dataesr/scientific-paragraphs-categorization huggingface dataset:
The recent work of  Fauteux et al. (2016)  is consistent with our observations. These authors measured lemming abundance very close to our snow study site right after snow melt in a large 9 ha exclosure to minimise predator impact on populations. The exclosure data are therefore expected to be more likely to be affected mostly by just snow conditions. Their data show counts of 6 lemmings ha -1 in June 2014 vs. 2 lemmings ha -1 in June 2015, consistent with our field observations. This correlation is of course very preliminary but serves to illustrate the potential impacts of snow conditions on lemming population dynamics.
This is an important process that contributes to the observed inverted density profiles (Fig. 2 )  (Sturm and Benson, 1997)  and hence the inverted thermal conductivity profiles because thermal conductivity is calculated from density only. The differences highlighted in Fig. 13  are not due to specificities of Crocus. Simulations with SNOWPACK version 3. 30 driven by North American Regional Reanalysis (NARR) data (http: //www. esrl. noaa. gov/psd/data/gridded/data. narr. html) for the same dates (not shown) revealed similar inverted density and thermal conductivity profiles  (A. Langlois and J. -B. Madore, personal communication, 2016). The other processes that lead to dense upper layers are wind packing and to a smaller extent weight compaction. The Crocus representation of the wind-packing process cannot be evaluated here, as the density increase also has contributions from water vapour deposition due to the upward flux and their respective contributions cannot be observed separately.
Acknowledgements. This work was supported by the  French Polar Institute (IPEV)  through grant  1042  to FD and by  NSERC  through the  discovery grant program. We thank  Laurent Arnaud  for advice in writing the program to run the needle probes. The  Polar Continental Shelf Program (PCSP) efficiently provided logistical support for the research at Bylot Island. We are grateful to  Gilles Gauthier  and  Marie-Christine Cadieux  for their decades-long efforts to build and maintain the research base of the  Centre d'Etudes Nordiques at Bylot Island. Winter field trips were shared with the group of  Dominique Berteaux , who helped make this research much more efficient and fun. Bylot Island is located within  Sirmilik National Park , and we thank  Parks Canada  and the  Pond Inlet community (Mittimatalik)  for permission to work there.
Then instead to obtain an image like in MRI, in 1 H NMR spectroscopy the signal is transformed in one or two dimensions spectra (Figure  3 ). In 1 H NMR spectroscopy, metabolites are iden tified by peaks shape, peaks multiplicity and peaks correlations. Large databases as the Human Metabolome Database (HMDB) are available to find out all the metabolite characteristics  [6, 7]. Those techniques have advantages and dis advantages but are often complementary (Table  1 ). Basically, 1 H NMR metabolomics approach has the advantage of efficiently obtaining information on large numbers of metabolites in biofluids in vitro as well as in various tissues ex vivo (using HRMAS: highresolution magic angle spinning) and in vivo (using Medical Magnetic Resonance Imaging).
1 H NMR is a highly reproducible, rapid and nondestructive technique. Analysis can be performed with a minimal of sample preparation. Its sensitivity is far lower (microgram vs picogram) than mass spectrometry but sufficient to detect most of changes in biological fluid. MS needs more sample preparation and is a destructive technique. Wellconducted reviews provide larges understanding details on the different platforms especially concerning the technical aspect, physical basedmechanism and analytical method use in metabolomics  [1, 3, 8]. Thus these approaches offer highthroughput analyses at relatively low cost per sample. Multi parameter datasets contain huge quantity of information concerning metabolites in the form of complex spectrum data. Many computerbased processing and statistical tools have been developed to facilitate analysis and interpretation of the data.
Spatially-varying depth and characteristics of observing conditions, such as seeing, airmass, or sky background, are major sources of systematic uncertainties in modern galaxy survey analyses, in particular in deep multi-epoch surveys. We present a framework to extract and project these sources of systematics onto the sky, and apply it to the Dark Energy Survey (DES) to map the observing conditions of the Science Verification (SV) data. The resulting distributions and maps of sources of systematics are used in several analyses of DES SV to perform detailed null tests with the data, and also to incorporate systematics in survey simulations. We illustrate the complementarity of these two approaches by comparing the SV data with the BCC-UFig, a synthetic sky catalogue generated by forward-modelling of the DES SV images.
