dataesr/scientific-paragraphs-categorization huggingface dataset:
We emphasize that this analysis focuses on the kine- matics of the ionized gas, which is traced by the bright emission lines. Stellar kinematics, which is much more difficult to derive in intermediate/high-redshift galaxies, will be the focus of further analyses. This paper is structured as follows. In section 2 we present the MUSE-HDFS dataset, the selection criteria used to define a sample of 28 spatially resolved galaxies, and how this sample compares to previous IFS surveys of intermediate/high-redshift galaxies in terms of stellar masses and star formation rates. The morphological (based on HST images) and ionized gas kinematic (based on MUSE data) analysis of these 28 galaxies is described in sections 3 and 4, respectively. Results concerning the dynamical state of galaxies, their location on the TFR and their angular momentum are presented in Sect.
The MUSE-HDFS dataset The Hubble Deep Field South (HDFS) was observed with MUSE, mounted at the ESO-VLT UT4, during six nights of the last commissioning run of the instrument between July 25 and August 3, 2014. A single 1 × 1 MUSE field centered on α = 22h32 55. 64 , δ = -60 • 33 47 in the HDFS was observed, with a total combined on-target exposure time of 27 hours, reaching a 1σ emission-line surface brightness limit of 1 × 10 -19 erg s -1 cm -2 arcsec -2. The observing strategy and data reduction procedure are fully described in  Bacon et al. (2015). In this paper, we simply summarize the main steps of the data acquisition/processing. The final MUSE-HDFS data cube is the result of a combination of 54 individual exposures of 30 minutes exposure time each. The instrument position angle (PA) was rotated by 90 • between two subsequent individual exposures together with random dithering offsets within a box of 3.
Misalignment between the kinematic and morphological major axes For rotating galaxies, we expect a rough agreement between the kinematic position angle derived from MUSE data (Sect. 4. 2) and the morphological position angle determined from the HST/F814W optical images (Sect. 3). The difference between these two parameters, ∆PA defined as an absolute value between 0 • and 90 • , is shown in Fig. 9  as a function of the galaxy inclination. The mean and median values of ∆PA for all the galaxies are 25 • and 13 • , respectively, within the expected errors of both measurements. For half of the galaxies the agreement is better than 15 • and for ≈ 70% of the sample galaxies the agreement is better than 30 •. Among the nine galaxies showing a higher difference (∆PA > 30 • ), six are at relatively low inclination (i < 40 • ) and four are dispersion dominated (see below).
g. ,  Miller et al. 2011; Vergani et al. 2012 ). Our sample is definitely too small to derive robust conclusions over such a large redshift interval. No doubt that the growing sample built from the various (deep) MUSE datasets will allow us to address this issue in the coming years, especially in the low-mass regime. Figure  10  also compares the TFR defined for the MUSE-HDFS sample to data and models from the literature. Data come from previous IFS surveys in similar and complementary redshift ranges: IMAGES at z ∼ 0. 4 -0. 8  (Puech et al. 2010 ) and MASSIV at z ∼ 0. 9-1. 4 (Epinat et al. 2012; Vergani et al. 2012 ). If needed, stellar masses were corrected to take into account any different IMF used in the SED modeling. As previously stated, the rotation-dominated galaxies selected in the MUSE-HDFS field follow the TFR defined with previous IFS surveys in the high-mass regime.
It is clear, however, that deep observations with MUSE make it possible to extend the TFR studies to much lower stellar masses, a regime that was almost inaccessible with the previous instrumentation. A comparison with recent predictions from the EAGLE cosmological hydrodynamical simulations  (Schaye et al. 2015; Crain et al. 2015)  is also shown in Figure  10. The EAGLE predictions 7  for the TFR are based on z ≈ 0. 1 simulated galaxies selected to be late type (Sersic index < 2. 5) and use maximum circular velocities. The agreement between the MUSE-HDFS dataset and the EAGLE predictions is quite impressive, even if the scatter around the TFR seen in the data is higher than that predicted by the simulations (delineated with dashed lines in Fig. 10 ). Very recently, based on the analysis of DEEP2 emission-line galaxies at 0.
