Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Evaluating the spatial intra-pixel sensitivity variations and influence based on space observation

Peipei Wang, Zihuang Cao, Chao Liu, Peng Wei, Xin Zhang, Jialu Nie

Abstract

Intra-pixel sensitivity variations (IPSVs) in charge-coupled devices (CCDs) and complementary metal-oxide-semiconductor (CMOS) detectors constitute a significant source of astrometric error for undersampled stellar observations. Since laboratory-based IPSV measurements suffer from limited applicability, we propose a computational method to directly infer IPSV from stellar images and validate it with simulated data. By minimizing the flux residuals between theoretical and observed stellar models through least-squares fitting, we can successfully recover the IPSV, which is treated as nearly identical across pixels. Simulations demonstrate that the reconstructed IPSV achieves high accuracy, and the instrumental point spread function (IPSF) restored using this IPSV improves stellar centroiding by nearly 30$\times$, effectively eliminating periodic pixel-phase errors. The method remains robust under different morphologies of IPSV and varying sampling conditions. Additionally, the framework can be extended to an iterative IPSF-IPSV closed-loop scheme that updates both components simultaneously, providing a practical pathway for continuous detector calibration in future space-based astronomical surveys.

Evaluating the spatial intra-pixel sensitivity variations and influence based on space observation

Abstract

Intra-pixel sensitivity variations (IPSVs) in charge-coupled devices (CCDs) and complementary metal-oxide-semiconductor (CMOS) detectors constitute a significant source of astrometric error for undersampled stellar observations. Since laboratory-based IPSV measurements suffer from limited applicability, we propose a computational method to directly infer IPSV from stellar images and validate it with simulated data. By minimizing the flux residuals between theoretical and observed stellar models through least-squares fitting, we can successfully recover the IPSV, which is treated as nearly identical across pixels. Simulations demonstrate that the reconstructed IPSV achieves high accuracy, and the instrumental point spread function (IPSF) restored using this IPSV improves stellar centroiding by nearly 30, effectively eliminating periodic pixel-phase errors. The method remains robust under different morphologies of IPSV and varying sampling conditions. Additionally, the framework can be extended to an iterative IPSF-IPSV closed-loop scheme that updates both components simultaneously, providing a practical pathway for continuous detector calibration in future space-based astronomical surveys.
Paper Structure (16 sections, 8 equations, 10 figures, 1 table)

This paper contains 16 sections, 8 equations, 10 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methods
  3. Model Foundation
  4. Model Assessment Metrics
  5. Data
  6. IPSF simulation
  7. IPSV and the stellar objects simulation
  8. Results
  9. IPSV solution accuracy and IPSF reconstruction performance
  10. Pixel phase error correction
  11. Discussion
  12. Subpixel resolution effects
  13. Numerical Stability of IPSV Solutions vs. Stellar Count
  14. IPSV morphology robustness
  15. IPSF–IPSV Alternating Reconstruction Framework for Real Observations
  16. ...and 1 more sections

Figures (10)

  • Figure 1: Illustration of the pixel and sub-pixel coordinate definitions used in this work. Each detector pixel is indexed by $(i,j)$, and its geometric center is denoted by $(x_{ij}, y_{ij})$. A local Cartesian coordinate system is defined with the origin at the geometric center of the central pixel (row 6, column 6). The continuous coordinates $(x, y)$ denote spatial positions in this local reference frame. Right panel: an $11\times11$ detector pixel array (black lines), where the stellar centroid (black dot) lies within subpixel $(1,2)$ of the central pixel at $(i,j)=(6,6)$. Left panel: a schematic sub-pixel grid (red lines) within a single pixel, showing a $3\times3$ sub-pixel division.
  • Figure 2: Normalized Gaussian IPSF ($\sigma$=0.5 pixel) within 11×11 pixel array. A 1.5-pixel radius from the pixel (6,6) center contains more than 80% of total flux.
  • Figure 3: Left: Simulated $3\times 3$ IPSV ($\sigma=0.8$) for a pixel. Right: illustration of the resulting IPSVs over an $11\times11$ pixel region when the same IPSV is applied to each pixel.
  • Figure 4: The IPSV solution. Left: simulated ground-truth IPSV. Middle: IPSV solution $\hat{IPSV}$. Right: relative residual $(IPSV-\hat{IPSV})/IPSV$. Numerical annotations in each panel indicate the sub-pixel response efficiency values or error values.
  • Figure 5: IPSF reconstruction for a representative star. Top row: (1) ground-truth IPSF, (2) observed IPSV-modulated stellar image, (3) residual between IPSF and stellar image, Bottom row: (4) reconstructed IPSF, (5) residual (pixel level) between Reconstructed IPSF and ground-truth IPSF.
  • ...and 5 more figures