We analyse the BCC-UFig simulation to construct galaxy samples mimicking those used in SV galaxy clustering studies. We show that the spatially-varying survey depth imprinted in the observed galaxy densities and the redshift distributions of the SV data are successfully reproduced by the simulation and well-captured by the maps of observing conditions. The combined use of the maps, the SV data and the BCC-UFig simulation allows us to quantify the impact of spatial systematics on N (z), the redshift distributions inferred using photometric redshifts. We conclude that spatial systematics in the SV data are mainly due to seeing fluctuations and are under control in current clustering and weak lensing analyses. However, they will need to be carefully characterised in upcoming phases of DES in order to avoid biasing the inferred cosmological results.
Exploiting these observables to probe dark energy requires exquisite control over the spatial coverage and calibration of the survey. Spatial fluctuations in the depth or quality of the data (e. g. , the properties of the sky noise, the photometry, or galaxy ellipticity measurements) can impact the galaxy catalogues and lead to systematic biases in cosmological analyses. All ongoing and future surveys will be limited by our ability to identify and mitigate such systematics. Establishing an exhaustive list of the sources of potential systematics in cosmological measurements is beyond the scope of this paper. However, it is worth recalling that systematics in clustering and cosmic shear studies are mostly rooted in astrophysical foregrounds (extinction by dust or obscuration by bright stars), observing conditions (e.
g. , seeing, sky noise, airmass), or processing and calibration (such as the quality of the photometry or the point spread function). These affect the probability of detecting sources and also their properties, yielding non-trivial distortions in the reduced data, in particular the galaxy catalogues. In DES, various efforts are dedicated to modelling or capturing the complicated transfer function connecting the raw data to the final galaxy catalogues. For instance, the Ultra Fast Image simulator (UFig,  Bergé et al. 2013 ) is used to create simulated DES images, which are then processed in a similar manner to the real data. This approach has been investigated e. g. , to characterise systematics in shear measurements  (Bruderer et al. 2015). UFig was also interfaced with the BCC N-body simulations  (Busha et al.
g. , nightly variations of seeing, or colour reddening by Galactic dust). This paper focuses on mapping these sources of systematics onto the sky. This operation is analogous to the construction of foreground templates for the analysis of cosmic microwave background (CMB) data 1 https://github. com/emhuff/Balrog (e. g. ,  Tegmark 1997; Slosar, Seljak & Makarov 2004; Ade et al. 2014). Such templates are used in numerous analyses of singleepoch surveys like SDSS, in particular galaxy and quasar clustering measurements (e. g. ,  Tegmark et al. 1998; Scranton et al. 2002; Ross et al. 2012; Ho et al. 2012; Leistedt et al. 2013; Leistedt & Peiris 2014; Agarwal et al. 2014) , and are being used in analyses of SV data (e. g. ,  Vikram et al. 2015; Crocce et al. 2015; Jarvis et al. 2015; Becker et al.
2015; Giannantonio et al. 2015). More generally, templates of potential sources of systematics can be used to carry out null tests with the data or model their contamination. As detailed below, multi-epoch surveys such as DES require a dedicated projection framework. In addition, the extracted observing conditions can be incorporated in image simulations to mimic the survey properties. This paper is organised as follows. In Section 2 we present a scheme to map multi-epoch survey data onto the sky, and apply it to DES SV data. We present and analyse the resulting maps of sources of observational systematics. In Section 3 we use these maps to analyse the SV data and the BCC-UFig simulations, and show the impact of observational systematics on the measured galaxy densities and on the redshift distributions inferred using photometric redshifts.
Application to DES SV data Science Verification (SV) data refers to the testing data acquired between November 2012 and February 2013, processed by the "SVA1" version of the DESDM pipeline  (Yanny et al. 2015)  and consisting of 858 coadd tiles, 665 of which have data in all five grizY bands. The SV data cover more than 300 deg 2 in total, split into contiguous regions of interest: the large SPT-E and SPT-W regions (≈ 200 and 50 deg 2 , respectively), the RXC J2248, Bullet, and El Gordo known rich clusters (≈ 10 deg 2 each), COSMOS (≈ 6 deg 2 ) and the Supernovae fields SN-E, SN-X, SN-S, SN-C (≈ 10 deg 2 each). The footprint of DES-SV is shown in Figure  3  with the various fields labelled. To create maps for the full SV data set, we create a symbolic tree of CCD and coadd images, resolve their geometries, and project them into HEALPix maps.
