Sparse modeling is widely used in image processing, signal processing, and machine learning recently. Thanks to the research and progress in statistical mathematics along with the evolution of computational power, the technique is supposed to be applicable to the radio interferometric imaging for the data obtained by ALMA (Atacama Large Millimeter-submillimeter Array). We've developed a new imaging tool based on the sparse modeling approach and experimentally implemented on CASA (Common Astronomy Software Application) which is an official reduction software for the ALMA data. The poster presentation gives supplemental information to the oral talk in session 11 by Nakazato et al. The new imaging technique with sparse modeling is a computationally intense process even for the latest CPUs. In the poster, several ideas and practices to reduce the calculation time will be presented. The comparison to CLEAN imaging by using artificial data (simulated data) will also be presented.
Link to PDF (may not be available yet): P12-9.pdf