Flux is one of the most fundamental parameters in astrophysics, and aperture photometry and point-spread function (PSF) photometry are commonly used methods to obtain the flux. With the continuous development of astronomical equipment that has generated massive data, researchers have to find more effective methods to obtain stellar fluxes. However, current photometric software such as SExtractor are very sensitive to the configuration parameters and are difficult to configure. We propose a new photometric model based on deep learning called sf-convolutional neural network (CNN) to extract aperture fluxes and PSF fluxes. For the simulated data including 5727 stars, the experimental results show that sf-CNN can predict fluxes better than SExtractor. The mean absolute error (MAE) values of sf-CNN and SExtractor for predicting PSF fluxes are 0.0034 and 0.0134, respectively. On the 6293 mixed stars in DECam Legacy Survey Data Release (DR) 9, the MAE values of the predicted PSF fluxes are 0.0075 and 0.0177, respectively. The PSF accuracy of the sf-CNN model is significantly higher than that of SExtractor. Additionally, the MAE values of the predicted aperture fluxes on 6215 mixed stars and 1341 blends of stars in Sloan Digital Sky Survey DR 12 illustrate that the accuracy of sf-CNN is still the highest. Meanwhile, the results indicate that sf-CNN outperforms VGG16 and ResNet50. Furthermore, sf-CNN is 100–200 times faster than Photutils on RTX 3070 GPU and 20–40 times faster than Photutils on I7 12700 CPU. sf-CNN can calculate fluxes efficiently and accurately only by setting a few parameters and may thus become a fundamental tool for the era of big data in astronomy.

Star Photometry for DECam Legacy Survey and Sloan Digital Sky Survey Images Based on Convolutional Neural Networks


 We present a comprehensive photometric study of RR Lyrae stars in the M3 globular cluster, utilising a vast dataset of 3140 optical (UBVRI) CCD images spanning 35 years from astronomical data archives. We have successfully identified previously known 238 RR Lyrae stars from the photometric data, comprising 178 RRab, 49 RRc, and 11 RRd stars. Multi-band periodogram was used to significantly improve the long-term periods of 65% of RR Lyrae stars in our sample, thanks to the unprecedentedly long temporal coverage of the observations. The light curve templates were used to obtain accurate and precise mean magnitudes and amplitudes of all RR Lyrae variables. We combined optical (UBVRI) and near-infrared (NIR, JHKs) photometry of RR Lyrae variables to investigate their location in the colour-magnitude diagrams as well as the pulsation properties such as period distributions, Bailey diagrams and amplitude ratios. The Period-Luminosity relations in R and I bands and Period-Wesenheit relations were derived after excluding outliers identified in CMDs. The Period-Wesenheit relations calibrated via the theoretically predicted relations were used to determine a distance modulus of μ = 15.04 ± 0.04 (stats) ± 0.19 (syst.) mag (using metal-independent WBV Wesenheit) and μ = 15.03 ± 0.04 (stats) ± 0.17 (syst.) mag (using metal-dependent WVI Wesenheit). Our distance measurements are in excellent agreement with published distances to M3 in the literature. We also employed an artificial neural network based comparison of theoretical and observed light curves to determine physical parameters (mass, luminosity, and effective temperature) for 79 non-Blazhko RRab stars that agree with limited literature measurements.

pdf/multi-wavelength-photometric-study-of-rr-lyrae-variables-in-mupg0jgr2x.pdf

Multi-wavelength photometric study of RR lyrae variables in the globular cluster NGC 5272 (Messier 3)


 We present a new technique to generate the light curves of RRab stars in different photometric bands (I and V bands) using artificial neural networks (ANN). A pre-computed grid of models was used to train the ANN, and the architecture was tuned using the I-band light curves. The best-performing network was adopted to make the final interpolators in the I and V bands. The trained interpolators were used to predict the light curve of RRab stars in the Magellanic Clouds, and the distances to the Large Magellanic Cloud and Small Magellanic Cloud were determined based on the reddening independent Wesenheit index. The estimated distances are in good agreement with the literature. The comparison of the predicted and observed amplitudes, and Fourier amplitude ratios showed good agreement, but the Fourier phase parameters displayed a few discrepancies. To showcase the utility of the interpolators, the light curve of the RRab star EZ Cnc was generated and compared with the observed light curve from the Kepler mission. The reported distance to EZ Cnc was found to be in excellent agreement with the updated parallax measurement from Gaia EDR3. Our ANN interpolator provides a fast and efficient technique to generate a smooth grid of model light curves for a wide range of physical parameters, which is computationally expensive and time-consuming using stellar pulsation codes.

/pdf/predicting-light-curves-of-rr-lyrae-variables-using-39t19jfx.pdf

Predicting light curves of RR Lyrae variables using artificial neural network based interpolation of a grid of pulsation models

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Predicting light curves of RR Lyrae variables using artificial neural network based interpolation of a grid of pulsation models