5 M ), whereas the KMOS 3D sample covers a range of higher stellar masses with only a small fraction (≈ 20%) of galaxies in the sample below 10 10 M , i. e. , where our sample ends. In subsequent papers, with larger samples drawn from MUSE-GTO datasets, we will be able to refine this sort of analysis for a larger number of low-mass galaxies, following  Burkert et al. (2015). Our results in Figure  11 , like those in  Burkert et al. (2015) , imply that the disk specific angular momentum is similar to that of the DM halos, i. e. , j d ∼ j DM , in agreement with the most recent state-of-the-art hydrodynamical simulations  (Genel et al. 2015; Zavala et al. 2015; Teklu et al. 2015). This agreement seems natural in the context of spherical collapse, where accretion is isotropic and the baryon angular momentum is closely related to that of the DM halo.
In the j d -M plane (Fig. 12 ), we see that intermediateredshift, star-forming galaxies fill a continuum transition from the local spiral (rotation-dominated galaxies) to elliptical Fig. 12. Specific angular momentum of disks j d as a function of galaxy stellar mass for the MUSE-HDFS sample of spatially resolved galaxies. Data points are color-coded according to the V/σ ratio. Diamond outlines indicate dispersion-dominated galaxies with V/σ < 1. The dashed lines show the relations defined for massive galaxies at z = 0  (Fall & Romanowsky 2013) , distinguishing between spheroids (red line, M > 10 10 M ) and disks (blue line, M > 10 9 M ). (dispersion-dominated galaxies) scaling relations, thus shifting from rotation-to dispersion-dominated (parametrized with V/σ) gas kinematics. This transition from star-forming, rotationdominated galaxies to dispersion-dominated is in qualitative agreement with  Genel et al.
The detailed analysis of their morphology (from HST images), gas kinematics (from the MUSE data cube), and close environments (from both) shed light on their structural properties and dynamical state. Most of the MUSE-HDFS galaxies have gas kinematics consistent with rotating disks. However, about 20% of these galaxies are dynamically dominated by random motions, as revealed by low (gaseous) V/σ ratios. This fraction of dispersion-dominated galaxies is similar to the one found at z ∼ 1 -2 in the first-year KMOS 3D sample  (Wisnioski et al. 2015). About 30% of the sample galaxies are also part of a close pair and/or show clear signs of recent gravitational interactions, similar to what was found in previous IFS surveys of more massive galaxies (e. g. , Förster  Schreiber et al. 2009; Epinat et al.
2014 ) but with a higher scatter compared to the more massive objects. Contrary to  Simons et al. (2015) , we do not find a large population of dispersion-dominated, slow rotating galaxies in the low-mass regime; 90% of the MUSE-HDFS galaxies with stellar masses below 10 9. 5 M are rotation dominated and thus have already settled into a disk. Finally, we take advantage of this unique dataset to characterize the angular momentum and spin parameter of intermediate-z low-mass galaxies and explore the link with their dark matter haloes. We find that the spin distribution for the haloes of the MUSE-HDFS galaxies is broadly consistent with predictions for the dark matter in the stellar mass range 10 8 to 10 10 M. The MUSE-HDFS galaxies follow the scaling relations defined in the local universe  (Romanowsky & Fall 2012; Fall & Romanowsky 2013)  between the specific angular momentum and stellar mass.
However, we found that intermediate-redshift, star-forming galaxies fill a continuum transition from the spiral to elliptical local scaling relations, according to their dynamical (rotation-or dispersion-dominated) state. This suggests that some galaxies may lose their angular momentum and become dispersion dominated prior to becoming passive. These new observational results appear in line with recent theoretical arguments (e. g. ,  Dekel & Burkert 2014; Genel et al. 2015; Bournaud 2016; Tacchella et al. 2016). Even though the MUSE-HDFS sample is already impressive in terms of data quality and uniqueness, it is still a small sample. More robust conclusions will be drawn from subsequent analyses based on the much larger datasets that will be acquired over the course of the MUSE-GTO. Table  1.
Acknowledgements. We thank the referee for providing useful and constructive comments on the submitted version of this paper. We warmly thank  Joel Vernet ,  Fernando Selman , and all Paranal staff for their enthusiastic support of  MUSE  during the commissioning runs. We thank  M. Fall ,  S. Genel , and  R. Genzel  for useful comments on this paper. This work has been carried out thanks to the support of the  ANR FOGHAR  ( ANR-13-BS05-0010-02 ), the  OCEVU Labex  ( ANR-11-LABX-0060 ) and the A* MIDEX  project ( ANR-11-IDEX-0001-02 ) funded by the " Investissements d'avenir" French government program  managed by the  ANR. RB acknowledges support from the  ERC  advanced grant 339659-MUSICOS. JR acknowledges support from the  ERC  starting grant CALENDS. JS acknowledges support from the  ERC  grant agreement 278594-GasAroundGalaxies.
