dataesr/scientific-paragraphs-categorization huggingface dataset:
Larsen et al. (2007)  used the SRTM DEM as their final topography after applying a linear correction of SRTM with altitude (2. 6 m per 1000 m elevation, with a -2. 5 offset at 0 elevation) determined by comparing SRTM to August 2000 laser altimetry data. Such a correction would correspond to a maximum SRTM penetration of ∼ 1. 5-2 m above 1500 m a. s. l. , much smaller than what we found here. Thus, the fact that SRTM penetration depth is larger than previously thought over southeast Alaska icefields may explain why  Larsen et al. (2007)  found larger mass losses than  Arendt et al. (2002)  and  Berthier et al. (2010) , who both used only non-penetrating optical data (lidar or stereo imagery). An uneven seasonal distribution of the ASTER DEMs could bias the multi-annual mass balances derived using the ASTER method  (Berthier et al.
Acknowledgements. We thank  Tobias Bolch  (editor),  Robert McNabb  and  Mauri Pelto  for their comments that greatly improved our manuscript. We thank  Fanny Brun  for sharing her Python codes. We thank the  Global Land Ice Measurement from Space (GLIMS)  project, which allowed the population of a vast archive of ASTER stereo images over glaciers. Etienne Berthier  acknowledges support from the  French Space Agency (CNES)  and the  Programme National de Télédétection Spatiale  grant  PNTS-2016-01. Edited by:  Tobias Bolch Reviewed  by:  Mauri Pelto  and  Robert McNabb.
Data availability. The individual ASTER DEMs and the maps of the rate of elevation change over the two icefields are available upon request to Etienne Berthier. The Alaska laser altimetry data are available upon request to Christopher Larsen. More recent laser surveys are available as part of the operation icebridge dataset: https://nsidc. org/data/ILAKS1B/versions/1. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/tc-12-1523-2018-supplement. Author contributions. EB designed the study, carried out the data analysis and led the writing. CL provided the laser altimetry data. WD, MW and MP provided unpublished results. All authors discussed the results and wrote the paper.
For example, the detailed way in which the mass of intracluster medium (ICM) systematically trends with total cluster mass, and the scatter about that mean behavior, shed light on the thermodynamic history of massive cosmic halos (e. g. ,  Ponman et al. 1999; Mohr et al. 1999; Pratt et al. 2010; Young et al. 2011). To date, the bulk of observational results have been obtained using cluster samples selected at low redshift (z 0. 6). Studying scaling relations at high redshift remains difficult due to the lack of sizable cluster samples and/or the adequately deep datasets to extract physical properties from the clusters. Enabled by the Sunyaev-Zel'dovich Effect (SZE;  Sunyaev & Zel'dovich 1970 , 1972 )-a signature on the Cosmic Microwave Background (CMB) that is caused by the inverse Compton scattering between the CMB photons and hot ICM-teams of scientists have developed novel instrumentation and have used it to search for galaxy clusters out to a redshift z ≈ 1.
However, it is important to note that current constraints on the redshift trends of scaling relations suffer from significant systematics introduced by comparing heterogeneous cluster samples analyzed in different ways  (Chiu et al. 2016b ). To overcome these systematic uncertainties, one needs to use a large sample with a wellunderstood selection function and-most importantly-employ an unbiased method on homogeneous datasets to determine the masses consistently across the mass and redshift range of interest. In this study, we aim to analyze the baryon content of massive galaxy clusters selected by their SZE signatures in the 2500 deg 2 South Pole Telescope SZE (SPT-SZ) survey. We have focused on a sample of 91 galaxy clusters that have X-ray observations from Chandra, optical imaging from DES, and near-infrared (NIR) data from Spitzer and WISE.
This sample is selected to lie above a detection significance ξ > 6. 8 and spans a broad redshift range 0. 25 z 1. 25. This sample is currently the largest, approximately mass-limited sample of galaxy clusters extending to high redshift with the required uniform, multi-wavelength datasets needed to carry out this analysis. Moreover, we adopt self-consistent methodologies to estimate the ICM, stellar and total masses of each galaxy cluster in our sample; this dramatically minimizes the potential systematics that could bias the observed mass and redshift trends in the scaling relations. This paper is organized as follows. The cluster sample and data are described in Section 2, while the determinations of cluster mass M 500 , ICM mass M ICM and the stellar mass M are given in Section 3. We describe our fitting procedure and the method for estimating both statistical and systematic uncertainties on the scaling relation parameters in Section 4, and we present the results of power law fits to the observed scaling relations in Section 5.
Optical and NIR data To estimate the stellar mass of each cluster galaxy in our sample, we use optical photometry in the griz bands observed by the Dark Energy Survey  (DES Collaboration 2005 , 2016)  together with nearinfrared (NIR) photometry obtained with either the Wide-field Infrared Survey Explorer (WISE;  Wright et al. 2010a)  or the Infrared Array Camera (IRAC,  Fazio et al. 2004)  of the Spitzer telescope. The combination of DES, WISE, and Spitzer datasets enables us to estimate the stellar masses of cluster galaxies by constraining their Spectral Energy Distributions (SED; see Section 3. 3). The Spitzer observations originate from an SPT follow-up program (see Section 2. 3. 2) that was designed to aid in the cluster confirmation at high redshift (z 0. 4). These data are much deeper than the WISE observations, which we have acquired from the public archive (see Section 2.
