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Precise and accurate star formation histories from galaxy spectra and the path to life finding NIR detectors
Greg Mosby (NASA Goddard)
February 22, 2018 @ 4:00 PM - 5:00 PM
Many questions in extragalactic astronomy depend on interpreting integrated galaxy spectra. In the limit of low galaxy surface brightness, integrated spectra often have such low S/N that it hinders analysis with standard stellar population modeling techniques. To address this problem, we have developed a method that can recover galaxy star formation histories (SFHs) from rest frame optical spectra with S/N ~ 5 Å^-1 with a specific application to quasar host galaxies. We use the machine learning technique diffusion k-means to tailor the stellar population basis set, composed of 4 broad age bins, and it is successful in recovering a range of galaxy SFHs. We use synthetic data to compare results of our novel method with previous techniques. Our new method has the advantage in recovering information from quasar host galaxies and could also be applied to the analysis of other low S/N galaxy spectra such as that typically obtained for high redshift objects and integral field spectroscopic surveys. I have now begun using diffusion k-means to generate a multi-metallicity basis set to estimate the stellar mass and chemical evolution of unresolved galaxies. In addition, I have begun work to fully characterizing today’s HgCdTe photodiode arrays to lay the foundation for future near infrared detector development. Low read noise and well-characterized detectors are crucial in the emerging search for biosignatures in exoplanet atmospheres. By generating a detailed noise budget, characterizing persistence and intrapixel capacitance, we will identify the current limitations of existing hardware, amassing a new knowledge base on the detector physics and systematics of HgCdTe arrays.