the project sky model. It is located in the sims stack and called sims_skybrightness.

Effect	Implementation	Data / Model Source	Short description	Validation Page and Notebooks
Sky Background	LSST eups package git repo	Based on the ESO sky brightness model and all-sky camera data from LSST site for twilight sky.	The model includes light from twilight (scattered sunlight), zodiacal light (scattered sunlight from SS dust), scattered moonlight, airglow, and emission lines from the upper and lower atmosphere. The model can return SEDs or magnitude per sq arcsec in LSST filters.	Validation plots can be found in the SPIE paper. Note the model does not include any "weather" (e.g., clouds, variable OH emission). There is an option to change the solar activity, which scales the airglow component.

System Throughputs

All of the system throughputs are recorded in baseline. This information is copied from the System Engineering database. More information can be found in the REAMDE file. In that directory you can find a graphical representation of the total throughput along with datafile representing each component and the total throughput. The file representing each throughput curve is referenced below. [It looks like the README might be a bit out of date with the files.. We need more research then we can add more information to a detailed page for each]

Effect	Implementation	Data / Model Source	Short description	Validation Page and Notebooks
Camera QE and AR	detector.dat	SysEngineering 1.1	Expected response (QE response + AR coatings) of the CCDs provided by each of the two vendors under consideration.	We expect a merge from the SysEng database very soon.
Lens	lens[1,2,3].dat	SysEngineering 1.1	Combination of fused silicon and BroadBand AntiReflective (BBAR) coatings
Filters	filter[u,g,r,i,z,y].dat	SysEngineering 1.1	Filter throughput in each band (from manufacturer?)
Mirrors	m[1,2,3].dat	SysEngineering 1.1	Reflectivity curve for each mirror
Atmosphere	atmosphere_std.dat and atmosphere_10.dat	SysEngineering 1.1	MODTRAN based standard US atmosphere with Aerosols added.	Both typical (standard) throughput with airmass X=1.2 and optimum X=1.0 files are provided
Total	total[u,g,r,i,z,y].dat	SysEngineering 1.1	The total throughput by band

[What about darsky.dat? Do we use this, now that we have the ESO model? Or is this used by OpSim? Should we include it? What is hardware[u,g,r,i,z,y].dat?]

Atmospheric model

Effect	Implementation	Data / Model Source	Short description	Validation Page and Notebooks

Optical model

Sky Model

Effect	Implementation	Data / Model Source	Short description	Validation Page and Notebooks
Vignetting	Not yet
Ghosts	Not currently possible			There is currently no optical ray trace
Aberrated optics	optical_system.py	Sensitivity matrix from LSST SE LCA-XXX	Difference from AO corrected mean represented as a sum of Zernikes contributing a phase screen.	Page link

Calibration Products

Effect	Implementation	Data / Model Source	Short description	Validation Page and Notebooks