LMD acknowledges support from the  Lyon Institute of Origins  under grant  ANR-10-LABX-0066. TPKM acknowledges support from the  Spanish Ministry of Economy and Competitiveness (MINECO)  under grant number  AYA2013-41243-P. BE  acknowledges financial support from " Programme National de Cosmologie and Galaxie" (PNCG)  of  CNRS/INSU, France.
Covering the extended outer LMC regions requires a large-area, photometric and homogeneous sample. This has recently been provided by the early data taken as part of the Dark Energy Survey (DES) Science Verification (SV). In this work we aim to probe clusters in the outer LMC field in DES-SV footprint, using a homogeneous data sample. This paper is organized as follows: Section 2 is a brief introduction presenting the data (the DES-SV data set, its reduction and the resulting LMC clusters sample). Section 3 describes the methods we applied to recover cluster structural parameters and cluster stellar populations. In Section 4 we tested the limits of the methods, simulating clusters, recovery of parameters and determining uncertainties. Section 5 presents our results and compares to available literature.
DES and SV The DES (The Dark Energy Survey Collaboration 2005) is a 5000 deg 2 imaging survey in grizY bands currently being carried out using DECam, a 3 deg 2 (2. • 2 diameter) wide-field mosaic camera on the CTIO Blanco 4 m telescope with a pixel scale of 0. 263 arcsec pixel -1  (Flaugher et al. 2015). DES will reach a characteristic photometric depth of 24th magnitude (with S/N 10 for g band, point-like sources) and enable accurate photometry and morphology of objects 10 times fainter than the Sloan Digital Sky Survey. The exposure times for DES single exposures are 90 s for g, r, i, z and 45 s for Y band. The DECam images are reduced by the DES Data Management (DESDM) team, which has a pipeline to co-add and calibrate (astrometrically and photometrically) images and finally catalogue and classify the objects in the images.
The DES-SV data were taken from November 2012 to February 2013 for a total of 427 h of observation. The data were intended to test DECam capabilities and the DESDM data pipeline (calibration and photometry, astrometry, image quality, pointing and guiding, operational readiness and pipelines for supernova). The largest contiguous area of the SV campaign was the South Pole Telescope East field (SPT-E), which is located immediately to the north of the LMC. This work is based on the co-add data products from the first release processing of the SV observations (SVA1), which includes the SPT-E region (157 deg 2 ) in 2537 exposures.
DES-SV stellar sample Since the DES main goals are cosmological in nature, the science requirements for the data are related to detecting, measuring and characterizing galaxies, not stars. Therefore, we ran a complementary diagnostic of the DES-SV stellar catalogue. Our main concern was to investigate the completeness of the SVA1 stellar catalogue as a function of S/N and crowding, since this aspect is crucial to the analysis of LMC clusters presented here. For that purpose, we used routines from IRAF/DAOPHOT (Stetson 1987), which is the benchmark program to detect and measure stellar data in crowded fields. Details about our DAOPHOT reduction and photometry pipeline are provided in Section 2. 4. A full description of stellar completeness in SVA1 catalogues can be found in a future technical paper  (Pieres et al.
With these results in mind, we decided that we could not use the SVA1 catalogues inside and around the LMC clusters. However, we used SVA1 co-add images to identify cluster candidates and its catalogs to fit zero-points to DAOPHOT photometry. We also decided to use three bands (two colours) instead of two, to better describe the stellar locus of each cluster candidate. For reference, a comparison of both software packages (SEXTRACTOR with PSFEX and DAOPHOT with ALLSTAR) can be found in  Annunziatella et al. (2013). In Section 2. 4 we describe in detail the data reduction and analysis based on our DAOPHOT pipeline.
