Submillimetre galaxies in a hierarchical universe: number counts, redshift distribution and implications for the IMF

High-redshift submillimetre galaxies (SMGs) are some of the most rapidly star-forming galaxies in the Universe. Historically, galaxy formation models have had difficulty explaining the observed number counts of SMGs. We combine a semi-empirical model with 3D hydrodynamical simulations and 3D dust radiative transfer to predict the number counts of unlensed SMGs. Because the stellar mass functions, gas and dust masses, and sizes of our galaxies are constrained to match observations, we can isolate uncertainties related to the dynamical evolution of galaxy mergers and the dust radiative transfer. The number counts and redshift distributions predicted by our model agree well with observations. Isolated disc galaxies dominate the faint (S_(1.1) ≲ 1 or S_(850) ≲ 2 mJy) population. The brighter sources are a mix of merger-induced starbursts and galaxy-pair SMGs; the latter subpopulation accounts for ∼30–50 per cent of all SMGs at all S_(1.1) ≳ 0.5 mJy (S_(850) ≳ 1 mJy). The mean redshifts are ∼3.0–3.5, depending on the flux cut, and the brightest sources tend to be at higher redshifts. Because the galaxy-pair SMGs will be resolved into multiple fainter sources by the Atacama Large Millimeter/submillimeter Array (ALMA), the bright ALMA counts should be as much as two times less than those observed using single-dish telescopes. The agreement between our model, which uses a Kroupa initial mass function (IMF), and observations suggests that the IMF in high-redshift starbursts need not be top heavy; if the IMF were top heavy, our model would overpredict the number counts. We conclude that the difficulty some models have reproducing the observed SMG counts is likely indicative of more general problems – such as an underprediction of the abundance of massive galaxies or a star formation rate and stellar mass relation normalization lower than that observed – rather than a problem specific to the SMG population

A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

We present a tutorial on Bayesian optimization, a method of finding the maximum of expensive cost functions. Bayesian optimization employs the Bayesian technique of setting a prior over the objective function and combining it with evidence to get a posterior function. This permits a utility-based selection of the next observation to make on the objective function, which must take into account both exploration (sampling from areas of high uncertainty) and exploitation (sampling areas likely to offer improvement over the current best observation). We also present two detailed extensions of Bayesian optimization, with experiments---active user modelling with preferences, and hierarchical reinforcement learning---and a discussion of the pros and cons of Bayesian optimization based on our experiences

Observations of "Fresh" and Weathered Surfaces on Asteroid Pairs and Their Implications on the Rotational-Fission Mechanism

The rotational-fission of a rubble-pile asteroid can result in an "asteroid pair", two un-bound asteroids sharing similar orbits. This mechanism might exposes material that previously had never have been exposed to the weathering conditions of space. Therefore, the surfaces of asteroid pairs offer the opportunity to observe non-weathered fresh spectra. We report near-IR spectroscopic observations of 31 asteroids in pairs. We analyze their spectral slopes, 1 {\mu}m absorption band, taxonomy, and estimate the time elapsed since their separation. Analyzing the 19 S-complex objects in our sample, we find two fresh Q-type asteroids that are the first of their kind to be observed in the main-belt over the full visible and near-IR range. This solidly demonstrates that Q-type objects are not limited to the NEA population. The pairs in our sample present a range of fresh and weathered surfaces with no clear evidence for a correlation with the ages of the pairs. However, our sample includes old pairs (1 to 2 My) that present low spectral slopes. This illustrates a timescale of at least ~2 My before an object develops high spectral slope that is typical for S-type asteroids. We discuss mechanisms that explain the existence of weathered pairs with young dynamical ages and find that the "secondary fission" model (Jacobson & Scheeres 2011) is the most robust with our observations since: 1) the secondary members in our sample present fresh parameters that tend to be fresher than their weathered primaries; 2) most of the fresh pairs in our sample have low size ratios between the secondary and the primary; 3) 33% of the primaries in our sample are fresh, similar to the prediction set by this model; 4) known satellites orbit two of the pairs in our sample with low size ratio and fresh surface; 5) there is no correlation between the weathering state and the primary shape as predicted by other models.Comment: 19 pages, 17 figures, 4 tables. Accepted to Icaru

