Using Machine Learning to Study the Relationship Between Galaxy Morphology and Evolution

We can track the physical evolution of massive galaxies over time by characterizing the morphological signatures inherent to different mechanisms of galactic assembly. Structural studies rely on a small set of measurements to bin galaxies into disk, spheroid and irregular classifications. These classes are correlated with colors, SF history and stellar masses. Rare and subtle features that are lost in such a generic classification scheme are important for characterizing the evolution of galaxy morphology. We can connect the Hubble sequence observed for local galaxies to their high redshift progenitors to determine the full distribution of galaxy morphologies as a function of time over the entire lifetime of the Universe. To fully capture the complex morphological transformation of galaxies we need more useful classifications. To accomplish such a feat in a computationally tractable way we will need to convert galaxy images to low-dimensional representations of only a few parameters

Using Machine Learning to Study the Relationship Between Galaxy Morphology and Evolution

We can track the physical evolution of massive galaxies over time by characterizing the morphological signatures inherent to different mechanisms of galactic assembly. Structural studies rely on a small set of measurements to bin galaxies into disk, spheroid and irregular classifications. These classes are correlated with colors, SF history and stellar masses. Rare and subtle features that are lost in such a generic classification scheme are important for characterizing the evolution of galaxy morphology. We can connect the Hubble sequence observed for local galaxies to their high redshift progenitors to determine the full distribution of galaxy morphologies as a function of time over the entire lifetime of the Universe. To fully capture the complex morphological transformation of galaxies we need more useful classifications. To accomplish such a feat in a computationally tractable way we will need to convert galaxy images to low-dimensional representations of only a few parameters. To overcome the limitations of the Hubble sequence, we use a principal component analysis of non-parametric morphological indicators (concentration, asymmetry, Gini coefficient, M20, multi-mode, intensity and deviation) measured at rest-frame B-band (corresponding to HST/WFC3 F125W at 1.4 10^10 Msun) galaxy morphologies. Principal component analysis (PCA) quantifies the correlations between these morphological indicators and determines the relative importance of each. The first three principal components (PCs) capture ~75% of the variance inherent to our sample. We interpret the first principal component (PC) as bulge strength, the second PC as dominated by concentration and the third PC as dominated by asymmetry. PC1 is a better predictor of quenching than stellar mass, as good as other structural indicators (Sersic-n or compactness). We divide the PCA results into groups using an agglomerative hierarchical clustering method. Distinguishing between these galaxy structural types in a quantitative manner is an important step towards understanding the connections between morphology, galaxy assembly and star-formation. Using a random forest classification technique, we are able to distinguish mergers from non-merger galaxies in Pan-STARRS imaging using a variety of input features (PCs, non-parametric morphologies, sSFR, M*, rest-frame color). Determining if a galaxy is a merger is important to understand how influential mergers are in building bulges and assembling galaxies. The galaxies were initially visually classified by users of Galaxy Zoo. Asymmetry is by far the most important indicator of whether a galaxy is experiencing a merger. The next most important features include: PC7, PC5, PC3, deviation and d(G,M20). The importance of PC7 represents a very interesting result because PC7 is the least important PC but plays a huge role in determining whether a galaxy is a merger. Galaxy simulations can provide valuable insight into the mechanisms behind galaxy evolution. The VELA simulations and subsequent non-parametric morphological measurements provide a resource to study the connection between morphology (through the use of PC results) and physical properties (such as sSFR, gas fraction, etc.). We stack the results of a discrete cross correlation between PCs and physical parameters from 9 VELA galaxies. Each of the first three PCs correlates differently with these physical parameters: PC1 is correlated strongly with ex-situ stellar mass, the gas fraction and sSFR; PC2 is weakly anti-correlated with all physical properties; PC3 is strongly correlated with sSFR at all length scales and with gas fraction in the central kpc. The process of star-formation, gas accretion and bulge assembly is a messy picture that will require more simulate galaxies to further understand the process of galaxy evolution

Who Should Bear the Risk? A Theoretical and Behavioral Investigation of After-Sales Service Contracts

