Assigning committee seats in mixed-member systems: how important is "localness" compared to the mode of election?

"Committees are important features in legislative decision making. The question of who serves on what committee is thus an important one. This paper asks about how mixed electoral systems affect the way committee seats are allocated. Stratmann and Baur (2002) argue that German parties strategically assign nominally elected legislators to those committees that allow them to please their local constituents. Our paper questions this argument in light of the functioning of the German mixed-member system and the individual motivations of German MPs. We argue that the motivations of German legislators do not necessarily mirror their mode of election, and that German parties do not necessarily perceive winning nominal votes as a predominant goal. We hypothesize that German parties aim to increase their vote share on the list-vote (Zweitstimme) by supporting legislators with a strong local focus independent of their mode of election. We will test this argument empirically drawing from the German Candidate Study 2005 and from statistical data on committee membership for the 16th German Bundestag (2005-2009)." (author's abstract

Rapid pretreatment of Miscanthus using the low-cost ionic liquid triethylammonium hydrogen sulfate at elevated temperatures

Deconstruction with low-cost ionic liquids (ionoSolv) is a promising method to pre-condition lignocellulosic biomass for the production of renewable fuels, materials and chemicals. This study investigated process intensification strategies for the ionoSolv pretreatment of Miscanthus X giganteus using the low-cost ionic liquid triethylammonium hydrogen sulfate ([TEA][HSO4]) in the presence of 20 wt% water, using high temperatures and a high solid to solvent loading of 1:5 g/g. The temperatures investigated were 150, 160, 170 and 180°C. We discuss the effect of pretreatment temperature on lignin and hemicellulose removal, cellulose degradation and enzymatic saccharification yields. We report that very good fractionation can be achieved across all investigated temperatures, including an enzymatic saccharification yield exceeding 75% of the theoretical maximum after only 15 min of treatment at 180°C. We further characterised the recovered lignins which established some tunability of the hydroxyl group content, subunit composition, connectivity and molecular weight distribution in the isolated lignin while maintaining maximum saccharification yield. This drastic reduction of pretreatment time at increased biomass loading without a yield penalty is promising for the development of a commercial ionoSolv pretreatment process

Stimmensplitting und Koalitionswahl

Hat sich die Unabhängigkeitsstrategie der FDP bei der letzten Bundestagswahl ausgezahlt? Wäre die FDP erfolgreicher gewesen, wenn sie im Vorfeld klar signalisiert hätte, dass man eine Koalition mit der Union anstrebt? Wie war das bei den Grünen, die ja im Gegensatz zur FDP keine Zweifel aufkommen ließen? Natürlich können wir nicht wie in einer Simulation oder einem Experiment einfach den Wahlkampf wiederholen und noch einmal wählen lassen. Um eine befriedigende Antwort auf diese Frage zu finden, vergleichen wir den Kontext der Bundestagswahl 2002 mit den zurückliegenden Bundestagswahlen. Aus dem Längsschnittvergleich versuchen wir Rückschlüsse auf den substanziellen Einfluss von strategischem Stimmensplitting im Sinne einer Koalitionswahl auf das Wahlergebnis gerade der kleinen Parteien zu ziehen. Um unsere Forschungsfrage zu beantworten und substanzielle Schlüsse ziehen zu können, muss zuerst klar sein, in welcher Form und warum Stimmensplitting relevant sein kann, welche Rolle dabei Koalitionsabsprachen vor einer jeden Wahl spielen und, schließlich, welche alternativen Erklärungsmöglichkeiten die Literatur zum Thema Stimmensplitting und strategischem Wählen anzubieten hat. Nur wenn wir auch die Wirkung alternativer und zum Teil konkurrierender Hypothesen zulassen, können wir unserer Schlußfolgerungen sicher sein

Astrometric calibration and performance of the Dark Energy Camera

We characterize the ability of the Dark Energy Camera (DECam) to perform relative astrometry across its 500~Mpix, 3 deg^2 science field of view, and across 4 years of operation. This is done using internal comparisons of ~4x10^7 measurements of high-S/N stellar images obtained in repeat visits to fields of moderate stellar density, with the telescope dithered to move the sources around the array. An empirical astrometric model includes terms for: optical distortions; stray electric fields in the CCD detectors; chromatic terms in the instrumental and atmospheric optics; shifts in CCD relative positions of up to ~10 um when the DECam temperature cycles; and low-order distortions to each exposure from changes in atmospheric refraction and telescope alignment. Errors in this astrometric model are dominated by stochastic variations with typical amplitudes of 10-30 mas (in a 30 s exposure) and 5-10 arcmin coherence length, plausibly attributed to Kolmogorov-spectrum atmospheric turbulence. The size of these atmospheric distortions is not closely related to the seeing. Given an astrometric reference catalog at density ~0.7 arcmin^{-2}, e.g. from Gaia, the typical atmospheric distortions can be interpolated to 7 mas RMS accuracy (for 30 s exposures) with 1 arcmin coherence length for residual errors. Remaining detectable error contributors are 2-4 mas RMS from unmodelled stray electric fields in the devices, and another 2-4 mas RMS from focal plane shifts between camera thermal cycles. Thus the astrometric solution for a single DECam exposure is accurate to 3-6 mas (0.02 pixels, or 300 nm) on the focal plane, plus the stochastic atmospheric distortion.Comment: Submitted to PAS

Quasar accretion disk sizes from continuum reverberation mapping in the DES standard-star fields

