Semi-Analytic Galaxy Evolution (SAGE): Model Calibration and Basic Results

This paper describes a new publicly available codebase for modelling galaxy formation in a cosmological context, the "Semi-Analytic Galaxy Evolution" model, or SAGE for short. SAGE is a significant update to that used in Croton et al. (2006) and has been rebuilt to be modular and customisable. The model will run on any N-body simulation whose trees are organised in a supported format and contain a minimum set of basic halo properties. In this work we present the baryonic prescriptions implemented in SAGE to describe the formation and evolution of galaxies, and their calibration for three N-body simulations: Millennium, Bolshoi, and GiggleZ. Updated physics include: gas accretion, ejection due to feedback, and reincorporation via the galactic fountain; a new gas cooling--radio mode active galactic nucleus (AGN) heating cycle; AGN feedback in the quasar mode; a new treatment of gas in satellite galaxies; and galaxy mergers, disruption, and the build-up of intra-cluster stars. Throughout, we show the results of a common default parameterization on each simulation, with a focus on the local galaxy population.Comment: 15 pages, 9 figures, accepted for publication in ApJS. SAGE is a publicly available codebase for modelling galaxy formation in a cosmological context, available at https://github.com/darrencroton/sage Questions and comments can be sent to Darren Croton: [email protected]

EM-Type Algorithms for DOA Estimation in Unknown Nonuniform Noise

The expectation--maximization (EM) algorithm updates all of the parameter estimates simultaneously, which is not applicable to direction of arrival (DOA) estimation in unknown nonuniform noise. In this work, we present several efficient EM-type algorithms, which update the parameter estimates sequentially, for solving both the deterministic and stochastic maximum--likelihood (ML) direction finding problems in unknown nonuniform noise. Specifically, we design a generalized EM (GEM) algorithm and a space-alternating generalized EM (SAGE) algorithm for computing the deterministic ML estimator. Simulation results show that the SAGE algorithm outperforms the GEM algorithm. Moreover, we design two SAGE algorithms for computing the stochastic ML estimator, in which the first updates the DOA estimates simultaneously while the second updates the DOA estimates sequentially. Simulation results show that the second SAGE algorithm outperforms the first one.Comment: arXiv admin note: text overlap with arXiv:2208.0751

Penalized Maximum-Likelihood Image Reconstruction Using Space-Alternating Generalized EM Algorithms

Most expectation-maximization (EM) type algorithms for penalized maximum-likelihood image reconstruction converge slowly, particularly when one incorporates additive background effects such as scatter, random coincidences, dark current, or cosmic radiation. In addition, regularizing smoothness penalties (or priors) introduce parameter coupling, rendering intractable the M-steps of most EM-type algorithms. This paper presents space-alternating generalized EM (SAGE) algorithms for image reconstruction, which update the parameters sequentially using a sequence of small “hidden” data spaces, rather than simultaneously using one large complete-data space. The sequential update decouples the M-step, so the maximization can typically be performed analytically. We introduce new hidden-data spaces that are less informative than the conventional complete-data space for Poisson data and that yield significant improvements in convergence rate. This acceleration is due to statistical considerations, not numerical overrelaxation methods, so monotonic increases in the objective function are guaranteed. We provide a general global convergence proof for SAGE methods with nonnegativity constraints.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85850/1/Fessler102.pd

Updated merged SAGE-CCI-OMPS+ dataset for the evaluation of ozone trends in the stratosphere

In this paper, we present the updated SAGE-CCI-OMPS+ climate data record of monthly zonal mean ozone profiles. This dataset covers the stratosphere and combines measurements by nine limb and occultation satellite instruments – SAGE II (Stratospheric Aerosol and Gases Experiment II), OSIRIS (Optical Spectrograph and InfraRed Imaging System), MIPAS (Michelson Interferometer for Passive Atmospheric Sounding), SCIAMACHY (SCanning Imaging Spectrometer for Atmospheric CHartographY), GOMOS (Global Ozone Monitoring by Occultation of Stars), ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer), OMPS-LP (Ozone Monitor Profiling Suite Limb Profiler), POAM (Polar Ozone and Aerosol Measurement) III, and SAGE III/ISS (Stratospheric Aerosol and Gases Experiment III on the International Space Station). Compared to the original version of the SAGE-CCI-OMPS dataset (Sofieva et al., 2017b), the update includes new versions of MIPAS, ACE-FTS, and OSIRIS datasets and introduces data from additional sensors (POAM III and SAGE III/ISS) and retrieval processors (OMPS-LP). In this paper, we show detailed intercomparisons of ozone profiles from different instruments and data versions, with a focus on the detection of possible drifts in the datasets. The SAGE-CCI-OMPS+ dataset has a better coverage of polar regions and of the upper troposphere and the lower stratosphere (UTLS) than the previous dataset. We also studied the influence of including new datasets on ozone trends, which are estimated using multiple linear regression. The changes in the merged dataset do not change the overall morphology of post-1997 ozone trends; statistically significant trends are observed in the upper stratosphere. The largest changes in ozone trends are observed in polar regions, especially in the Southern Hemisphere

