We present our new, fully automated method to detect and measure the ages of star clusters in nearby galaxies, where individual stars can be resolved. The method relies purely on statistical analysis of observations and Monte-Carlo simulations to define stellar overdensities in the data. It decontaminates the cluster color-magnitude diagrams and, using a revised version of the Bayesian isochrone fitting code of Ramirez-Siordia et al., estimates the ages of the clusters. Comparisons of our estimates with those from other surveys show the superiority of our method to extract and measure the ages of star clusters, even in the most crowded fields. An application of our method is shown for the high-resolution, multiband imaging of the Large Magellanic Cloud. We detect 4850 clusters in the 7 deg(2) we surveyed, 3451 of which have not been reported before. Our findings suggest multiple epochs of star cluster formation, with the most probable occurring similar to 310 Myr ago. Several of these events are consistent with the epochs of the interactions among the Large and Small Magellanic Clouds, and the Galaxy, as predicted by N-body numerical simulations. Finally, the spatially resolved star cluster formation history may suggest an inside-out cluster formation scenario throughout the LMC, for the past 1 Gyr.

We present a measurement of the abundance of carbon monoxide in the early universe, utilizing the final results from the CO Power Spectrum Survey (COPSS). Between 2013 and 2015, we performed observations with the Sunyaev-Zel'dovich Array to measure aggregate CO emission from z similar to 3 galaxies with the intensity mapping technique. Data were collected on 19 fields, covering an area of 0.7 square degrees, over the frequency range 27-35 GHz. With these data, along with data analyzed in COPSS I, we are able to observe the CO(1-0) transition within the redshift range z = 2.3-3.3 for spatial frequencies between k = 0.5-10 h Mpc(-1), spanning a comoving volume of 4.9 x 10(6) h(-3) Mpc(3). We present estimates of contributions from continuum sources and ground illumination within our measurement. We constrain the amplitude of the CO power spectrum to P-CO = 3.0(-1.3)(+1.3) x 10(3) mu K-2(h(-1) Mpc)(3), or Delta(2)(CO)(k=1 h Mpc(-1)) = 1.5(-0.7)(+0.7) x 10(3) mu K-2, at 68% confidence, and PCO > 0 at 98.9% confidence. These results are a factor of 10 improvement in sensitivity compared to those of COPSS I. With this measurement, we constrain on the CO(1-0) galaxy luminosity function at z similar to 3. Assuming that CO emission is proportional to halo mass and using theoretical estimates of the scatter in this relationship, we constrain the ratio of CO(1-0) luminosity to halo mass to A(CO) = 6.3(-2.1)(+1.4) x 10 (7) L circle dot M circle dot-1. Assuming a Milky Way-like linear relationship between CO luminosity and molecular gas mass, we estimate a mass fraction of molecular gas of f(H2) = 5.5(-2.2)(+3.4) x 10(-2) for halos with masses of similar to 10(12)M(circle dot). Using theoretical estimates for the scaling of molecular gas mass fraction and halo mass, we estimate the cosmic molecular gas density to be rho(z similar to 3) (H-2) = 1.1(-0.4)(+0.7) x 10(8) M(circle dot)Mpc(-3).

The Event Horizon Telescope (EHT) is a millimeter-wavelength, very-long-baseline interferometry (VLBI) experiment that is capable of observing black holes with horizon-scale resolution. Early observations have revealed variable horizon-scale emission in the Galactic Center black hole, Sagittarius. A* (Sgr A*). Comparing such observations to time-dependent general relativistic magnetohydrodynamic (GRMHD) simulations requires statistical tools that explicitly consider the variability in both the data and the models. We develop here a Bayesian method to compare time-resolved simulation images to variable VLBI data, in order to infer model parameters and perform model comparisons. We use mock EHT data based on GRMHD simulations to explore the robustness of this Bayesian method and contrast it to approaches that do not consider the effects of variability. We find that time-independent models lead to offset values of the inferred parameters with artificially reduced uncertainties. Moreover, neglecting the variability in the data and the models often leads to erroneous model selections. We finally apply our method to the early EHT data on Sgr A*.

Recent studies regarding the habitability, observability, and possible orbital evolution of the indirectly detected exoplanet Proxima b have mostly assumed a planet with M similar to 1.3 M-circle plus, a rocky composition, and an Earth-like atmosphere or none at all. In order to assess these assumptions, we use previous studies of the radii, masses, and compositions of super-Earth exoplanets to probabilistically constrain the mass and radius of Proxima. b, assuming an isotropic inclination probability distribution. We find it is similar to 90% likely that the planet's density is consistent with a rocky composition; conversely, it is at least 10% likely that the planet has a significant amount of ice or an H/He envelope. If the planet does have a rocky composition, then we find expectation values and 95% confidence intervals of < M >(rocky) = 1.63(-0.72)(+1.66) M-circle plus for its mass and < R >(rocky) = 1.07(-0.31)(+0.38) R-circle plus for its radius.