DAOPHOT data reduction As mentioned earlier, the SVA1 catalogue is incomplete in crowded regions, such as inside LMC star clusters. To bypass this problem, we used the SVA1 co-add image products in the vicinity of each of our candidate clusters as inputs for our own photometric extraction based on DAOPHOT. For each object we made an image cutout with 6. 75 arcmin on a side, visually centred on the candidate, in three bands: g, r and i. The IRAF/DAOPHOT routines were combined into a pipeline to reduce DES co-add cutouts (Fig. 1 ). This pipeline is a set of scripts using the tasks daofind, phot, pstselect, psf, allstar and works as follows. First, we combined g and r images and ran daofind to detect sources on these combined images; we rejected sources that were less than 3. 0σ above the background.
We then picked the 50 brightest sources to use as a starting template for PSF modelling (running psf task). With the initial PSF model, we ran allstar over all sources found and refined our initial list of stars used for PSF fitting by choosing those whose local background is ≤3 counts, and which have low magnitude error (≤0. 01 mag) and low chi-square (≤0. 2). This procedure of refining the PSF after an initial allstar run is meant to make the process subject to minimal human interference (although some human decisions were still needed at times). PYTHON  and FORTRAN routines were made to select the stars and convert their α and δ coordinates to/from x and y coordinates. This conversion, whenever needed, uses the WCS of the DESDM co-adds, therefore preserving DESDM astrometry. The SVA1 catalogue has a typical accuracy of 200 mas per coordinate when compared to UCAC-4, and 100 mas for nearby sources.
Using a program similar to the join Linux command, we then composed the DAOPHOT catalogues from each filter. We then added g, r and i zero-points, by taking DES catalogue photometry as reference, and created the final DAOPHOT catalogue. The stars used for this calibration were those that satisfied the following criteria: magerr DAO ≤ 0. 03, |sharp| ≤ 1. 00 in g, r and i filters, brighter than 20 (in g and r filters) and 19 (for i filter). The corresponding sources in SVA1 had to have | spread model |≤0. 002 and magnitude error ≤0. 03. The standard deviation for these difference (|mag DAOmag SVA1 |) has typical values ≤0. 02 for the three filters. This small random scatter after the zero-point correction precludes the need of adopting colour terms in the calibration. After the reduction, diagnostic plots were made to evaluate the DAOPHOT catalogue completeness level and to make reduction sanity checks.
We used the same extinction law as cited above for DES bands and a typical magnitude error curve for each filter. We simulated star clusters at a fixed distance modulus of (m -M) 0 = 18. 5 and no reddening. The simulated clusters were all cut at g ≤24 and σ g, r, i ≤0. 1. These photometric uncertainties were assigned to the synthetic stars following empirical curves as a function of gri magnitudes taken from  Balbinot et al. (2015). For simplicity, we did not simulate unresolved binaries. We also used a fixed con-1 https:/github. com/balbinot/genCMD centration parameter typical of LMC clusters for all the simulations, log 10 (r t /r c ) = 0. 6  (Werchan & Zaritsky 2011). We produced sets of clusters with different richness levels, varying the number of stars but keeping tidal radius constant (for a same set of simulations).
Comparison to literature Before proceeding with an LMC cluster system analysis, we first validated the methods outlined in Section 3 with DES-SV data by comparing our LMC clusters parameters to those found in the literature. Our comparison sample is made up of six relatively rich clusters for which data of comparable or superior quality are available. For NGC 1868 and NGC 2162, there are parameter estimates from more than one source in the literature, while for Hodge 4, ESO 121-03, NGC 2193 and Reticulum cluster only one reference was found. The results are summarized in Table  1  and Fig. 8. The results listed in Table  1  come from a variety of different methods and data. Most of them are based on CMD analysis, like our own. In this sense, the fits found in previous works are not necessarily more accurate than ours.
In particular, none of those papers are based on the same set of PARSEC isochrones used in this work and some of them are not based on optical data, as is the case of  Grocholski et al. (2007). These issues, coupled with variations in methodology, are probably the cause of the spread in the parameters from different authors, or even among the same authors, as attested by the compilation of NGC 1868 and NGC 2162 results. Even with this spread, there is a clear correlation between our fits and literature, as one can see in Fig. 8  for all the parameters. Fig. 8  testifies that there is no strong systematic trends in our determinations. Therefore, we conclude that the soundness of our methodology is further corroborated based on these real data comparisons. For the structural parameters, we had no overlap with the large sample of clusters measured by  Werchan & Zaritsky (2011).
