Fitting Moving Objects in Up-The-Ramp Data with Applications to the Roman Space Telescope and JWST
Abstract
A moving object breaks the fundamental property of constant per-pixel count rates in an astronomical image read out up-the-ramp. In this paper, we show how to fit a moving object's path across a detector as that detector is read out nondestructively. We write the full likelihood function for every pixel subject to a constant count rate plus a time-dependent count rate due to a moving source. Assuming the moving source to be point-like and assuming the effective point-spread function to be known, we are left with four parameters that enter the likelihood nonlinearly: two for position and two for velocity. All remaining parameters can be optimized using closed-form expressions. Our approach extracts maximal information on a moving source's position and speed and enables the source to be accurately removed from the image. We investigate the dependence of flux, position, and velocity precision on the target's speed and the readout pattern. We also find a small, positive bias on the recovered flux due to the need to fit for an uncertain position and speed. Our approach can be used for space-based images with minor Solar system bodies in the foreground, e.g.~from Roman and JWST, or for ground-based observations with satellites in the foreground. We demonstrate the promise of our method with a fit to an asteroid track observed serendipitously by the NIRISS instrument on JWST, comparing it to the performance of the JWST pipeline. Python code implementing our approach is available at https://github.com/t-brandt/moving_source. The total computational cost to fit the track of a moving object is ∼1 second on a 2023 Macbook Pro.
Related
- PSFSim: PhySics First Simulations for the Point Spread Function of the Roman Space Telescope
- The Roman Coronagraph Community Participation Program: corgisim - a simulation suite for the Nancy Grace Roman Space Telescope Coronagraph Instrument
- Nancy Grace Roman Space Telescope Wide Field Instrument: Grism Saturation Response to Bright Sources during Thermal-Vacuum Testing
- The Nancy Grace Roman Space Telescope Wide Field Instrument: Characterization of the Brighter-Fatter Effect in a Roman H4RG-10 Detector using the Talbot Effect
- Multiprobe strategies for Kinematic Lensing with the Roman Space Telescope High Latitude Wide Area Survey
BibTeX
@misc{brandt2026fitting,
title = {{Fitting Moving Objects in Up-The-Ramp Data with Applications to the Roman Space Telescope and JWST}},
author = {Timothy D. Brandt},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.25632},
eprint = {2609.25632},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.25632},
}
RIS
TY - UNPB
TI - Fitting Moving Objects in Up-The-Ramp Data with Applications to the Roman Space Telescope and JWST
AU - Timothy D. Brandt
PY - 2026
DA - 2026/09/22
PB - arXiv
DO - 10.48550/arxiv.2609.25632
UR - https://arxiv.org/abs/2609.25632
AB - A moving object breaks the fundamental property of constant per-pixel count rates in an astronomical image read out up-the-ramp. In this paper, we show how to fit a moving object's path across a detector as that detector is read out nondestructively. We write the full likelihood function for every pixel subject to a constant count rate plus a time-dependent count rate due to a moving source. Assuming the moving source to be point-like and assuming the effective point-spread function to be known, we are left with four parameters that enter the likelihood nonlinearly: two for position and two for velocity. All remaining parameters can be optimized using closed-form expressions. Our approach extracts maximal information on a moving source's position and speed and enables the source to be accurately removed from the image. We investigate the dependence of flux, position, and velocity precision on the target's speed and the readout pattern. We also find a small, positive bias on the recovered flux due to the need to fit for an uncertain position and speed. Our approach can be used for space-based images with minor Solar system bodies in the foreground, e.g.~from Roman and JWST, or for ground-based observations with satellites in the foreground. We demonstrate the promise of our method with a fit to an asteroid track observed serendipitously by the NIRISS instrument on JWST, comparing it to the performance of the JWST pipeline. Python code implementing our approach is available at https://github.com/t-brandt/moving_source. The total computational cost to fit the track of a moving object is ∼1 second on a 2023 Macbook Pro.
ER -