The extensive catalogue of gamma-ray selected flat-spectrum radio quasars (FSRQs) produced by Fermi during a four-year survey has generated considerable interest in determining their gamma-ray luminosity function (GLF) and its evolution with cosmic time. In this paper, we introduce the novel idea of using this extensive database to test the differential volume expansion rate predicted by two specific models, the concordance Lambda cold darkmatter (Lambda CDM) and R-h = ct cosmologies. For this purpose, we use two well-studied formulations of the GLF, one based on pure luminosity evolution (PLE) and the other on a luminosity-dependent density evolution (LDDE). Using a Kolmogorov-Smirnov test on one-parameter cumulative distributions (in luminosity, redshift, photon index and source count), we confirm the results of earlier works showing that these data somewhat favour LDDE over PLE; we show that this is the case for both Lambda CDM and R-h = ct. Regardless of which GLF one chooses, however, we also show that model selection tools very strongly favour R-h = ct over Lambda CDM. We suggest that such population studies, though featuring a strong evolution in redshift, may none the less be used as a valuable independent check of other model comparisons based solely on geometric considerations.

We study the Sunyaev-Zel'dovich effect (SZE) signature in South Pole Telescope (SPT) data for an ensemble of 719 optically identified galaxy clusters selected from 124.6 deg(2) of the Dark Energy Survey (DES) science verification data, detecting a clear stacked SZE signal down to richness lambda similar to 20. The SZE signature is measured using matched-filtered maps of the 2500 deg(2) SPT-SZ survey at the positions of the DES clusters, and the degeneracy between SZE observable and matched-filter size is broken by adopting as priors SZE and optical mass-observable relations that are either calibrated using SPT-selected clusters or through the Arnaud et al. (A10) X-ray analysis. We measure the SPT signal-to-noise zeta - lambda relation and two integrated Compton-y Y500-lambda relations for the DES-selected clusters and compare these to model expectations that account for the SZE-optical centre offset distribution. For clusters with lambda > 80, the two SPT-calibrated scaling relations are consistent with the measurements, while for the A10-calibrated relation the measured SZE signal is smaller by a factor of 0.61 +/- 0.12 compared to the prediction. For clusters at 20 < lambda < 80, the measured SZE signal is smaller by a factor of similar to 0.20-0.80 (between 2.3 sigma and 10 sigma significance) compared to the prediction, with the SPT-calibrated scaling relations and larger lambda clusters showing generally better agreement. We quantify the required corrections to achieve consistency, showing that there is a richness-dependent bias that can be explained by some combination of (1) contamination of the observables and (2) biases in the estimated halo masses. We also discuss particular physical effects associated with these biases, such as contamination of. from line-of-sight projections or of the SZE observables from point sources, larger offsets in the SZE-optical centring or larger intrinsic scatter in the lambda-mass relation at lower richnesses.

We present a new matched-filter algorithm for direct detection of point sources in the immediate vicinity of bright stars. The stellar point-spread function (PSF) is first subtracted using a Karhunen-Love image processing (KLIP) algorithm with angular and spectral differential imaging (ADI and SDI). The KLIP-induced distortion of the astrophysical signal is included in the matched-filter template by computing a forward model of the PSF at every position in the image. To optimize the performance of the algorithm, we conduct extensive planet injection and recovery tests and tune the exoplanet spectra template and KLIP reduction aggressiveness to maximize the signalto- noise ratio (S/N) of the recovered planets. We show that only two spectral templates are necessary to recover any young Jovian exoplanets with minimal S/N loss. We also developed a complete pipeline for the automated detection of point-source candidates, the calculation of receiver operating characteristics (ROC), contrast curves based on. false positives, and completeness contours. We process in a uniform manner more than 330 data sets from the Gemini Planet Imager Exoplanet Survey and assess GPI typical sensitivity as a function of the star and the hypothetical companion spectral type. This work allows for the first time a comparison of different detection algorithms at a survey scale accounting for both planet completeness and false-positive rate. We show that the new forward model matched filter allows the detection of 50% fainter objects than a conventional cross-correlation technique with a Gaussian PSF template for the same false-positive rate.

