The Experts below are selected from a list of 14634 Experts worldwide ranked by ideXlab platform
Mansi M. Kasliwal - One of the best experts on this subject based on the ideXlab platform.
-
Intermediate palomar transient factory: Realtime Image Subtraction pipeline
Publications of the Astronomical Society of the Pacific, 2016Co-Authors: Yi Cao, Perry E. Nugent, Mansi M. KasliwalAbstract:A fast-turnaround pipeline for realtime data reduction plays an essential role in discovering and permitting follow-up observations to young supernovae and fast-evolving transients in modern time-domain surveys. In this paper, we present the realtime Image Subtraction pipeline in the intermediate Palomar Transient Factory. By using high-performance computing, efficient database, and machine learning algorithms, this pipeline manages to reliably deliver transient candidates within ten minutes of Images being taken. Our experience in using high performance computing resources to process big data in astronomy serves as a trailblazer to dealing with data from large-scale time-domain facilities in near future.
Waqas Bhatti - One of the best experts on this subject based on the ideXlab platform.
-
Image Subtraction reduction of open clusters m35 ngc 2158 in the k2 campaign 0 super stamp
Publications of the Astronomical Society of the Pacific, 2017Co-Authors: M Soaresfurtado, Joel D. Hartman, Gáspár Á. Bakos, C. X. Huang, Kaloyan Penev, Waqas BhattiAbstract:Observations were made of the open clusters M35 and NGC 2158 during the initial K2 campaign (C0). Reducing these data to high-precision photometric time-series is challenging due to the wide point spread function (PSF) and the blending of stellar light in such dense regions. We developed an Image-Subtraction-based K2 reduction pipeline that is applicable to both crowded and sparse stellar fields. We applied our pipeline to the data-rich C0 K2 super-stamp, containing the two open clusters, as well as to the neighboring postage stamps. In this paper, we present our Image Subtraction reduction pipeline and demonstrate that this technique achieves ultra-high photometric precision for sources in the C0 super-stamp. We extract the raw light curves of 3960 stars taken from the UCAC4 and EPIC catalogs and de-trend them for systematic effects. We compare our photometric results with the prior reductions published in the literature. For detrended, TFA-corrected sources in the 12--12.25 $\rm K_{p}$ magnitude range, we achieve a best 6.5 hour window running rms of 35 ppm falling to 100 ppm for fainter stars in the 14--14.25 $ \rm K_{p}$ magnitude range. For stars with $\rm K_{p}> 14$, our detrended and 6.5 hour binned light curves achieve the highest photometric precision. Moreover, all our TFA-corrected sources have higher precision on all time scales investigated. This work represents the first published Image Subtraction analysis of a K2 super-stamp. This method will be particularly useful for analyzing the Galactic bulge observations carried out during K2 campaign 9. The raw light curves and the final results of our detrending processes are publicly available at \url{this http URL}.
-
Image Subtraction Reduction of Open Clusters M35 & NGC 2158 in the K2 Campaign 0 Super Stamp
Publications of the Astronomical Society of the Pacific, 2017Co-Authors: M. Soares-furtado, Joel D. Hartman, Gáspár Á. Bakos, C. X. Huang, Kaloyan Penev, Waqas BhattiAbstract:Observations were made of the open clusters M35 and NGC 2158 during the initial K2 campaign (C0). Reducing these data to high-precision photometric time-series is challenging due to the wide point spread function (PSF) and the blending of stellar light in such dense regions. We developed an Image-Subtraction-based K2 reduction pipeline that is applicable to both crowded and sparse stellar fields. We applied our pipeline to the data-rich C0 K2 super-stamp, containing the two open clusters, as well as to the neighboring postage stamps. In this paper, we present our Image Subtraction reduction pipeline and demonstrate that this technique achieves ultra-high photometric precision for sources in the C0 super-stamp. We extract the raw light curves of 3960 stars taken from the UCAC4 and EPIC catalogs and de-trend them for systematic effects. We compare our photometric results with the prior reductions published in the literature. For detrended, TFA-corrected sources in the 12--12.25 $\rm K_{p}$ magnitude range, we achieve a best 6.5 hour window running rms of 35 ppm falling to 100 ppm for fainter stars in the 14--14.25 $ \rm K_{p}$ magnitude range. For stars with $\rm K_{p}> 14$, our detrended and 6.5 hour binned light curves achieve the highest photometric precision. Moreover, all our TFA-corrected sources have higher precision on all time scales investigated. This work represents the first published Image Subtraction analysis of a K2 super-stamp. This method will be particularly useful for analyzing the Galactic bulge observations carried out during K2 campaign 9. The raw light curves and the final results of our detrending processes are publicly available at \url{this http URL}.
