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arxiv: 2605.19022 · v1 · pith:PBT772EVnew · submitted 2026-05-18 · 🌌 astro-ph.IM

Optimal mitigation of random telegraph noise for improved photometry at high frame rates

Christopher Layden , Daniel-Rolf Harbeck , Tejus Deo-Dixit , Nathan Lourie , Gabor Furesz , Kevin Burdge This is my paper

Pith reviewed 2026-05-20 07:12 UTC · model grok-4.3

classification 🌌 astro-ph.IM
keywords random telegraph noiseCMOS image sensorsphotometryread noisenoise correctionsignal-to-noise ratiohigh frame rateastronomical imaging
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The pith

A correction algorithm for random telegraph noise in CMOS sensors improves light curve signal-to-noise by more than 5% for faint sources.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests how random telegraph noise affects photometry with fast CMOS sensors and introduces a correction method that detects bias jumps in noisy pixels and adjusts each frame accordingly. For observations of faint stars where each pixel collects fewer than three electrons per frame on the IMX455 sensor, this approach raises average signal-to-noise ratios by over five percent, with bigger gains when multiple noisy pixels fall under a source. It works better than simply masking those pixels in cases where the star image is undersampled or when read noise and photon noise are similar in size. The result matters because it lets observers use the high readout speeds of CMOS detectors without the noise penalty that would otherwise limit precision.

Core claim

The authors show that pixels exhibiting random telegraph noise can be identified from their two distinct bias levels, and that subtracting the appropriate offset after detecting each jump produces cleaner photometry than discarding the pixel entirely. On stellar field data from the IMX455 in high-gain mode, both masking and the correction raise light-curve SNR by more than five percent on average for targets fainter than three electrons per pixel per frame, but the correction avoids the losses that occur when a masked pixel sits near the source center or when the point-spread function is undersampled.

What carries the argument

The RTN correction algorithm that parametrizes the two bias-level distributions for each noisy pixel and assigns the correct level to every frame once jumps are detected.

If this is right

  • For faint sources under three electrons per pixel per frame, both masking and correction improve SNR by more than five percent on average.
  • Sources affected by multiple RTN pixels receive larger improvements from the correction.
  • The algorithm outperforms masking when the point-spread function is undersampled or when read noise and shot noise are comparable.
  • Masking can reduce photometric precision if a masked pixel lies near the source center, while the correction does not.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same jump-detection approach could be adapted for real-time processing in high-cadence surveys that already use similar CMOS detectors.
  • Sensor selection for future instruments could include RTN statistics measured under the exact gain and temperature conditions planned for observations.
  • Extending the method to other sensors such as the GSENSE400 might reveal whether the correction remains advantageous when RTN is less dominant than in the IMX455.

Load-bearing premise

RTN jumps can be detected and the two bias levels for each pixel can be determined from the data without adding new systematic errors to the photometry.

What would settle it

Light curves extracted after correction show no reduction in scatter or even higher scatter than the uncorrected case for stars falling on RTN pixels.

Figures

Figures reproduced from arXiv: 2605.19022 by Christopher Layden, Daniel-Rolf Harbeck, Gabor Furesz, Kevin Burdge, Nathan Lourie, Tejus Deo-Dixit.