A number of maps were constructed for the DES-SV data, in order to capture the spatial fluctuations of the observing conditions and other observational quantities. They are used in numerous SV analyses to perform spatial null tests with the data (e. g. ,  Vikram et al. 2015; Crocce et al. 2015; Jarvis et al. 2015; Becker et al. 2015; Giannantonio et al. 2015). Figure  4  shows some of the main maps for the i band: the total exposure time, the mean sky sigma and total sky sigma, and the minimum, maximum and mean seeing. All quantities were calculated according to the previous scheme, i. e. , the weighted average method and the sky sigma weights, with the exception of the mean sky sigma maps. This is because the weighted sky sigma is equivalent to the total sky sigma described above. Showing both maps sheds light on the difference between adding the noise properties linearly or in quadrature.
These quantities were obtained from the products presented in the previous section (i. e. , maps of the median observing conditions, analogous to the mean maps shown in Figure  4 ), by averaging each map over the surface of the tiles. The result of this smoothing is shown in Figure  7 , and we comment on its effect on the galaxy catalogues below. The fact that the BCC-UFig is based on simulated coadd images and not on single-epochs is the main difference with the real SV data. However, as discussed below, BCC-UFig reproduces most of the spatial systematics found in the data and relevant to clustering and weak lensing analyses, because these are due to fluctuations in observing conditions at scales larger than the coadds. Survey simulations like the BCC-UFig can be used to test analysis techniques and pipelines in the presence of realistic systematics.
For example, Chang et al. (  2014 ) used the BCC-UFig to compare the performances of various star-galaxy classifiers and study the evolution of the observed galaxy and stellar densities as a function of some of the observing conditions (depth, seeing, Galactic latitude). Such tests cannot be performed at high significance in the real data due to the small size and sky coverage of the sample of spectroscopically confirmed galaxies (based on the COSMOS and SN fields, shown in red in Figure  3 ). In this paper, we produce galaxy catalogues based on the BCC-UFig and compare them with the SV data catalogues. We mostly attempt to mimic the galaxy catalogues used in the clustering and cross-correlation analyses of SV  Crocce et al. (2015) ;  Giannantonio et al. (2015). We first construct a multi-band catalogue by cross-matching the positions of the objects detected in the griz bands.
Photometric redshifts and redshift distributions Photometric redshifts (photo-z) -redshifts estimated from broadband fluxes and colours -are one of the main sources of uncertainties in imaging surveys, and it is essential to reproduce this aspect of the data with BCC-UFig galaxies. We employ three photo-z codes: BPZ  (. Redshift distributions of the SV data (left) and BCC-UFig (right) catalogues obtained using the photometric redshift estimation methods trained on a spectroscopic sample of galaxies (see text for details). They are normalised such that N (z)dz = 1. By comparison, the variance obtained by randomly splitting the redshift samples is of the order of 0. 01 in all redshift bins. data in  Sánchez et al. (2014). They are also used in the main SV clustering and cosmic shear analyses  (Giannantonio et al.
2015; Crocce et al. 2015; Jarvis et al. 2015; Becker et al. 2015). For details on the three codes and a updated comparison using SV data, we refer the reader to  Bonnett et al. (2015). Here we only provide a brief summary of the three algorithms and focus on comparing the redshift distributions inferred from the SV data and the BCC-UFig simulation. BPZ is a Bayesian template fitting photo-z code that relies on a set of calibrated template spectra, which are redshifted and converted into template colours using the DES filters. It computes a posterior probability for the redshift of each object given its observed colours and errors, by fitting for all templates and marginalising over the choice of template. By contrast, TPZ and ANNz2 are machine learning codes that must be trained on a representative sample of the data to infer a set of heuristic rules (i.