Krzysztof Z. Stanek - One of the best experts on this subject based on the ideXlab platform.
-
Reanalysis of Very Large Telescope Data for M83 with Image Subtraction—Ninefold Increase in Number of Cepheids
The Astrophysical Journal, 2003Co-Authors: Alceste Z. Bonanos, Krzysztof Z. StanekAbstract:We apply the Image-Subtraction method in order to reanalyze the ESO Very Large Telescope data on M83 (NGC 5236), obtained and analyzed by Thim et al. Whereas Thim et al. found 12 Cepheids with periods between 12 and 55 days, we find 112 Cepheids with periods ranging from 7 to 91 days as well as ~60 other variables. These include two candidate eclipsing binaries, which, if confirmed, would be the first optically discovered outside the Local Group. We thus demonstrate that the Image-Subtraction method is much more powerful for detecting variability, especially in crowded fields. However, Hubble Space Telescope observations are necessary to obtain a Cepheid period-luminosity distance not dominated by blending and crowding. We propose a "hybrid" approach, in which numerous Cepheids are discovered and characterized using large ground-based telescopes and then followed up with the Hubble Space Telescope to obtain precise distances.
-
reanalysis of very large telescope data for m83 with Image Subtraction ninefold increase in number of cepheids
The Astrophysical Journal, 2003Co-Authors: Alceste Z. Bonanos, Krzysztof Z. StanekAbstract:We apply the Image-Subtraction method in order to reanalyze the ESO Very Large Telescope data on M83 (NGC 5236), obtained and analyzed by Thim et al. Whereas Thim et al. found 12 Cepheids with periods between 12 and 55 days, we find 112 Cepheids with periods ranging from 7 to 91 days as well as ~60 other variables. These include two candidate eclipsing binaries, which, if confirmed, would be the first optically discovered outside the Local Group. We thus demonstrate that the Image-Subtraction method is much more powerful for detecting variability, especially in crowded fields. However, Hubble Space Telescope observations are necessary to obtain a Cepheid period-luminosity distance not dominated by blending and crowding. We propose a "hybrid" approach, in which numerous Cepheids are discovered and characterized using large ground-based telescopes and then followed up with the Hubble Space Telescope to obtain precise distances.
A Galyam - One of the best experts on this subject based on the ideXlab platform.
-
proper Image Subtraction optimal transient detection photometry and hypothesis testing
The Astrophysical Journal, 2016Co-Authors: Barak Zackay, Eran O. Ofek, A GalyamAbstract:Transient detection and flux measurement via Image Subtraction stand at the base of time domain astronomy. Due to the varying seeing conditions, the Image Subtraction process is non-trivial, and existing solutions suffer from a variety of problems. Starting from basic statistical principles, we develop the optimal statistic for transient detection, flux measurement, and any Image-difference hypothesis testing. We derive a closed-form statistic that: (1) is mathematically proven to be the optimal transient detection statistic in the limit of background-dominated noise, (2) is numerically stable, (3) for accurately registered, adequately sampled Images, does not leave Subtraction or deconvolution artifacts, (4) allows automatic transient detection to the theoretical sensitivity limit by providing credible detection significance, (5) has uncorrelated white noise, (6) is a sufficient statistic for any further statistical test on the difference Image, and, in particular, allows us to distinguish particle hits and other Image artifacts from real transients, (7) is symmetric to the exchange of the new and reference Images, (8) is at least an order of magnitude faster to compute than some popular methods, and (9) is straightforward to implement. Furthermore, we present extensions of this method that make it resilient to registration errors, color-refraction errors, and any noise source that can be modeled. In addition, we show that the optimal way to prepare a reference Image is the proper Image coaddition presented in Zackay & Ofek. We demonstrate this method on simulated data and real observations from the PTF data release 2. We provide an implementation of this algorithm in MATLAB and Python.
