The Environment of HII Galaxies revisited

We present a study of the close (< 200 kpc) environment of 110 relatively local (z< 0.16) HII galaxies, selected from the Sloan Digital Sky Survey (SDSS; DR7). We use available spectroscopic and photometric redshifts in order to investigate the presence of a close and possibly interacting companion galaxy. Our aim is to compare the physical properties of isolated and interacting HII galaxies and investigate possible systematic effects in their use as cosmological probes. We find that interacting HII galaxies tend to be more compact, less luminous and have a lower velocity dispersion than isolated ones, in agreement with previous studies on smaller samples. However, as we verified, these environmental differences do not affect the cosmologically important L_{H{\beta}}-{\sigma} correlation of the HII galaxies.Comment: 5 pages, accepted for publication in A&

On the limits of measuring the bulge and disk properties of local and high-redshift massive galaxies

A considerable fraction of the massive quiescent galaxies at \emph{z} $\approx$ 2, which are known to be much more compact than galaxies of comparable mass today, appear to have a disk. How well can we measure the bulge and disk properties of these systems? We simulate two-component model galaxies in order to systematically quantify the effects of non-homology in structures and the methods employed. We employ empirical scaling relations to produce realistic-looking local galaxies with a uniform and wide range of bulge-to-total ratios ($B/T$), and then rescale them to mimic the signal-to-noise ratios and sizes of observed galaxies at \emph{z} $\approx$ 2. This provides the most complete set of simulations to date for which we can examine the robustness of two-component decomposition of compact disk galaxies at different $B/T$. We confirm that the size of these massive, compact galaxies can be measured robustly using a single S\'{e}rsic fit. We can measure $B/T$ accurately without imposing any constraints on the light profile shape of the bulge, but, due to the small angular sizes of bulges at high redshift, their detailed properties can only be recovered for galaxies with $B/T$ \gax\ 0.2. The disk component, by contrast, can be measured with little difficulty

Variations of the ISM Compactness Across the Main Sequence of Star-Forming Galaxies: Observations and Simulations

(abridged) The majority of star-forming galaxies follow a simple empirical correlation in the star formation rate (SFR) versus stellar mass ($M_*$) plane, usually referred to as the star formation Main Sequence (MS). Here we combine a set of hydro-dynamical simulations of interacting galactic disks with state-of-the-art radiative transfer codes to analyze how the evolution of mergers is reflected upon the properties of the MS. We present \textsc{Chiburst}, a Markov Chain Monte Carlo (MCMC) Spectral Energy Distribution (SED) code that fits the multi-wavelength, broad-band photometry of galaxies and derives stellar masses, star formation rates, and geometrical properties of the dust distribution. We apply this tool to the SEDs of simulated mergers and compare the derived results with the reference output from the simulations. Our results indicate that changes in the SEDs of mergers as they approach coalescence and depart from the MS are related to an evolution of dust geometry in scales larger than a few hundred parsecs. This is reflected in a correlation between the specific star formation rate (sSFR), and the compactness parameter $\mathcal{C}$, that parametrizes this geometry and hence the evolution of dust temperature ($T_{\rm{dust}}$) with time. As mergers approach coalescence, they depart from the MS and increase their compactness, which implies that moderate outliers of the MS are consistent with late-type mergers. By further applying our method to real observations of Luminous Infrared Galaxies (LIRGs), we show that the merger scenario is unable to explain these extreme outliers of the MS. Only by significantly increasing the gas fraction in the simulations are we able to reproduce the SEDs of LIRGs.Comment: 18 pages, 10 figures, accepted in Ap

Image Analysis and Machine Learning in Agricultural Research

Agricultural research has been a focus for academia and industry to improve human well-being. Given the challenges in water scarcity, global warming, and increased prices of fertilizer, and fossil fuel, improving the efficiency of agricultural research has become even more critical. Data collection by humans presents several challenges including: 1) the subjectiveness and reproducibility when doing the visual evaluation, 2) safety when dealing with high toxicity chemicals or severe weather events, 3) mistakes cannot be avoided, and 4) low efficiency and speed. Image analysis and machine learning are more versatile and advantageous in evaluating different plant characteristics, and this could help with agricultural data collection. In the first chapter, information related to different types of imaging (e.g., RGB, multi/hyperspectral, and thermal imaging) was explored in detail for its advantages in different agriculture applications. The process of image analysis demonstrated how target features were extracted for analysis including shape, edge, texture, and color. After acquiring features information, machine learning can be used to automatically detect or predict features of interest such as disease severity. In the second chapter, case studies of different agricultural applications were demonstrated including: 1) leaf damage symptoms, 2) stress evaluation, 3) plant growth evaluation, 4) stand/insect counting, and 5) evaluation for produce quality. Case studies showed that the use of image analysis is often more advantageous than visual rating. Advantages of image analysis include increased objectivity, speed, and more reproducibly reliable results. In the third chapter, machine learning was explored using romaine lettuce images from RD4AG to automatically grade for bolting and compactness (two of the important parameters for lettuce quality). Although the accuracy is at 68.4 and 66.6% respectively, a much larger data base and many improvements are needed to increase the model accuracy and reliability. With the advancement in cameras, computers with high computing power, and the development of different algorithms, image analysis and machine learning have the potential to replace part of the labor and improve the current data collection procedure in agricultural research. Advisor: Gary L. Hei

Long-baseline optical intensity interferometry: Laboratory demonstration of diffraction-limited imaging

A long-held vision has been to realize diffraction-limited optical aperture synthesis over kilometer baselines. This will enable imaging of stellar surfaces and their environments, and reveal interacting gas flows in binary systems. An opportunity is now opening up with the large telescope arrays primarily erected for measuring Cherenkov light in air induced by gamma rays. With suitable software, such telescopes could be electronically connected and also used for intensity interferometry. Second-order spatial coherence of light is obtained by cross correlating intensity fluctuations measured in different pairs of telescopes. With no optical links between them, the error budget is set by the electronic time resolution of a few nanoseconds. Corresponding light-travel distances are approximately one meter, making the method practically immune to atmospheric turbulence or optical imperfections, permitting both very long baselines and observing at short optical wavelengths. Previous theoretical modeling has shown that full images should be possible to retrieve from observations with such telescope arrays. This project aims at verifying diffraction-limited imaging experimentally with groups of detached and independent optical telescopes. In a large optics laboratory, artificial stars were observed by an array of small telescopes. Using high-speed photon-counting solid-state detectors, intensity fluctuations were cross-correlated over up to 180 baselines between pairs of telescopes, producing coherence maps across the interferometric Fourier-transform plane. These measurements were used to extract parameters about the simulated stars, and to reconstruct their two-dimensional images. As far as we are aware, these are the first diffraction-limited images obtained from an optical array only linked by electronic software, with no optical connections between the telescopes.Comment: 13 pages, 9 figures, Astronomy & Astrophysics, in press. arXiv admin note: substantial text overlap with arXiv:1407.599