3. 3). The field of view of our Spitzer imaging is small and can only sufficiently cover the angular area out to R 500 for clusters at z 0. 4, while we specifically re-process the WISE data such that each resulting coadd image is centered on the cluster with coverage of ≈ 40 × 40 (see Section 2. 3. 3). In addition, the Spitzer imaging has better angular resolution, making it more appropriate for galaxy studies in crowded environmentsespecially cluster cores-at higher redshift. Thus, when we combine the optical data from DES with the additional NIR datasets, we choose to use Spitzer observations whenever available. These shallower WISE observations are used only for the lower redshift clusters. There are 84 out of the 91 clusters covered by the footprints of the Science Verification, Year One and Year Two of the DES datasets, and the remaining 7 clusters are or will be imaged by the continuing efforts from the DES.
Optical dataset For the optical data, the Science Verification, Year One and Year Two of the DES datasets  (Diehl et al. 2016 ) are used to obtain the griz photometry. For each cluster, we build Point Spread Function (PSF)-homogenized coadd images for the griz bands with the field of view of ≈ 1 deg 2 centered on the cluster; this avoids the edge effects that are typically seen in wide field surveys. The optical imaging is processed by the CosmoDM pipeline  (Mohr et al. 2012) , and the full description of data reduction, source extraction and photometric calibration is given elsewhere  (Desai et al. 2012; Liu et al. 2015a; Hennig et al. 2017). These DES catalogs and images have been specifically processed for studying the SPT clusters, and the excellent photometric quality has been presented elsewhere  (Hennig et al.
2 , where the median and root mean square variation of the completeness are plotted with horizontal bars. For clarity, we only show the results of the riz bands where we will perform the magnitude cut on our galaxy samples in the following analysis (see Section 3. 3. 2); the median 50% completeness of the g band is 24. 51 ± 0. 02 mag. In Fig. 2 , we also plot the characteristic magnitude m ,char (z) as a function of redshift for the griz bands as predicted by the CSP model (see Section 2. 1). Overall, the 50% completeness of the griz bands is deeper than m ,char (z) by > 2 mag (≈ 1. 5 mag for z 1. 1). This suggests that the depth of DES optical data is sufficiently deep to detect the cluster galaxies that are dominating the stellar content of our sample, allowing us to estimate the total stellar mass of each cluster.
Spitzer dataset The Spitzer observations are obtained in IRAC channels 3. 6 µm and 4. 5 µm with the Program IDs (PI Brodwin) 60099, 70053 and 80012, resulting in photometry of  [3. 6] and [4. 5] , respectively. The data acquisition, processing and photometric calibration of the Spitzer observations are fully described in  Ashby et al. (2013) , to which we defer the reader for more details. By design, the depths of IRAC observations are sufficient to image the cluster galaxies, which are brighter than m ,char (z) + 1 mag in [3. 6] and [4. 5], as predicted by the CSP model (see Section 2. 1) out to redshift z ≈ 1. 5 with more than 90% completeness. The 10σ depth of [3. 6] and [4. 5] are 22. 00 and 20. 92 mag with 1σ variation of 0. 13 and 0. 18 mag, respectively, as shown in Fig. 2. The field of view of the Spitzer mosaics is ≈ 7 × 7 (and is ≈ 5 × 5 for the full depth), which as already mentioned is sufficient to cover the R 500 region of the clusters in our sample at redshift z 0.
WISE dataset For each cluster, we acquire the NIR imaging observed by the Widefield Infrared Survey Explorer (WISE) through the WISE all-sky survey and the project of Near-Earth Object Wide-field Infrared Survey Explorer (NEOWISE;  Mainzer et al. 2014). The NEOWISE project is part of the primary WISE survey, which started in 2009 with the goal of imaging the full sky in four bands  (3. 4, 4. 6, 12  and 22 µm, denoted by W 1, W 2, W 3 and W 4, respectively). The main mission was followed by the NEOWISE Reactivation Missionbeginning in 2013-with the goal of continuing to find and characterize near-Earth objects using passbands W 1 and W 2. The data taken by the primary WISE survey, the NEOWISE project and its Reactivation Mission have been regularly released since 2011, providing a valuable legacy dataset to the community.
In this work, we collect the imaging of the 91 galaxy clusters in our sample in the passbands of W 1 and W 2 from the ALLWISE data release combined with the release from the NEOWISE Reactivation Mission through 2016, which enables us to detect fainter sources than the official catalogs from the ALLWISE data release. After acquiring the single-exposure images centered on each cluster, we coadd them with the area weighting by the Image Coaddition with Optional Resolution Enhancement (ICORE;  Masci & Fowler 2009 ) in the WISE/NEOWISE Coadder 2. The photometric zeropoint is calibrated on the basis of each single-exposure image using the measurements of a network of calibration standard stars near the ecliptic poles (see more details in  Wright et al. 2010a; Jarrett et al. 2011 ), and we properly calculate the final zero point of the coadd image in the reduction process.
