The scale of ongoing and future electromagnetic surveys pose formidable challenges to classify astronomical objects. Pioneering efforts on this front include citizen science campaigns adopted by the Sloan Digital Sky Survey (SDSS). SDSS datasets have been recently used to train neural network models to classify galaxies in the Dark Energy Survey (DES) that overlap the footprint of both surveys. Herein, we demonstrate that knowledge from deep learning algorithms, pre-trained with real-object images, can be transferred to classify galaxies that overlap both SDSS and DES surveys, achieving state-of-the-art accuracy ∼ > 99.6%. We demonstrate that this process can be completed within just eight minutes using distributed training. While this represents a significant step towards the classification of DES galaxies that overlap previous surveys, we need to initiate the characterization of unlabelled DES galaxies in new regions of parameter space. To accelerate this program, we use our neural network classifier to label over ten thousand unlabelled DES galaxies, which do not overlap previous surveys. Furthermore, we use our neural network model as a feature extractor for unsupervised clustering and find that unlabeled DES images can be grouped together in two distinct galaxy classes based on their morphology, which provides a heuristic check that the learning is successfully transferred to the classification of unlabelled DES images. We conclude by showing that these newly labeled datasets can be combined with unsupervised recursive training to create large-scale DES galaxy catalogs in preparation for the Large Synoptic Survey Telescope era.
An ever increasing number of gravitational wave detections with the LIGO and Virgo observatories has firmly established the existence of binary black hole mergers. Elucidating the astrophysical environments where these objects form and coalesce is an active area of research. Motivated by recent electromagnetic observations which suggest the existence of compact binary populations in the Galactic Cluster M22 [1] and in the Galactic center [2], and considering that eccentricity provides one of the cleanest signatures to identify these compact binary populations, in this article we study the importance of including higher-order waveform multipoles to enable gravitational wave observations of eccentric binary black hole mergers. Using a catalog of Einstein Toolkit numerical relativity simulations that describe eccentric, non-spinning black holes mergers with mass-ratios 1 ≤ q ≤ 10, and eccentricities e0 ∼ < 0.2 ten cycles before merger, we determine the mass-ratio, eccentricity and binary inclination angle combinations that maximize the contribution of the higher-order waveform multipoles ( , |m|) = {(2, for gravitational wave detection. We then explore the implications of these results in the context of stellar mass black holes that are detectable by LIGO detectors at design sensitivity, and show that compared to models that only include the ( , |m|) = (2, 2) mode, the inclusion of higher-order waveform multipoles can increase the signal-to-noise ratio of eccentric binary black hole mergers by up to ∼ 45% for mass-ratio binaries q ≤ 10. Furthermore, building upon our pioneering deep learning work [3,4], we show for the first time that machine learning can accurately reconstruct higher-order waveform multipole signals from eccentric binary black mergers embedded in real LIGO data.
Abstract. With increasing crop water demands and drought threats, mapping and monitoring of cropland evapotranspiration (ET) at high spatial and temporal resolutions become increasingly critical for water management and sustainability. However, estimating ET from satellites for precise water resource management is still challenging due to the limitations in both existing ET models and satellite input data. Specifically, the process of ET is complex and difficult to model, and existing satellite remote-sensing data could not fulfill high resolutions in both space and time. To address the above two issues, this study presents a new high spatiotemporal resolution ET mapping framework, i.e., BESS-STAIR, which integrates a satellite-driven water–carbon–energy coupled biophysical model, BESS (Breathing Earth System Simulator), with a generic and fully automated fusion algorithm, STAIR (SaTallite dAta IntegRation). In this framework, STAIR provides daily 30 m multispectral surface reflectance by fusing Landsat and MODIS satellite data to derive a fine-resolution leaf area index and visible/near-infrared albedo, all of which, along with coarse-resolution meteorological and CO2 data, are used to drive BESS to estimate gap-free 30 m resolution daily ET. We applied BESS-STAIR from 2000 through 2017 in six areas across the US Corn Belt and validated BESS-STAIR ET estimations using flux-tower measurements over 12 sites (85 site years). Results showed that BESS-STAIR daily ET achieved an overall R2=0.75, with root mean square error RMSE =0.93 mm d−1 and relative error RE =27.9 % when benchmarked with the flux measurements. In addition, BESS-STAIR ET estimations captured the spatial patterns, seasonal cycles, and interannual dynamics well in different sub-regions. The high performance of the BESS-STAIR framework primarily resulted from (1) the implementation of coupled constraints on water, carbon, and energy in BESS, (2) high-quality daily 30 m data from the STAIR fusion algorithm, and (3) BESS's applicability under all-sky conditions. BESS-STAIR is calibration-free and has great potentials to be a reliable tool for water resource management and precision agriculture applications for the US Corn Belt and even worldwide given the global coverage of its input data.
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