Application of Compressive Sensing to Gravitational Microlensing Experiments

Barry²

2022

Signals

Self Cite

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.

“…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

Barry²

2022

Signals

Self Cite

“…Our research is targeted towards microlensing events, however, it can be extended to any astronomical events which require differenced images for observing transient events. Our previous work ( [10], [11]) shows optimistic preliminary results for applying CS to very sparse spatial images consisting of a star source experiencing single lens microlensing event. This research extends to differenced images by applying a novel CS architecture.…”

Section: Compressive Sensing Architecturementioning

confidence: 99%

“…Although a differenced image with no microlensing events will give sub-optimal results, an image with a microlensing event should increase image sparsity leading to better CS reconstruction results. We use a conic optimization algorithm as described in [8] [9] to solve the optimization problem shown in equation (11).…”

Section: Compressive Sensing Architecturementioning

confidence: 99%

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

2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

Barry

2020

Self Cite

Compressive sensing (CS) is a mathematical technique for simultaneous data acquisition and compression. In this work, we show a CS based architecture for acquiring and reconstructing transient astrophysical events. This architecture reconstructs a differenced image, eliminating the need for any sparse domain transforms, otherwise required for traditional CS reconstruction. The resulting reconstructed differenced image is of importance as the information required for generating timeseries photometric light curves is best obtained from an image differenced with a reference image. This architecture eliminates the need to 1.) transform an image to a sparse domain, 2.) reconstruct a dense field, and then apply differencing on the image to obtain the time-ordered photometry. We study the case of gravitational microlensing in which a distant source star in a crowded field is briefly magnified by the passage of a mass through the line of sight between the source star and observer. Our results show that this architecture is able to reconstruct the light curve for magnification factors greater than 1 with error less than 2% using only 10% of the Nyquist rate samples.

“…The mathematical technique implemented for CS exploits this sparsity inherent in gravitational microlensing and encodes the image during acquisition, significantly reducing data volume and for space flight instruments, it reduces onboard resources. 3,4 Similar to traditional methods, we apply data acquisition of the spatial images, followed by differencing to obtain a light curve representing a microlensing star over time. The differencing provides the relative change in pixel magnitude over time, as shown in Fig.…”

Section: Introductionmentioning

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%

Application of compressive sensing for gravitational microlensing events

J. Astron. Telesc. Instrum. Syst.

Barry

2022

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.