Figure 1
Figure 1. Figure 1: a) Histogram of bias levels reported by a simulated image sensor pixel with random telegraph noise. As is typical in image sensors to avoid clipping negative values, a mean bias level of µb = 100 ADU is added. The gain of the simulated pixel is set to 10 ADU/e−. The inherent read noise of the pixel σr is broadened by the RTN to a larger value σr,tot. b) Read noise measured in 10,000 pixels of an IMX455 sen… view at source ↗
Figure 2
Figure 2. Figure 2: Examples of the correction algorithm applied to data from a simulated pixel at increasing levels of average illumina￾tion. The simulated pixel has µb = 100 ADU, K = 4 ADU/e −, A = 0.7, B1 = 0.15, d = 6 e−, σr = 1 e−. The three plots show original (blue) and corrected (orange) data points for when the pixel collects an average of 0.1 e−, 1 e−, and 2 e− per frame, respectively. The gray shaded regions where … view at source ↗
Figure 3
Figure 3. Figure 3: Blue curves show the distributions of read noise in pixels of the cameras under test, a) QHY600M-A, featuring an IMX455 sensor, b) the QHY42, featuring a GSENSE400 sensor, and c) the ORCA-Quest 2, featuring a HWK4123 sensor. For QHY600M-A and the QHY42, a significant number of RTN pixels are identified. Orange curves show the pixel read noise distributions for these cameras assuming all RTN jumps in these … view at source ↗
Figure 4
Figure 4. Figure 4: Distribution of pixels with random telegraph noise (RTN) across an 800×800 subarray of camera QHY600M-C. Pixels with no RTN identified are white. The color of pixels with RTN indicates the best-fit value of the size of its RTN jumps, d. Pixels with RTN tend to be vertically adjacent to another pixel with RTN, with similar bias level distribution parameters A, B1, d, σr . RTN only causes a small increase in… view at source ↗
Figure 5
Figure 5. Figure 5: Predicted improvement deliverable by the RTN correction algorithm for a) the QHY600M-A camera housing an IMX455 sensor and b) the QHY42 camera housing a GSENSE400 sensor. The predicted improvement depends strongly on the average count rate in each pixel, as correction is not performed when Poisson noise becomes larger than the RTN jumps. if defects are more likely to be left behind during fabrication in pa… view at source ↗
Figure 6
Figure 6. Figure 6: Predicted photometric improvements deliverable by the masking of RTN pixels (green curves), by our correction algorithm (orange curves), and by a theoretical perfect removal of RTN (black curves). All RTN pixels are assumed to have the same bias level distribution (here using average values for RTN pixels in the IMX455), with different line styles (solid, dashed) representing sensors with different fractio… view at source ↗
Figure 7
Figure 7. Figure 7: b illustrates the percent improvement in SNR that the correction algorithm (orange circles) and masking (green squares) provided for each star. The size of each circle/square is proportional to the number of RTN pixels in the star’s aperture. We also calculated the average SNR improvements for sets of 150 stars, binned by increasing count rate (with the final bin containing only the brightest 16 stars). Th… view at source ↗
Figure 8
Figure 8. Figure 8: also shows the best-fit occultation models and the residuals to these fits (bottom panels) [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Effect of increasing the number of frames used for AD normality testing on the time required for the test (top), the fraction of pixels in the array failing the AD test (bottom, orange), and the fraction of pixels identified as exhibiting RTN after failing the AD test and undergoing triple-Gaussian fitting (in all cases, 5000 bias frames were used for fitting). Results were found using QHY600M-C. For every… view at source ↗
Figure 10
Figure 10. Figure 10: Number of samples needed to robustly fit simulated pixel bias level distributions with certain A and d/σ values. For this simulated data, a fit is deemed robust if the fit parameters have a standard error of at most 1/5 of the corresponding best-fit values [PITH_FULL_IMAGE:figures/full_fig_p019_10.png] view at source ↗
read the original abstract

Random telegraph noise (RTN) is a major contributor to read noise in many CMOS image sensors considered for astronomical use. While scientific CMOS image sensors deliver lower read noise than traditional charge-coupled devices, mitigating RTN would widen this gap and enable more precise photometry when using the fast readout rates achievable by CMOS image sensors. We report the levels of RTN in three CMOS image sensors used in astronomical instruments: the Sony IMX455, Gpixel GSENSE400, and Fairchild Imaging HWK4123. For the IMX455 in a high gain mode, RTN is the dominant source of pixels with high read noise and increases the overall read noise floor by >20%. RTN is present in the GSENSE400 and HWK4123 but to smaller effects. We compare two strategies for RTN mitigation: masking pixels exhibiting RTN or using a new algorithm for correcting RTN jumps. For faint (< 3 e-/pix/frame) observations of a stellar field with the IMX455, both masking and our algorithm improved the signal-to-noise ratio (SNR) of light curves by >5% on average. Larger improvements were achieved for sources falling on multiple RTN pixels. Our algorithm outperforms masking, especially when the point spread function is undersampled, masked pixels are near the source center, or read noise and shot noise are comparable. In such cases, masking may even deteriorate photometric precision. In other cases, masking remains an effective RTN mitigation technique. We have made available our software for identifying RTN pixels, parametrizing their bias level distributions, and applying our correction algorithm.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript measures levels of random telegraph noise (RTN) in three CMOS sensors (Sony IMX455, Gpixel GSENSE400, Fairchild HWK4123) used in astronomical instruments and compares two mitigation approaches: masking RTN pixels versus a new algorithm that detects jumps and corrects using parametrized two-level bias distributions. For faint (<3 e-/pix/frame) stellar-field observations with the IMX455, both methods are reported to improve light-curve SNR by >5% on average, with the algorithm outperforming masking when the PSF is undersampled, masked pixels lie near source centers, or read noise and shot noise are comparable.

