Detectors
Detectors
About
LMIRcam was upgraded to HAWAII-2RG (H2RG) HgCdTe detector in 2018. This detector is 2048 x 2048 pixels2. The outer pixels are vignetted. In Summer of 2017 LMIRcam electronics were upgraded with Teledyne’s SIDECAR electronics.
The LMIRcam plate scale is ~10.7mas/pix with a FoV 22 arcseconds squared. Due to vignetting at the edges this FoV is closer to 18 arcseconds squared.
Characteristics
The dark current and read noise are low, allowing for background-limited observations in most imaging and spectroscopic configurations. There is significant persistence and nonlinearity with these detectors. Detector characterization in progress.
| Parameter | LMIRCAM | NOMIC | ||
|---|---|---|---|---|
| FAST | MEDIUM | SLOW | ||
| Detector Area(pixels) | See Readout Stripes | 2048×2048 | 2048×2048 | 1024×1024 |
| Pixel Scale(mas/pixels) | 10.7 | 10.7 | 10.7 | 17.9 |
| Frame Time [s] | see Readout Stripes | 0.4918 | 1.4753 | TDB |
| Saturation Level | 3900 | 62000 | 62000 | 16000 |
| 5% non-linearity | 2900 | 47500 | 47500 | N/A |
| Bias Level | 500 | 5000 | 5000 | 1200-2500 |
| Dark Count | 2 | 18 | 18 | TDB |
| Read Noise | 2 | 35 | 40 | 15-30 |
Sub windowing is available for LMIRCAM in FAST mode only. Minimum exposure time scales linearly with the vertical pixel size.
Readout Stripes
| Label | First Row | Last Row | Frame Time [ms] |
|---|---|---|---|
| Full Image | 1 | 2048 | 27.5 |
| Center_Stripe_1024 | 513 | 1536 | 13.7 |
| Center_Stripe_512 | 1025 | 1536 | 6.9 |
| Center_Stripe_256 | 1025 | 1280 | 3.4 |
Saturation
Saturation is around 64K counts. ??% non linear at ~40K. Persistence is negligible.
Non-Linearity
All near-infrared detectors are non-linear. The lmircam_linearty.py script should be run about once per run. The script takes a series of exposures intended to cover the full dynamic range of the detector and allows the users to characterize the linearization fit across the detector. If you work with high-dynamic range data or you care about doing higher precision photometry, it will be very important to linearize your data.