Canadian Cooperative Wildlife Health Centre, Volume 9-2, Fall 2003

Type E Botulism in Fish-Eating Birds on Lake Huron and Lake Erie 1998-2003 Chronic Wasting Disease in free-ranging cervids in western Canada Esophageal Lesions in Crows Surplus killing of roseate terns and common terns by a mink West Nile Virus infection in Québec Raptors : 2003 season Mortality in Great Black-Backed Gulls near Presqu\u27ile Type E Botulism Update West Nile Virus Update Adenovirus Encephalitis in a Wild Fox Newcastle Disease Virus in Double-crested Cormorants in Alberta and Saskatchewan in 2003 West Nile Virus in Sage Grouse The Research Group for Arctic Parasitology (RGAP), November 2003 Death of a Grizzly Bear caused by capture and handling Diagnosing Disease in Wild Animals. February 25-27, 2004, Saskatoon, S

Canadian Cooperative Wildlife Health Centre, Volume 9-2, Fall 2003

Type E Botulism in Fish-Eating Birds on Lake Huron and Lake Erie 1998-2003 Chronic Wasting Disease in free-ranging cervids in western Canada Esophageal Lesions in Crows Surplus killing of roseate terns and common terns by a mink West Nile Virus infection in Québec Raptors : 2003 season Mortality in Great Black-Backed Gulls near Presqu\u27ile Type E Botulism Update West Nile Virus Update Adenovirus Encephalitis in a Wild Fox Newcastle Disease Virus in Double-crested Cormorants in Alberta and Saskatchewan in 2003 West Nile Virus in Sage Grouse The Research Group for Arctic Parasitology (RGAP), November 2003 Death of a Grizzly Bear caused by capture and handling Diagnosing Disease in Wild Animals. February 25-27, 2004, Saskatoon, S

Fifth Freedom, 1979-04-01

Buffalo Celebrates International Woman\u27s Day: pg1 Editorial: pg2 From Our Mailbag: pg2 Short Shots: pg3 News Notes: pg4 A Slip Of The Tongue: pg5 Dear Mary: pg5 From The Fountain\u27s: pg6 Youth Unite!: pg8 Mort D\u27amour: pg9 Crunch Nestles Quick: pg10 How Does Your Garden Grow: pg11 Sage Update: pg11 Zeitgeist: pg12 Doubtful Group Meets Again: pg12 SELections by Sam: pg13 Gay Rights National Lobby: pg14 Classified: pg14 Gay Directory: pg15https://digitalcommons.buffalostate.edu/fifthfreedom/1057/thumbnail.jp

Space-Alternating Generalized Expectation-Maximization Algorithm

The expectation-maximization (EM) method can facilitate maximizing likelihood functions that arise in statistical estimation problems. In the classical EM paradigm, one iteratively maximizes the conditional log-likelihood of a single unobservable complete data space, rather than maximizing the intractable likelihood function for the measured or incomplete data. EM algorithms update all parameters simultaneously, which has two drawbacks: 1) slow convergence, and 2) difficult maximization steps due to coupling when smoothness penalties are used. The paper describes the space-alternating generalized EM (SAGE) method, which updates the parameters sequentially by alternating between several small hidden-data spaces defined by the algorithm designer. The authors prove that the sequence of estimates monotonically increases the penalized-likelihood objective, derive asymptotic convergence rates, and provide sufficient conditions for monotone convergence in norm. Two signal processing applications illustrate the method: estimation of superimposed signals in Gaussian noise, and image reconstruction from Poisson measurements. In both applications, the SAGE algorithms easily accommodate smoothness penalties and converge faster than the EM algorithms.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85886/1/Fessler103.pd