Barak Zackay - One of the best experts on this subject based on the ideXlab platform.
-
Proper Image Subtraction—Optimal Transient Detection, Photometry, and Hypothesis Testing
The Astrophysical Journal, 2016Co-Authors: Barak Zackay, Eran O. Ofek, Avishay Gal-yamAbstract:Transient detection and flux measurement via Image Subtraction stand at the base of time domain astronomy. Due to the varying seeing conditions, the Image Subtraction process is non-trivial, and existing solutions suffer from a variety of problems. Starting from basic statistical principles, we develop the optimal statistic for transient detection, flux measurement, and any Image-difference hypothesis testing. We derive a closed-form statistic that: (1) is mathematically proven to be the optimal transient detection statistic in the limit of background-dominated noise, (2) is numerically stable, (3) for accurately registered, adequately sampled Images, does not leave Subtraction or deconvolution artifacts, (4) allows automatic transient detection to the theoretical sensitivity limit by providing credible detection significance, (5) has uncorrelated white noise, (6) is a sufficient statistic for any further statistical test on the difference Image, and, in particular, allows us to distinguish particle hits and other Image artifacts from real transients, (7) is symmetric to the exchange of the new and reference Images, (8) is at least an order of magnitude faster to compute than some popular methods, and (9) is straightforward to implement. Furthermore, we present extensions of this method that make it resilient to registration errors, color-refraction errors, and any noise source that can be modeled. In addition, we show that the optimal way to prepare a reference Image is the proper Image coaddition presented in Zackay & Ofek. We demonstrate this method on simulated data and real observations from the PTF data release 2. We provide an implementation of this algorithm in MATLAB and Python.
-
proper Image Subtraction optimal transient detection photometry and hypothesis testing
The Astrophysical Journal, 2016Co-Authors: Barak Zackay, Eran O. Ofek, A GalyamAbstract:Transient detection and flux measurement via Image Subtraction stand at the base of time domain astronomy. Due to the varying seeing conditions, the Image Subtraction process is non-trivial, and existing solutions suffer from a variety of problems. Starting from basic statistical principles, we develop the optimal statistic for transient detection, flux measurement, and any Image-difference hypothesis testing. We derive a closed-form statistic that: (1) is mathematically proven to be the optimal transient detection statistic in the limit of background-dominated noise, (2) is numerically stable, (3) for accurately registered, adequately sampled Images, does not leave Subtraction or deconvolution artifacts, (4) allows automatic transient detection to the theoretical sensitivity limit by providing credible detection significance, (5) has uncorrelated white noise, (6) is a sufficient statistic for any further statistical test on the difference Image, and, in particular, allows us to distinguish particle hits and other Image artifacts from real transients, (7) is symmetric to the exchange of the new and reference Images, (8) is at least an order of magnitude faster to compute than some popular methods, and (9) is straightforward to implement. Furthermore, we present extensions of this method that make it resilient to registration errors, color-refraction errors, and any noise source that can be modeled. In addition, we show that the optimal way to prepare a reference Image is the proper Image coaddition presented in Zackay & Ofek. We demonstrate this method on simulated data and real observations from the PTF data release 2. We provide an implementation of this algorithm in MATLAB and Python.