ALMA Cycle 6 data on Aquila dense cores identifies two starless sources and scale-dependent increases in multiplicity, consistent with turbulent core collapse simulations predicting 1.19 starless detections.
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The paper proposes the iSEEDs project to integrate machine learning with astrochemistry for extracting physical conditions and molecular abundances from protostellar disk datasets.
SKAO, especially SKA-Mid Band 5, is expected to overcome dust opacity and frequency limits to detect complex prebiotic molecules in high-mass and solar-type protostellar regions.
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Fragmentation in the Serpens/Aquila Star-forming Region
ALMA Cycle 6 data on Aquila dense cores identifies two starless sources and scale-dependent increases in multiplicity, consistent with turbulent core collapse simulations predicting 1.19 starless detections.