The Conservation Reserve Program: Economic Implications for Rural America

This report estimates the impact that high levels of enrollment in the Conservation Reserve Program (CRP) have had on economic trends in rural counties since the program's inception in 1985 until today. The results of a growth model and quasi-experimental control group analysis indicate no discernible impact by the CRP on aggregate county population trends. Aggregate employment growth may have slowed in some high-CRP counties, but only temporarily. High levels of CRP enrollment appear to have affected farm-related businesses over the long run, but growth in the number of other nonfarm businesses moderated CRP's impact on total employment. If CRP contracts had ended in 2001, simulation models suggest that roughly 51 percent of CRP land would have returned to crop production, and that spending on outdoor recreation would decrease by as much as $300 million per year in rural areas. The resulting impacts on employment and income vary widely among regions having similar CRP enrollments, depending upon local economic conditions.Community/Rural/Urban Development, Land Economics/Use,

Quantifying short-range correlations in nuclei

Background: Short-range correlations (SRC) are an important ingredient of the dynamics of nuclei. Purpose: An approximate method to quantify the magnitude of the two-nucleon (2N) and three-nucleon (3N) short-range correlations and their mass dependence is proposed. Method: The proposed method relies on the concept of the "universality" or "local nuclear character" of the SRC. We quantify the SRC by computing the number of independent-particle model (IPM) nucleon pairs and triples which reveal beyond-mean-field behavior. It is argued that those can be identified by counting the number of nucleon pairs and triples in a zero relative orbital momentum state. A method to determine the quantum numbers of pairs and triples in an arbitrary mean-field basis is outlined. Results: The mass dependence of the 2N and 3N SRC is studied. The predictions are compared to measurements. This includes the ratio of the inclusive inelastic electron scattering cross sections of nuclei to H-2 and He-3 at large values of the Bjorken variable. Corrections stemming from the center-of-mass motion of the pairs are estimated. Conclusions: We find that the relative probability per nucleon for 2N and 3N SRC has a soft dependence with mass number A and that the proton-neutron 2N SRC outnumber the proton-proton (neutron-neutron) 2N SRC. A linear relationship between the magnitude of the EMC effect and the predicted number of proton-neutron SRC pairs is observed. This provides support for the role of local nuclear dynamics on the EMC effect

Learning Repeatable Speech Embeddings Using An Intra-class Correlation Regularizer

A good supervised embedding for a specific machine learning task is only sensitive to changes in the label of interest and is invariant to other confounding factors. We leverage the concept of repeatability from measurement theory to describe this property and propose to use the intra-class correlation coefficient (ICC) to evaluate the repeatability of embeddings. We then propose a novel regularizer, the ICC regularizer, as a complementary component for contrastive losses to guide deep neural networks to produce embeddings with higher repeatability. We use simulated data to explain why the ICC regularizer works better on minimizing the intra-class variance than the contrastive loss alone. We implement the ICC regularizer and apply it to three speech tasks: speaker verification, voice style conversion, and a clinical application for detecting dysphonic voice. The experimental results demonstrate that adding an ICC regularizer can improve the repeatability of learned embeddings compared to only using the contrastive loss; further, these embeddings lead to improved performance in these downstream tasks.Comment: Accepted by NeurIPS 202

Forecast Combination with Multiple Models and Expert Correlations

Combining multiple forecasts in order to generate a single, more accurate one is a well-known approach. A simple average of forecasts has been found to be robust despite theoretically better approaches, increasing availability in the number of expert forecasts, and improved computational capabilities. The dominance of a simple average is related to the small sample sizes and to the estimation errors associated with more complex methods. We study the role that expert correlation, multiple experts, and their relative forecasting accuracy have on the weight estimation error distribution. The distributions we find are used to identify the conditions when a decision maker can confidently estimate weights versus using a simple average. We also propose an improved expert weighting approach that is less sensitive to covariance estimation error while providing much of the benefit from a covariance optimal weight. These two improvements create a new heuristic for better forecast aggregation that is simple to use. This heuristic appears new to the literature and is shown to perform better than a simple average in a simulation study and by application to economic forecast data

Expected Performance of the ATLAS Experiment - Detector, Trigger and Physics

A detailed study is presented of the expected performance of the ATLAS detector. The reconstruction of tracks, leptons, photons, missing energy and jets is investigated, together with the performance of b-tagging and the trigger. The physics potential for a variety of interesting physics processes, within the Standard Model and beyond, is examined. The study comprises a series of notes based on simulations of the detector and physics processes, with particular emphasis given to the data expected from the first years of operation of the LHC at CERN