Measurements of the physical properties of accretion disks in active galactic nuclei are important for better understanding the growth and evolution of supermassive black holes. We present the accretion disk sizes of 22 quasars from continuum reverberation mapping with data from the Dark Energy Survey (DES) standard star fields and the supernova C fields. We construct continuum lightcurves with the \textit{griz} photometry that span five seasons of DES observations. These data sample the time variability of the quasars with a cadence as short as one day, which corresponds to a rest frame cadence that is a factor of a few higher than most previous work. We derive time lags between bands with both JAVELIN and the interpolated cross-correlation function method, and fit for accretion disk sizes using the JAVELIN Thin Disk model. These new measurements include disks around black holes with masses as small as $\sim10^7$ $M_{\odot}$, which have equivalent sizes at 2500\AA \, as small as $\sim 0.1$ light days in the rest frame. We find that most objects have accretion disk sizes consistent with the prediction of the standard thin disk model when we take disk variability into account. We have also simulated the expected yield of accretion disk measurements under various observational scenarios for the Large Synoptic Survey Telescope Deep Drilling Fields. We find that the number of disk measurements would increase significantly if the default cadence is changed from three days to two days or one day.Comment: 33 pages, 24 figure

H0LiCOW X: Spectroscopic/imaging survey and galaxy-group identification around the strong gravitational lens system WFI2033-4723

Galaxies and galaxy groups located along the line of sight towards gravitationally lensed quasars produce high-order perturbations of the gravitational potential at the lens position. When these perturbation are too large, they can induce a systematic error on $H_0$ of a few-percent if the lens system is used for cosmological inference and the perturbers are not explicitly accounted for in the lens model. In this work, we present a detailed characterization of the environment of the lens system WFI2033-4723 ($z_{\rm src} = 1.662$, $z_{\rm lens}$ = 0.6575), one of the core targets of the H0LICOW project for which we present cosmological inferences in a companion paper (Rusu et al. 2019). We use the Gemini and ESO-Very Large telescopes to measure the spectroscopic redshifts of the brightest galaxies towards the lens, and use the ESO-MUSE integral field spectrograph to measure the velocity-dispersion of the lens ($\sigma_{\rm {los}}= 250^{+15}_{-21}$ km/s) and of several nearby galaxies. In addition, we measure photometric redshifts and stellar masses of all galaxies down to $i < 23$ mag, mainly based on Dark Energy Survey imaging (DR1). Our new catalog, complemented with literature data, more than doubles the number of known galaxy spectroscopic redshifts in the direct vicinity of the lens, expanding to 116 (64) the number of spectroscopic redshifts for galaxies separated by less than 3 arcmin (2 arcmin) from the lens. Using the flexion-shift as a measure of the amplitude of the gravitational perturbation, we identify 2 galaxy groups and 3 galaxies that require specific attention in the lens models. The ESO MUSE data enable us to measure the velocity-dispersions of three of these galaxies. These results are essential for the cosmological inference analysis presented in Rusu et al. (2019).Comment: Matches the version accepted for publication by MNRAS. Note that this paper previously appeared as H0LICOW X

Phenotypic redshifts with self-organizing maps: A novel method to characterize redshift distributions of source galaxies for weak lensing

Wide-field imaging surveys such as the Dark Energy Survey (DES) rely on coarse measurements of spectral energy distributions in a few filters to estimate the redshift distribution of source galaxies. In this regime, sample variance, shot noise, and selection effects limit the attainable accuracy of redshift calibration and thus of cosmological constraints. We present a new method to combine wide-field, few-filter measurements with catalogs from deep fields with additional filters and sufficiently low photometric noise to break degeneracies in photometric redshifts. The multi-band deep field is used as an intermediary between wide-field observations and accurate redshifts, greatly reducing sample variance, shot noise, and selection effects. Our implementation of the method uses self-organizing maps to group galaxies into phenotypes based on their observed fluxes, and is tested using a mock DES catalog created from N-body simulations. It yields a typical uncertainty on the mean redshift in each of five tomographic bins for an idealized simulation of the DES Year 3 weak-lensing tomographic analysis of $\sigma_{\Delta z} = 0.007$, which is a 60% improvement compared to the Year 1 analysis. Although the implementation of the method is tailored to DES, its formalism can be applied to other large photometric surveys with a similar observing strategy.Comment: 24 pages, 11 figures; matches version accepted to MNRA

Transfer learning for galaxy morphology from one survey to another

© 2018 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society.Deep Learning (DL) algorithms for morphological classification of galaxies have proven very successful, mimicking (or even improving) visual classifications. However, these algorithms rely on large training samples of labelled galaxies (typically thousands of them). A key question for using DL classifications in future Big Data surveys is how much of the knowledge acquired from an existing survey can be exported to a new dataset, i.e. if the features learned by the machines are meaningful for different data. We test the performance of DL models, trained with Sloan Digital Sky Survey (SDSS) data, on Dark Energy survey (DES) using images for a sample of $\sim$5000 galaxies with a similar redshift distribution to SDSS. Applying the models directly to DES data provides a reasonable global accuracy ($\sim$ 90%), but small completeness and purity values. A fast domain adaptation step, consisting in a further training with a small DES sample of galaxies ($\sim$500-300), is enough for obtaining an accuracy > 95% and a significant improvement in the completeness and purity values. This demonstrates that, once trained with a particular dataset, machines can quickly adapt to new instrument characteristics (e.g., PSF, seeing, depth), reducing by almost one order of magnitude the necessary training sample for morphological classification. Redshift evolution effects or significant depth differences are not taken into account in this study.Peer reviewedFinal Accepted Versio