Since downtime is expensive, it is key to use the right after-sales service contract to achieve high equipment availability. Resource-based contracts (RBCs) are common, but they fail to motivate suppliers to provide reliable products and services as suppliers are paid for their after-sales services. Performance-based contracts (PBCs) have been proposed as a way to solve this issue, as it shifts much of the downtime risk to the supplier by making him responsible for machine uptime, but then customers might reduce care efforts. We are the first to analytically incorporate the care in equipment availability. We propose the full-care contract (FCC) to achieve both high reliability and care, and maximize the chain efficiency. We find that only the FCC can achieve full chain efficiency. After discussing potential behavioral factors in this context, with a focus on risk aversion, we conduct a lab study with decision makers as suppliers. Experimental results confirm that the FCC achieves higher total profits than the PBC and RBC. We further find that subjects are more likely to switch from the RBC to the FCC than to the PBC, despite the higher risk involved in the FCC.Finally, we observe that effort levels set by suppliers are above normative predictions and we discuss potential explanations for this result

Diverse Structural Evolution at z > 1 in Cosmologically Simulated Galaxies

From mock Hubble Space Telescope images, we quantify non-parametric statistics of galaxy morphology, thereby predicting the emergence of relationships among stellar mass, star formation, and observed rest-frame optical structure at 1 < z < 3. We measure automated diagnostics of galaxy morphology in cosmological simulations of the formation of 22 central galaxies with 9.3 < log10 M_*/M_sun < 10.7. These high-spatial-resolution zoom-in calculations enable accurate modeling of the rest-frame UV and optical morphology. Even with small numbers of galaxies, we find that structural evolution is neither universal nor monotonic: galaxy interactions can trigger either bulge or disc formation, and optically bulge-dominated galaxies at this mass may not remain so forever. Simulated galaxies with M_* > 10^10 M_sun contain relatively more disc-dominated light profiles than those with lower mass, reflecting significant disc brightening in some haloes at 1 < z < 2. By this epoch, simulated galaxies with specific star formation rates below 10^-9.7 yr^-1 are more likely than normal star-formers to have a broader mix of structural types, especially at M_* > 10^10 M_sun. We analyze a cosmological major merger at z ~ 1.5 and find that the newly proposed MID morphology diagnostics trace later merger stages while G-M20 trace earlier ones. MID is sensitive also to clumpy star-forming discs. The observability time of typical MID-enhanced events in our simulation sample is less than 100 Myr. A larger sample of cosmological assembly histories may be required to calibrate such diagnostics in the face of their sensitivity to viewing angle, segmentation algorithm, and various phenomena such as clumpy star formation and minor mergers.Comment: 23 pages, 16 figures, MNRAS accepted versio

Multiperiod Inventory Management with Budget Cycles: Rational and Behavioral Decision-Making

We examine inventory decisions in a multiperiod newsvendor model. In particular, we analyze the impact of budget cycles in a behavioral setting. We derive optimal rational decisions and characterize the behavioral decision-making process using a short-sightedness factor. We test the aforementioned effect in a laboratory environment. We find that subjects reduce order-up-to levels significantly at the end of the current budget cycle, which results in a cyclic pattern during the budget cycle. This indicates that the subjects are short-sighted with respect to future budget cycles. To control for inventory that is carried over from one period to the next, we introduce a starting-inventory factor and find that order-up-to levels increase in the starting inventory

Beyond Spheroids and Discs: Classifications of CANDELS Galaxy Structure at 1.4 < z < 2 via Principal Component Analysis

Important but rare and subtle processes driving galaxy morphology and star-formation may be missed by traditional spiral, elliptical, irregular or S\'ersic bulge/disk classifications. To overcome this limitation, we use a principal component analysis of non-parametric morphological indicators (concentration, asymmetry, Gini coefficient, $M_{20}$, multi-mode, intensity and deviation) measured at rest-frame $B$-band (corresponding to HST/WFC3 F125W at 1.4 $10^{10} M_{\odot}$) galaxy morphologies. Principal component analysis (PCA) quantifies the correlations between these morphological indicators and determines the relative importance of each. The first three principal components (PCs) capture $\sim$75 per cent of the variance inherent to our sample. We interpret the first principal component (PC) as bulge strength, the second PC as dominated by concentration and the third PC as dominated by asymmetry. Both PC1 and PC2 correlate with the visual appearance of a central bulge and predict galaxy quiescence. PC1 is a better predictor of quenching than stellar mass, as as good as other structural indicators (S\'ersic-n or compactness). We divide the PCA results into groups using an agglomerative hierarchical clustering method. Unlike S\'ersic, this classification scheme separates compact galaxies from larger, smooth proto-elliptical systems, and star-forming disk-dominated clumpy galaxies from star-forming bulge-dominated asymmetric galaxies. Distinguishing between these galaxy structural types in a quantitative manner is an important step towards understanding the connections between morphology, galaxy assembly and star-formation.Comment: 31 pages, 24 figures, accepted for publication in MNRA