We also show completeness curves in the centres of moderately and severely crowded clusters, namely SL 262 and NGC 1868. For that purpose, we picked a square image (54 × 54 arcsec) centred in each cluster. For clarity, curves are only presented for g band, but are similar at other bands. The completeness levels shown correlate well with the strong discrepancies we find with respect to  Mackey & Gilmore (2003)  and suggests further that incompleteness is the dominant source of these discrepancies. Note that rich clusters which have crowded centres make up about 15 per cent of our sample. The completeness was estimated in our data by inserting 2. 4 × 10 4 artificial stars in 10 realizations of the image in each band (g, r and i) for the two clusters cited above. We then proceeded to reduce the artificial stars in the same manner as the DES-SV data.
(iii) To reduce stellar incompleteness in crowded fields, we developed a pipeline to reduce data using DAOPHOT. The pipeline combines g and r images and runs PSF selection and photometry in a largely automatic way. Using stars with good photometry in both catalogues, we compared the final DAOPHOT magnitudes to DES-SVA1, determining a zero-point. The agreement is very good and on average DES-SVA1 and DAOPHOT photometry agree within  (iv) We applied a maximum likelihood estimation approach to fit the stellar density profile to a King model  (King 1962) , following a recipe similar to that of  Martin et al. (2008). This method is robust to determine centre position, core and tidal radii. As output, for each cluster candidate we listed the stars within the tidal radius, along with their probability to belong to cluster candidate (P kp ).
AC K N OW L E D G E M E N T S We would like to thank the anonymous referee for many useful suggestions. This paper has gone through internal review by the DES collaboration. AdP acknowledges financial support from the Brazilian Institution CNPq. EdB acknowledges financial support from the European Research Council  CLUSTERS). We are grateful for the extraordinary contributions of our CTIO colleagues and the DECam Construction, Commissioning and Science Verification teams in achieving the excellent instrument and telescope conditions that have made this work possible. The success of this project also relies critically on the expertise and dedication of the DES Data Management group. The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Enérgeticas, We append the list of clusters for which we fit profiles and isochrones.
Assessing global tendencies and impacts of conditional payments for environmental services (PES) programs is challenging because of their heterogeneity, and scarcity of comparative studies. This meta-study systematizes 55 PES schemes worldwide in a quantitative database. Using categorical principal component analysis to highlight clustering patterns, we reconfirm frequently hypothesized differences between public and private PES schemes, but also identify diverging patterns between commercial and non-commercial private PES vis-à-vis their service focus, area size, and market orientation. When do these PES schemes likely achieve significant environmental additionality? Using binary logistical regression, we find additionality to be positively influenced by three theoretically recommended PES 'best design' features: spatial targeting, payment differentiation, and strong conditionality, alongside some contextual controls (activity paid for and implementation time elapsed).
Introduction Payments for environmental services (PES) have become an increasingly popular tool for environmental management, supplementing policy tools that were previously widely focused on command-and-control measures. Dozens of programs to reward provision of environmental services (ES) are currently being implemented at multiple geographical scales around the world See e. g. www. watershedconnect. com, www. ecosystemarketplace. com and http://www. oas. org/ dsd/PES/Database. htm#). PES feature direct, conditional contracts to achieve a negotiated environmental outcome between a provider and a user of environmental services  [1]. The underlying rationale is that if the service user's gain from pro-environmental action is sufficiently large, there may be a good case for compensating the service-providing landowners for choosing a profit-wise second-best, but environmentally more benign resource use.
Sample and Methods While hundreds of PES schemes are reported loosely upon in the literature, most contributions do not provide sufficient in-depth information to be useful for quantitative meta-analysis. We selected our case studies based on different methodological guidelines for meta-analysis, derived from clinical and social sciences  [37] [38] [39]. We carried out a systematic literature research drawing on various pre-existing PES databases, qualitative PES reviews, journal storage (JSTOR) and other web-based (Google Scholar) sources (Table  1 ). From the resulting identified records, we filtered out cases where payments to ES providers (i) could not be confirmed to have occurred at least once (never mind whether the program was still ongoing); (ii) did not conform well to our conditionality-focused definition for PES; or (iii) did not in the PES case study deliver sufficient descriptors to meet our minimum data standards.