Scotch Whisky is a complex mixture comprising thousands of chemical species at a diverse range of concentrations. The identities, origins, and significance of many of these compounds is largely unknown. Routine characterisation of Scotch Whisky mostly utilises techniques such as gas or liquid chromatography (GC, LC) coupled to a FID, UV, or low-resolution mass spectrometry (MS) detector. In this thesis, advanced spectroscopic and spectrometric techniques are investigated as potential complementary means to unravel the chemical complexity of Scotch Whisky. Chemometric methods are applied to decipher the significance or potential origin of many of these compounds. Being predominantly (ca. 99 %) protonated 'solvent' - ethanol and water - 1H and 13C Nuclear Magnetic Resonance (NMR) required solvent suppression to be implemented into the acquisition of high resolution spectra. A 1D NOESY-presaturation sequence was modified and implemented in automation for this purpose. Furthermore, this solvent suppression was coupled with several 1D and 2D homo- and heterocorrelated NMR experiments for the analysis of Scotch Whisky. With limited sample preparation - only addition of buffer - an approximate limit of detection of 50 μm was achieved. The developed NMR methodology was subsequently used for structural elucidation of dozens of compounds in Scotch Whisky. Quantification of these compounds was hindered by variable chemical shifts, signal overlap, and for some compounds the existence of equilibria of different forms. Quantification of ethanol concentrations in model solutions and genuine Scotch Whisky samples was successful. A large set of Scotch Whisky samples were analysed by the solvent suppressed 1D 1H NMR methodology and various statistical techniques including statistical total correlation spectroscopy (STOCSY), independent and principal component analysis (ICA, PCA), and orthogonal partial least squares discriminant analysis (OPLS-DA). Various parameters were modelled, and discrimination of 'malt' or 'blend' status was achieved, whilst maturation wood type discrimination was less successful. High resolution negative mode electrospray ionisation (ESI) Fourier transform ion cyclotron resonance (FTICR) MS was then used to examine a large set of Scotch Whisky samples. Thousands of unique molecular formulae were assigned within a 1 ppm threshold, representing an assignment rate of 72-88 % of the detected peaks in the spectra. Assignments were selectively confirmed by isotopic fine structure (IFS) analysis, and structural information obtained by both quadrupolar isolation and fragmentation, and in-cell isolation and fragmentation. Similar chemometric methods as applied to NMR data were used to model sample parameters, and identification of potential maturation wood marker compounds was achieved. Alternative ionisation sources - including atmospheric pressure chemical- and photo-ionisation (APCI, APPI), and laser desorption ionisation (LDI) - were compared for the analysis of Scotch Whisky by FTICR MS. The differing sources provide complementary compositional information on the sample set, with APCI and LDI being most different to ESI in terms of compounds ionised and spectral profiles. Positive mode ionisation was also successful, but molecular formula assignment was hindered by insufficient resolving power. Late experimentation pushed the achievable resolving power to 2.8 million at m/z 400, however the required approach is significantly more time consuming and prone to signal quality degradation. Formula assignment software, both commercial, open source, and in-house developed, were compared. The commercial and published open source software provided essentially identical results for Scotch Whisky, and whilst the in-house tools assigned fewer species (a subset of those assigned by the other tools), they did so with a smaller mean error of assignment. Various analysis and visualisation tools for MS data of complex mixtures were also developed.

Yes / A workshop was recently held at Nagoya University (31 October – 02 November 2017), sponsored by the Center for International Collaborative Research, at the Institute for Space-Earth Environmental Research, Nagoya University, Japan, to quantitatively compare the performance of today’s operational solar flare forecasting facilities. Building upon Paper I of this series (Barnes et al. 2016), in Paper II (Leka et al. 2019) we described the participating methods for this latest comparison effort, the evaluation methodology, and presented quantitative comparisons. In this paper we focus on the behavior and performance of the methods when evaluated in the context of broad implementation differences. Acknowledging the short testing interval available and the small number of methods available, we do find that forecast performance: 1) appears to improve by including persistence or prior flare activity, region evolution, and a human “forecaster in the loop”; 2) is hurt by restricting data to disk-center observations; 3) may benefit from long-term statistics, but mostly when then combined with modern data sources and statistical approaches. These trends are arguably weak and must be viewed with numerous caveats, as discussed both here and in Paper II. Following this present work, we present in Paper IV a novel analysis method to evaluate temporal patterns of forecasting errors of both types (i.e., misses and false alarms; Park et al. 2019). Hence, most importantly, with this series of papers we demonstrate the techniques for facilitating comparisons in the interest of establishing performance-positive methodologies. / We wish to acknowledge funding from the Institute for Space-Earth Environmental Research, Nagoya University for supporting the workshop and its participants. We would also like to acknowledge the “big picture” perspective brought by Dr. M. Leila Mays during her participation in the workshop. K.D.L. and G.B. acknowledge that the DAFFS and DAFFS-G tools were developed under NOAA SBIR contracts WC-133R-13-CN-0079 (Phase-I) and WC-133R-14-CN-0103 (PhaseII) with additional support from Lockheed-Martin Space Systems contract #4103056734 for Solar-B FPP Phase E support. A.E.McC. was supported by an Irish Research Council Government of Ireland Postgraduate Scholarship. D.S.B. and M.K.G were supported by the European Union Horizon 2020 research and innovation programme under grant agreement No. 640216 (FLARECAST project; http://flarecast.eu). MKG also acknowledges research performed under the A-EFFort project and subsequent service implementation, supported under ESA Contract number 4000111994/14/D/ MPR. S. A. M. is supported by the Irish Research Council Postdoctoral Fellowship Programme and the US Air Force Office of Scientific Research award FA9550-17-1-039. The operational Space Weather services of ROB/SIDC are partially funded through the STCE, a collaborative framework funded by the Belgian Science Policy Office.

Diss. (sammanfattning) Umeå : Sveriges lantbruksuniv., 2006. / Härtill 5 uppsatser.