College of Letters & Science
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The College of Letters and Science, the largest center for learning, teaching and research at Montana State University, offers students an excellent liberal arts and sciences education in nearly 50 majors, 25 minors and over 25 graduate degrees within the four areas of the humanities, natural sciences, mathematics and social sciences.
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Item Hundreds of Low-mass Active Galaxies in the Galaxy And Mass Assembly (GAMA) Survey(American Astronomical Society, 2022-09) Salehirad, Sheyda; Reines, Amy E.; Molina, MalloryWe present an entirely new sample of 388 low-mass galaxies (M ⋆ ≤ 1010 M ⊙) that have spectroscopic signatures indicating the presence of massive black holes (BHs) in the form of active galactic nuclei (AGNs) or tidal disruption events. Of these, 70 have stellar masses in the dwarf galaxy regime with 108 ≲ M ⋆/M ⊙ ≲ 109.5. We identify the active galaxies by analyzing optical spectra of a parent sample of ∼23,000 low-mass emission-line galaxies in the Galaxy and Mass Assembly (GAMA) Survey Data Release 4, and employing four different diagnostics based on narrow emission-line ratios and the detection of high-ionization coronal lines. We find that 47 of the 388 low-mass active galaxies exhibit broad Hα in their spectra, corresponding to virial BH masses in the range M BH ∼ 105.0–7.7 M ⊙ with a median BH mass of 〈M BH〉 ∼ 106.2 M ⊙. Our sample extends to higher redshifts (z ≤ 0.3; 〈z〉 = 0.13) than previous samples of AGNs in low-mass/dwarf galaxies based on Sloan Digital Sky Survey spectroscopy, which can be attributed to the spectroscopic limit of GAMA being ∼2 mag deeper. Moreover, our multi-diagnostic approach has revealed low-mass active galaxies spanning a wide range of properties, from blue star-forming dwarfs to luminous “miniquasars” powered by low-mass BHs. As such, this work has implications for BH seeding and AGN feedback at low masses.Item Multivariate Classification with Random Forests for Gravitational Wave Searches of Black Hole Binary Coalescence(2015-03) Baker, Paul T.; Caudill, Sarah; Hodge, Kari A.; Talukder, Dipongkar; Capano, Collin; Cornish, Neil J.Searches for gravitational waves produced by coalescing black hole binaries with total masses ≳25M⊙ use matched filtering with templates of short duration. Non-Gaussian noise bursts in gravitational wave detector data can mimic short signals and limit the sensitivity of these searches. Previous searches have relied on empirically designed statistics incorporating signal-to-noise ratio and signal-based vetoes to separate gravitational wave candidates from noise candidates. We report on sensitivity improvements achieved using a multivariate candidate ranking statistic derived from a supervised machine learning algorithm. We apply the random forest of bagged decision trees technique to two separate searches in the high mass (≳25M⊙) parameter space. For a search which is sensitive to gravitational waves from the inspiral, merger, and ringdown (IMR) of binary black holes with total mass between 25M⊙ and 100M⊙, we find sensitive volume improvements as high as 70±13−109±11\% when compared to the previously used ranking statistic. For a ringdown-only search which is sensitive to gravitational waves from the resultant perturbed intermediate mass black hole with mass roughly between 10M⊙ and 600M⊙, we find sensitive volume improvements as high as 61±4−241±12\% when compared to the previously used ranking statistic. We also report how sensitivity improvements can differ depending on mass regime, mass ratio, and available data quality information. Finally, we describe the techniques used to tune and train the random forest classifier that can be generalized to its use in other searches for gravitational waves.