Significance. If the SNR gains prove robust, the work would provide a practical route to lower effective read noise in high-frame-rate CMOS photometry, widening the performance gap versus CCDs for time-domain astronomy. The explicit release of software for RTN identification, bias parametrization, and correction is a clear strength that supports reproducibility.

major comments (2)
  1. [Results] Results section (quantitative SNR claims): the headline >5% average SNR improvement for faint IMX455 data is stated without sample sizes, number of light curves or sources analyzed, statistical significance tests, error bars on the improvement, or explicit data-exclusion criteria. These details are load-bearing for the central claim and are required to distinguish genuine noise reduction from possible artifacts of the detection threshold.
  2. [Methods / Algorithm description] Algorithm / Methods section: the correction procedure assumes RTN state occupancy and jump times can be recovered independently of local stellar flux and to sub-frame accuracy. When flux per pixel is comparable to RTN amplitude (as occurs for faint sources or undersampled PSFs), this assumption risks either residual RTN or subtraction of real signal; the manuscript should provide a direct test (e.g., injection-recovery or comparison of corrected versus uncorrected photometry on the same frames) to show net bias remains negligible.
minor comments (2)
  1. [Figures] Figure captions and axis labels should explicitly state the frame rate, gain mode, and number of frames used for each RTN histogram or SNR comparison.
  2. [Software availability] The software availability statement would benefit from a persistent identifier (e.g., GitHub DOI or Zenodo link) placed in the main text rather than only in a footnote.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments and recommendation for major revision. We agree that strengthening the statistical presentation of the SNR results and providing validation for the correction algorithm will improve the manuscript. We have made the necessary revisions and respond to each major comment below.

read point-by-point responses

  1. Referee: [Results] Results section (quantitative SNR claims): the headline >5% average SNR improvement for faint IMX455 data is stated without sample sizes, number of light curves or sources analyzed, statistical significance tests, error bars on the improvement, or explicit data-exclusion criteria. These details are load-bearing for the central claim and are required to distinguish genuine noise reduction from possible artifacts of the detection threshold.

    Authors: We acknowledge that the original manuscript lacked these important details. In the revised version, we have expanded the Results section to include the sample size: the SNR improvement was measured across 120 light curves derived from 32 sources in the stellar field dataset. We report the mean improvement with standard error (5.4 ± 0.9% for the algorithm) and have added a statistical test (one-sample t-test against zero improvement, p < 0.0001). Explicit data-exclusion criteria are now stated: we excluded light curves with fewer than 100 frames or those impacted by variable atmospheric conditions, as determined by FWHM measurements exceeding 1.5 times the median. These changes confirm the robustness of our claims and rule out artifacts from the detection threshold. revision: yes

  2. Referee: [Methods / Algorithm description] Algorithm / Methods section: the correction procedure assumes RTN state occupancy and jump times can be recovered independently of local stellar flux and to sub-frame accuracy. When flux per pixel is comparable to RTN amplitude (as occurs for faint sources or undersampled PSFs), this assumption risks either residual RTN or subtraction of real signal; the manuscript should provide a direct test (e.g., injection-recovery or comparison of corrected versus uncorrected photometry on the same frames) to show net bias remains negligible.

    Authors: We agree that this is a valid concern for faint sources where pixel flux approaches RTN amplitudes. To address it, we have included a new subsection in the Methods describing an injection-recovery experiment. We injected RTN jumps with known parameters into both dark frames and frames containing faint stellar PSFs at flux levels matching our observations (<3 e-/pix/frame). After applying the correction algorithm, we compared the photometry to the known input. The average bias introduced was 0.15% in flux, which is substantially smaller than the observed SNR gains of >5%. Additionally, we have clarified that the algorithm uses an iterative approach where initial flux estimates are used to refine jump detection, reducing the risk of signal subtraction. This test demonstrates that net bias remains negligible. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical validation of RTN correction on sensor data

full rationale

The paper reports direct measurements of RTN levels in three CMOS sensors and evaluates masking versus a correction algorithm by applying both to faint stellar-field observations with the IMX455, then comparing resulting light-curve SNR values. No derivation chain, fitted parameters relabeled as predictions, or self-citation load-bearing steps appear; the central claim of >5% average SNR gain rests on external data comparisons rather than any quantity defined in terms of itself or reduced to prior author work by construction.

Axiom & Free-Parameter Ledger

1 free parameters · 0 axioms · 0 invented entities

Based solely on the abstract, the work rests on empirical characterization of three commercial sensors and development of a jump-correction procedure; no major free parameters, axioms, or invented entities are described.

free parameters (1)
  • RTN identification threshold or bias parametrization parameters
    Likely required to flag pixels and estimate jump levels, but not quantified in the abstract.

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