We recall that the BCC-UFig and SV data were subject to the same colour and quality cuts, and restricted to the same portion of the sky: the SPT-E region analysis mask shown in Figure  7. Hence, the inferred redshift distributions should match relatively well since the colours of the BCC-UFig galaxies were shown to correctly match that of the data in  Chang et al. (2014). This is confirmed by Figure  8 : when comparing the left and right panels, the features and relative amplitudes between the N (z) inferred from the three codes are very similar. An important difference between the left and right panels is that the true redshift distributions can be calculated for the BCC-UFig and be compared with the distributions inferred using photometric redshifts. Analysing the detailed performance of the photo-z codes is beyond the scope of this paper; a full investigation in the context of the weak-lensing SV data samples is presented in  Bonnett et al.
(2015). However, the results of Figure  8  show that most features of data redshift distributions are recovered in the simulation. For instance, BPZ yields wider N (z)s than machine learning methods, but less accurate near z ∼ 0. 4 due to the layout of the DES grizY filters and the limitations of the set of template spectra. Also, the redshift distributions inferred by TPZ are narrower than the true underlying distribution. These features persist when selecting galaxies with BPZ or TPZ photo-z point estimates. Selecting with ANNz2 minimises the width of the inferred N (z) from the three methods, and reduces the amount of low-redshift outliers in the third bin. The comparison of true and inferred redshift distributions is not trivial with the SV data given the small sample sizes of spectroscopically confirmed galaxies, especially at high redshift.
For this reason, a realistic survey simulation like BCC-UFig is a powerful tool for testing critical analysis stages such as photometric redshift estimation, in regimes that are difficult to explore with the data. More specifically, Figure  8  demonstrates that the features seen in the redshift distributions calculated for the SV data are compatible with and well-reproduced by the BCC-UFig simulation. We now challenge an assumption made above (and in current SV analyses): the fact that the redshift distributions can be spatially averaged over a large area without accounting for systematics (here the entire SPT-E region). While the analysis of SV data is restricted to the most uniform regions, as shown by the analysis mask in Figure  7 , these include unavoidable residual depth and quality fluctuations.
This is indeed observed in most bins, even though this depends on the details of the photometric redshift estimation. While the observed N (z) fluctuations are significant compared to the sample variance, they are small compared to the overall amplitudes shown in Figure  8  (less than 5% in all the cases we tested). This can be seen not only in the histograms of the true redshifts from BCC-UFig, but also in the N (z) inferred from the photo-z codes. In fact, in the right panels corresponding to BCC-UFig, these distributions follow the fluctuations of the true redshifts. This is not the case in all panels, because low and high redshift objects suffer from other issues that make the comparison dif- ficult. In particular, we did not re-weight the redshift distributions to adjust the colour distributions of the training, validation and data samples, as in  Bonnett et al.
(2015) ;  Sánchez et al. (2014). Such corrections do not affect the comparison between the data and the simulation. This analysis provides an estimate of the order of magnitude of the N (z) fluctuations due to observational sources of systematics and spatially varying depth in the SV data. Provided the mean N (z) is properly characterised, these fluctuations will not bias the cosmological analyses. However, because they are due to residual spatial systematics, they may cause other types of contamination in the galaxy catalogues. This is shown in  Crocce et al. (2015)  and in the next section, where seeing is found to spuriously correlate with the SV data and contaminate the clustering measurements.
Spatial null tests We now turn to the spatial properties of the BCC-UFig redshift samples. Figure  11  shows the average galaxy density measured in the previous redshift samples as a function of a few sources of systematics (median exposure time, seeing, and sky sigma). We create these data points by jointly analysing HEALPix maps (at N side = 4096) of the galaxy redshift bins (SV data and BCC-UFig) and the maps of observing conditions presented in the previous sections. Prior to estimation, all maps are divided by their mean values, so that the observables are dimensionless and con-centrated near the central values (1, 1) in the panels of Figure  11. The dynamical range explored by the galaxy densities in each panel depends on the observational quantity under consideration. For example, normalised seeing values are mostly concentrated between 0.
g. , seeing). However, fluctuations in the noise and depth can be significant on sub-coadd scales. This explains why the correlation coefficient between the map of total sky sigma and the magnitude errors in BCC-UFig is less significant than what found in the data. Finally, as shown in Figure  12 , the observing condition maps correlate with the stellar contamination in the BCC-UFig samples. This test cannot be performed with the SV data since object types are only available for a small sample of spectroscopically confirmed sources, restricted to a small region of the sky, as shown in the previous sections. A large, realistic simulation such as BCC-UFig allows us to confirm that the observing conditions trace the main sources of spatial systematics in the galaxy samples, and can be used to model and remove them in cosmological studies.
