Compressive Sensing Based Data Acquisition Architecture for Transient Stellar Events in Crowded Star Fields

Korde-Patel, Asmita; Barry, Richard

doi:10.1109/i2mtc43012.2020.9128610

Cited by 2 publications

(5 citation statements)

References 9 publications

(4 reference statements)

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“…In order to eliminate constant star sources in crowded fields, differencing can be applied. Through our previous work, 5 we show that CS can be applied on crowded star fields to produce differenced images, preserving the microlensed star magnification, with a very low error when the point spread function (PSF) of the two differenced images are the same.…”

Section: Compressive Sensing Simulations Setupmentioning

confidence: 95%

“…Due to the relative motion between the lens and source, amplification is dependent on the position of the source image at each time, t. Equation (5) shows the position of the source at each time given the trajectory the source takes 1 E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 0 5 ; 1 1 6 ; 3 9 0…”

Section: Single Lens Gravitationally Microlensed Eventsmentioning

confidence: 99%

“…In our simulations, we use CS framework based on our previous work. 5 An architectural diagram is shown in Fig. 3.…”

Section: Compressive Sensing Architecture and Processmentioning

confidence: 99%

“…In our previous work, [3][4][5] we did a preliminary analysis on the effects of CS on transient photometric measurements. In this work, we specifically analyze single and binary microlensing events and the implications of CS reconstruction on gravitational microlensing parameters of interest.…”

Section: Introductionmentioning

confidence: 99%

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Application of compressive sensing for gravitational microlensing events

Korde-Patel

Barry

2022

J. Astron. Telesc. Instrum. Syst.

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Compressive sensing (CS) is a unique mathematical technique for simultaneous data acquisition and compression. This technique is particularly apt for time-series photometric measurements; we apply CS to time-series photometric measurements specifically obtained due to gravitational microlensing events. We show the error sensitivity in detecting microlensing event parameters through simulation modeling. Particularly, we show the relation of both the amount of error and its impact on the microlensing parameters of interest. We derive statistical error bounds to apply those as a baseline for analyzing the effectiveness of CS application. Our results of single and binary microlensing events conclude that we can obtain error less than 1% over a three-pixel radius of the center of the microlensing star by using 25% Nyquist rate measurements.

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Section: Compressive Sensing Simulations Setupmentioning

confidence: 95%

Section: Single Lens Gravitationally Microlensed Eventsmentioning

confidence: 99%

“…In our simulations, we use CS framework based on our previous work. 5 An architectural diagram is shown in Fig. 3.…”

Section: Compressive Sensing Architecture and Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of compressive sensing for gravitational microlensing events

Korde-Patel

Barry

2022

J. Astron. Telesc. Instrum. Syst.

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“…We use CS architecture based on our previous work, as described in [7,8]. A figurative description is shown in Figure 1.…”

Section: Cs Architecturementioning

confidence: 99%

Application of Compressive Sensing in the Presence of Noise for Transient Photometric Events

Korde-Patel

Barry²

2022

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Compressive sensing is a simultaneous data acquisition and compression technique, which can significantly reduce data bandwidth, data storage volume, and power. We apply this technique for transient photometric events. In this work, we analyze the effect of noise on the detection of these events using compressive sensing (CS). We show numerical results on the impact of source and measurement noise on the reconstruction of transient photometric curves, generated due to gravitational microlensing events. In our work, we define source noise as background noise, or any inherent noise present in the sampling region of interest. For our models, measurement noise is defined as the noise present during data acquisition. These results can be generalized for any transient photometric CS measurements with source noise and CS data acquisition measurement noise. Our results show that the CS measurement matrix properties have an effect on CS reconstruction in the presence of source noise and measurement noise. We provide potential solutions for improving the performance by tuning some of the properties of the measurement matrices. For source noise applications, we show that choosing a measurement matrix with low mutual coherence can lower the amount of error caused due to CS reconstruction. Similarly, for measurement noise addition, we show that by choosing a lower expected value of the binomial measurement matrix, we can lower the amount of error due to CS reconstruction.

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Compressive Sensing Based Data Acquisition Architecture for Transient Stellar Events in Crowded Star Fields

Cited by 2 publications

References 9 publications

Application of compressive sensing for gravitational microlensing events

Application of compressive sensing for gravitational microlensing events

Application of Compressive Sensing in the Presence of Noise for Transient Photometric Events

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