Changes between Version 49 and Version 50 of Background_Dark_Model

Timestamp:

Apr 18, 2012, 5:48:55 PM (14 years ago)

Author:

watersc1

Comment:

--

Background_Dark_Model

@@ -1 +1 @@
 [wiki:Background_Continuity Return] 
+
+== April 18, 2012 ==
+
+=== Proposed Noisemap Boost calculations ===
+
+[[Image(proposed_boosts.png,400px)]]
+
+The method used to calculate the proposed boosts is as follows:
+
+ 1. Run a "standard" chip processing with photometry on a set of BIAS exposures (N=10 for this case).  All detected objects are presumed to be false, as a BIAS has no light on it.  (Flats were not applied, as OPEN filter doesn't have a flat.  PSF for photometry chosen from a science exposure).
+ 2. Collate all detected sources, and count the number of sources above some threshold: {{{1 / MAG_PSF_ERR > sigma_threshold}}}.
+ 3. Assuming all of these detections represent a mis-estimate of the background noise, we can derive the effective threshold as {{{sigma_effective = sqrt(2) * inverfc((2 * N_false) / (dX * dY * Nexp / A_psf))}}} using N_false, the number of detections above the threshold; dX,dY, the size of the area we counted the false detections; Nexp the number of BIAS exposures measured; A_psf, an estimate of a PSF footprint area, chosen to be ~16 pixels.
+ 4. Calculate the boost factor: {{{B = sigma_threshold / sigma_effective}}}
+
+This plot shows the boost calculation for three OTAs, one that is known to have significant false positives (OTA67) and two that have much lower false positive rates (OTA16 and OTA31).  The calculation was done on 20x600 stripes oriented along the chip y-axis (to map the variation in the noise in the x-axis).  The large plume visible for OTA67 shows that many of these stripes have false positives, and would require a boost to have the effective sigma match the desired threshold.  A similarly lacking plume for OTA16/OTA31 shows that these OTAs do indeed have less of a false positive problem.
+
+One concern is the obvious quantization of the boost.  A single false positive in this data will suggest a boost factor of 1.37.  Adding more input bias frames would reduce this quantization, and more calculations are running to resolve this problem.
 
 == April 17, 2012 ==