Optimization of the multiple sampling and signal extraction in nondestructive exposures

Kubik, B.; Barbier, R.; Castera, A.; Chabanat, E.; Ferriol, S.; Smadja, G.

doi:10.1117/1.jatis.1.3.038001

Cited by 4 publications

(14 citation statements)

References 6 publications

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“…for k < l. By contrast to the white noise readout noise case presented in equations ( 7) and ( 8) of Kubik et al (2015a), we observe that the contribution of the readout noise to the group difference covariance is not constant in the diagonal terms and it adds extra correlation in the off-diagonal ones.…”

Section: Correlated Noise Covariance Matrixcontrasting

confidence: 55%

“…for the off-diagonal ones. By constrast to the white noise case described by Kubik et al (2015a) we find in the latter expression a contribution from the readout noise.…”

Section: Correlated Noise Covariance Matrixmentioning

confidence: 85%

“…During flight EUCLID uses MACC(4,16,4) and MACC(15,16,11) for photometry and spectroscopy, respectively. Following Kubik et al (2015a) we estimate the total flux from the group differences, ∆G k = G k+1 − G k .…”

Section: Flux Estimationmentioning

confidence: 99%

“…We discuss here the computation of the group difference noise covariance matrix in the case of correlated readout noise. We refer to Kubik et al (2015a) for the white noise case. We will account for both photon and readout noises.…”

Section: Correlated Noise Covariance Matrixmentioning

confidence: 99%

“…As a consequence to obtain an accurate flux estimate it is necessary to have an accurate description of the photon and readout noise. In the current Euclid baseline both the readout and photon noise are described by a white noise approximation (Fowler & Gatley 1990;Kubik et al 2015a). The main properties of the readout noise assuming white noise are characterized during ground calibration and used in-flight.…”

Section: Introductionmentioning

confidence: 99%

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Euclid: Estimation of the Impact of Correlated Readout Noise for Flux Measurements with the Euclid NISP Instrument*

Munoz

Macías-Pérez

Secroun

et al. 2021

PASP

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The Euclid satellite, to be launched by ESA in 2022, will be a major instrument for cosmology for the next decades. Euclid is composed of two instruments: the Visible (VIS) instrument and the Near Infrared Spectromete and Photometer (NISP). In this work we estimate the implications of correlated readout noise in the NISP detectors for the final in-flight flux measurements. Considering the multiple accumulated (MACC) readout mode, for which the UTR (Up The Ramp) exposure frames are averaged in groups, we derive an analytical expression for the noise covariance matrix between groups in the presence of correlated noise. We also characterize the correlated readout noise properties in the NISP engineering grade detectors using long dark integrations. For this purpose, we assume a (1/ f ) α -like noise model and fit the model parameters to the data, obtaining typical values of σ = 19.7 +1.1 −0.8 e − Hz −0.5 , f knee = 5.2 +1.8 −1.3 × 10 −3 Hz and α = 1.24 +0.26 −0.21 . Furthermore, via realistic simulations and using a maximum likelihood flux estimator we derive the bias between the input flux and the recovered one. We find that using our analytical expression for the covariance matrix of the correlated readout noise we diminish this bias by up to a factor of four with respect to the white noise approximation for the covariance matrix. Finally, we conclude that the final bias on the in-flight NISP flux measurements should still be negligible even in the white noise approximation, which is taken as a baseline for the Euclid on-board processing.

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Section: Correlated Noise Covariance Matrixcontrasting

confidence: 55%

“…for the off-diagonal ones. By constrast to the white noise case described by Kubik et al (2015a) we find in the latter expression a contribution from the readout noise.…”

Section: Correlated Noise Covariance Matrixmentioning

confidence: 85%

Section: Flux Estimationmentioning

confidence: 99%

Section: Correlated Noise Covariance Matrixmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Euclid: Estimation of the Impact of Correlated Readout Noise for Flux Measurements with the Euclid NISP Instrument*

Munoz

Macías-Pérez

Secroun

et al. 2021

PASP

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A New Signal Estimator from the NIR Detectors of the Euclid Mission

Kubik

Barbier

Chabanat

et al. 2016

PASP

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We describe how the Euclid detectors in the Near Infrared Spectrometer and Photometer (NISP) channel will be read out on board and present an analytic expression for the estimated fluence in each pixel with the associated quality factor of the fit per pixel. The method accounts for the Poisson like distribution of the data and includes the effects of noise correlations that arise after the coadding procedure of frames read non-destructively up the ramp during one exposure. The bias of the flux estimator presented in this paper is kept lower than 0.3% over a wide rang of scientifically interesting fluxes of Euclid. The associated error is by 6% lower than the commonly used formula derived in Rauscher et al. in the context of an equally weighted least squares fit. Moreover, the quality factor follows the very well known c x n ; th 2 ( ) distribution and thus provides a well behaved statistical tool to check the goodness of the ramp fit. The method is proposed in the context of a large amount of data per exposure, produced by the NISP detectors, that cannot be transferred to the ground for the subsequent processing. The method, which is validated using real and simulated test data, can be safely used by most near-infrared instruments which require very accurate measurements to be performed on board.

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Optimal Fitting and Debiasing for Detectors Read Out Up-the-Ramp

Brandt

2024

PASP

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This paper derives the optimal fit to a pixel’s count rate in the case of an ideal detector read out nondestructively in the presence of both read and photon noise. The approach is general for any readout scheme, provides closed-form expressions for all quantities, and has a computational cost that is linear in the number of resultants (groups of reads). I also derive the bias of the fit from estimating the covariance matrix and show how to remove it to first order. The ramp-fitting algorithm I describe provides the χ 2 value of the fit of a line to the accumulated counts, which can be interpreted as a goodness-of-fit metric. I provide and describe a pure Python implementation of these algorithms that can process a 10-resultant ramp on a 4096 × 4096 detector in ≈8 s with bias removal on a single core of a 2020 Macbook Air. This Python implementation, together with tests and a tutorial notebook, are available at https://github.com/t-brandt/fitramp. A companion paper describes a jump detection algorithm based on hypothesis testing of ramp fits and demonstrates all algorithms on data from JWST.

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Optimization of the multiple sampling and signal extraction in nondestructive exposures

Cited by 4 publications

References 6 publications

Euclid: Estimation of the Impact of Correlated Readout Noise for Flux Measurements with the Euclid NISP Instrument*

Euclid: Estimation of the Impact of Correlated Readout Noise for Flux Measurements with the Euclid NISP Instrument*

A New Signal Estimator from the NIR Detectors of the Euclid Mission

Optimal Fitting and Debiasing for Detectors Read Out Up-the-Ramp

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