1904.00011
Measuring star-formation histories, distances, and metallicities with pixel color-magnitude diagrams I: model definition and mock tests
Cook, Conroy, van Dokkum, Speagle
We present a comprehensive study of the applications of the pixel color-magnitude diagram (pCMD) technique for measuring star formation histories (SFHs) and other stellar population parameters of galaxies, and demonstrate that the technique can also constrain distances. SFHs have previously been measured through either the modeling of resolved-star CMDs or of integrated-light SEDs, yet neither approach can easily be applied to galaxies in the "semi-resolved regime". The pCMD technique has previously been shown to have the potential to measure stellar populations and star formation histories in semi-resolved galaxies. Here we present Pixel Color-Magnitude Diagrams with Python (pcmdpy), a GPU-accelerated package that makes significant computational improvements to the original code and including more realistic physical models. These advances include the simultaneous fitting of distance, modeling a Gaussian metallicity-distribution function, and an observationally-motivated dust model. GPU-acceleration allows these more realistic models to be fit roughly 7x faster than the simpler models in the original code. We present results from a suite of mock tests, showing that with proper model assumptions, the code can simultaneously recover SFH, [Fe/H], distance, and dust extinction. Our results suggest the code, applied to observations with HST-like resolution, should constrain these properties with high precision within 10 Mpc and can be applied to systems out to as far as 100 Mpc. pCMDs open a new window to studying the stellar populations of many galaxies that cannot be readily studied through other means.
Measuring star-formation histories, distances, and metallicities with pixel color-magnitude diagrams I: model definition and mock tests
Cook, Conroy, van Dokkum, Speagle
We present a comprehensive study of the applications of the pixel color-magnitude diagram (pCMD) technique for measuring star formation histories (SFHs) and other stellar population parameters of galaxies, and demonstrate that the technique can also constrain distances. SFHs have previously been measured through either the modeling of resolved-star CMDs or of integrated-light SEDs, yet neither approach can easily be applied to galaxies in the "semi-resolved regime". The pCMD technique has previously been shown to have the potential to measure stellar populations and star formation histories in semi-resolved galaxies. Here we present Pixel Color-Magnitude Diagrams with Python (pcmdpy), a GPU-accelerated package that makes significant computational improvements to the original code and including more realistic physical models. These advances include the simultaneous fitting of distance, modeling a Gaussian metallicity-distribution function, and an observationally-motivated dust model. GPU-acceleration allows these more realistic models to be fit roughly 7x faster than the simpler models in the original code. We present results from a suite of mock tests, showing that with proper model assumptions, the code can simultaneously recover SFH, [Fe/H], distance, and dust extinction. Our results suggest the code, applied to observations with HST-like resolution, should constrain these properties with high precision within 10 Mpc and can be applied to systems out to as far as 100 Mpc. pCMDs open a new window to studying the stellar populations of many galaxies that cannot be readily studied through other means.
1904.00125
Idenetifying solar flare precursors using time series of SDO/HMI images and SHARP parameters
Chen, et al
We present several methods towards construction of precursors, which show great promise towards early predictions, of solar flare events in this paper. A data pre-processing pipeline is built to extract useful data from multiple sources (Geostationary Operational Environmental Satellites (GOES) and Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) to prepare inputs for machine learning algorithms. Two classification models are presented: classification of flares from quiet times for active regions and classification of strong versus weak flare events. We adopt deep learning algorithms to capture both the spatial and temporal information from HMI magnetogram data. Effective feature extraction and feature selection with raw magnetogram data using deep learning and statistical algorithms enable us to train classification models to achieve almost as good performance as using active region parameters provided in HMI/Space-Weather HMI-Active Region Patch (SHARP) data files. The results show great promise towards accurate, reliable, and timely predictions of solar flare events. The use of Atmospheric Imaging Assembly (AIA) data will be the topic of future studies.
1904.00536
Possibility of a coordinated signaling scheme in the Galaxy and SETI experiments
Seto
We discuss a Galaxy-wide coordinated signaling scheme with which a SETI observer needs to examine a tiny fraction of the sky. The target sky direction is determined as a function of time, based on high-precision measurements of a progenitor of a conspicuous astronomical event such as a coalescence of a double neutron star binary. In various respects, such a coordinated scheme would be advantageous for both transmitters and receivers, and might be widely prevailing as a tacit adjustment. For this scheme, the planned space gravitational-wave detector LISA and its follow-on missions have a potential to narrow down the target sky area by a factor of $10^{3\textit{-}4}$, and could have a large impact on future SETI experiments.
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