Pearson correlation coefficients between observing conditions, stars missclassified as galaxies in the BCC-UFig reduced data, and mean i band magnitude errors (magerr auto i) in both the BCC-UFig and SVA1-Gold galaxy catalogues (in magnitude bins, using mag auto i). This shows that the maps of the observing conditions are significantly correlated with the stellar contamination and depth fluctuations in the SV data and simulations, therefore capturing the main sources of spatial systematics present in the galaxy samples. B C C -U F ig M is c la s s if ie d S t a r s B C C -U F ig M a g E r r i ( 1 8. 0 -2 1. 5 ) B C C -U F ig M a g E r r i ( 2 1. 5 -2 2. 5 ) B C C -U F ig M a g E r r i ( 2 2. 5 -2 4. 0 ) S V A -G o ld M a g E r r i ( 1 8. 0 -2 1. 5 ) S V A -G o ld M a g E r r i ( 2 1. 5 -2 2.
CONCLUSIONS AND OUTLOOK We detailed a method to extract and project the properties of multiepoch galaxy surveys onto the sky, making use of the properties of the images and the HEALPix pixelisation. We applied this technique to the DES SV data and mapped the main sources of observational systematics, including the average properties of seeing, airmass, and sky sigma. These maps will be made publicly available in the forthcoming DES data releases, and are currently used in analyses of SV data (e. g. ,  Vikram et al. 2015; Crocce et al. 2015; Jarvis et al. 2015; Becker et al. 2015; Giannantonio et al. 2015). High-resolution maps of the observing conditions can be used as templates to identify, model, and mitigate spatial systematics or residual contamination in the data. As an illustration, we measured the galaxy densities and redshift distributions of DES SV tomographic redshift galaxy samples, and showed that they were significantly affected by the observational conditions of the survey due to residual depth and photometry fluctuations.
Photometric redshifts (photo-z) -redshifts estimated from broadband fluxes and colours -are one of the main sources of uncertainties in imaging surveys, and it is essential to reproduce this aspect of the data with BCC-UFig galaxies. We employ three photo-z codes: BPZ (Benítez 2000; Coe et al. 2006) , TPZ (Carrasco Kind & Brunner 2013 , 2014), and ANNz2 (Sadeh, Abdalla & Lahav 2015). These rely on very distinct algorithms that were tested on early SV.
Figure12. Pearson correlation coefficients between observing conditions, stars missclassified as galaxies in the BCC-UFig reduced data, and mean i band magnitude errors (magerr auto i) in both the BCC-UFig and SVA1-Gold galaxy catalogues (in magnitude bins, using mag auto i). This shows that the maps of the observing conditions are significantly correlated with the stellar contamination and depth fluctuations in the SV data and simulations, therefore capturing the main sources of spatial systematics present in the galaxy samples.
ACKNOWLEDGEMENTS BL, HVP and FE are supported by  STFC  and the  European Research Council  under the European Community's  Seventh Framework Programme  ( FP7/2007-2013)  /  ERC  grant agreement no 306478-CosmicDawn. This work was supported in part by  National Science Foundation  Grant No. PHYS-1066293  and the hospitality of the  Aspen Center for Physics. CC, AR, AA  and CB are supported by in part the  Swiss National Foundation  grants  200021-149442  and  200021-143906. FS acknowledges financial support provided by  CAPES  under contract No. 3171-13-2. ML is partially supported by  FAPESP  and  CNPq. We acknowledge use of the HEALPix software package (Górski et al. 2005). We are grateful for the extraordinary contributions of our CTIO colleagues and the  DES Camera, Commissioning and Science Verification  teams in achieving the excellent instrument and telescope conditions that have made this work possible.