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. Author manuscript; available in PMC: 2014 Nov 13.

Published in final edited form as: Nano Lett. 2013 Oct 10;13(11):10.1021/nl4026665. doi: 10.1021/nl4026665

Deviation of detected locations of quantum dots from their actual positions in the presence of Gaussian noises at various levels (defined by the ratio of N to the photon number from a single quantum dot). The deviation is defined as $δ_{C} (r_{d}, r_{a}) = \frac{∣ r_{d} - r_{a} ∣}{∣ r_{d} ∣ + ∣ r_{a} ∣}$ , where r_d and r_a are the detected and actual locations of a quantum dot and r_d = ∞ for a missing quantum dot. Gaussian noises were chosen to respect the nature of photon noise (i.e. Poisson noise) because it can be often modeled using a Gaussian distribution, (μ = N, σ² = N), whose variance depends on the expected photon count (N). QDB3 can faithfully localize all the quantum dots correctly, up to noise level of ~ 100%. When the noise level is above ~ 100%, QDB3 started to fail detection of some quantum dots (≤ 2 out of ~ 10 quantum dots at noise level 200%).

Inline graphic — Deviation of detected locations of quantum dots from their actual positions in the presence of Gaussian noises at various levels (defined by the ratio of N to the photon number from a single quantum dot). The deviation is defined as $δ_{C} (r_{d}, r_{a}) = \frac{∣ r_{d} - r_{a} ∣}{∣ r_{d} ∣ + ∣ r_{a} ∣}$ , where r_d and r_a are the detected and actual locations of a quantum dot and r_d = ∞ for a missing quantum dot. Gaussian noises were chosen to respect the nature of photon noise (i.e. Poisson noise) because it can be often modeled using a Gaussian distribution, (μ = N, σ² = N), whose variance depends on the expected photon count (N). QDB3 can faithfully localize all the quantum dots correctly, up to noise level of ~ 100%. When the noise level is above ~ 100%, QDB3 started to fail detection of some quantum dots (≤ 2 out of ~ 10 quantum dots at noise level 200%).