Combining DES and NIR datasets Our goal is to construct a photometric catalog (either grizW 1W 2 or griz[3. 6][4. 5]) for each cluster. However, one of the greatest challenges in combining these multi-wavelength datasets is that the source blending varies from band to band due to the variation in the PSF size. Many methods have been proposed to solve or alleviate the blending problem (e. g. ,  Laidler et al. 2007; De Santis et al. 2007; Mancone et al. 2013; Joseph et al. 2016; Laigle et al. 2016) , and good performance has been demonstrated when one adopts priors based on the images with better resolution. In this work, we use the T-PHOT package (Merlin et al. 2015)-a state-of-the-art PSF-matching technique-to deblend the NIR fluxes on the NIR images observed by either the WISE or Spitzer telescope based on priors from the DES optical imaging.
2017). We also note that we re-process the WISE datasets for the full sample (including the ones with Spitzer follow-up observations); therefore, we are able to (1) increase the statistics in our study of the SED fitting performance on grizW 1W 2 and (2) crosscompare the results obtained between griz[3. 6][4. 5] and grizW 1W 2 for those 52 clusters with both WISE and Spitzer data. In the cluster fields there are 2,149 galaxies in total with spec-z measurements for the clusters with grizW 1W 2 photometry; for the clusters with the IRAC photometry there are 999 galaxies. A comparison between the photo-z's and the spectroscopic redshifts (spec-z or z s ) for this sample is contained in Fig. 3. When using the estimator Z_BEST 3  for the photo-z, we measure the mean bias ∆z ≡ z pz s / (1 + z s ) and the root-mean-square variation about the mean as a function of redshift for the sample (bottom panel).
The results are shown in Fig. 4. We find that the stellar masses m of galaxies show a mild bias as a function of redshift if photo-z's are used: the derived m is biased high by ≈ 0. 05 -0. 07 dex at low redshift and trends to a bias of ≈ -0. 07 dex as the redshift increases to ≈ 1. 2. This fractional bias in stellar mass-denoted as δ (z)-is present for both photometric datasets. To correct for this bias, we fit a linear function δ (z) to the observed δ (z), and we use this model to correct the derived stellar masses of each cluster. We repeat the exercise while using spectroscopic redshifts z s with the two different sets of photometry. The results are shown by the blue diamonds in Fig. 4. This comparison reveals any systematic offsets between the stellar masses when using the different datasets, and this appears to be well described by a redshift independent factor.
Namely, the stellar masses estimated with grizW 1W 2 are systematically lower than those obtained with griz[3. 6][4. 5] by ≈ 0. 031 dex (≈ 7%). We apply the correction ε ≡ log(m ,grizW 1W 2 )log(m ,griz[3. 6][4. 5] ) = -0. 031 dex to the stellar masses estimated from grizW 1W 2 to bring them into consistency with those obtained using griz[3. 6][4. 5] (see Section 3. 3. 3). In summary, the correction ε accounts for the redshift-independent offset in results from the two datasets, while the correction δ (z) 0. 2 0. 4 0. 6 0. 8 1. 0 1. 2 z s 0. 3 0. 2 0. 1 0. 0 0. 1 0. 2 log(m , Y ) -log(m , X ) [dex] accounts for the redshift-dependent discrepancy in the derived stellar masses introduced by using photo-z's. = -0. 031 ± 0. 004 X: photo-z griz[3. 6][4. 5] v. s. Y: spec-z griz[3. 6][4. 5] X: photo-z grizW1W2 v.
Statistical background subtraction To eliminate the contamination of (1) faint stars that are not discarded by the cut in spread_model and (2) non-cluster galaxies due to the photo-z scatter, we perform statistical background subtraction. Specifically, we select the footprint with the field of view of ≈ 1 deg 2 located at the center of the COSMOS field  (Capak et al. 2007; Ilbert et al. 2009 ) as the background field, because this region is also observed by DES and lies within the Spitzer Large Area Survey with Hyper-Suprime-Cam (SPLASH,  Capak et al. 2012) , with the same wavelength coverage as our cluster fields. In the COS-MOS field, we only use the passbands griz  [3. 6][4. 5]  to ensure the uniformity between the optical and NIR datasets available for the SPT clusters. Moreover, we stress that (1) this region is free from any cluster that is as massive as the SPT clusters, (2) we specifically build this background field by coadding the single exposures observed by the DES to reach comparable depth in the griz bands as we have in the cluster fields, even though the combined DES data in the COSMOS field would be much deeper, and (3) the photometric catalogs of the griz bands are also processed and cataloged using the CosmoDM system.
3. 1). We apply the correction to account for the systematics caused by the use of photometric redshifts and a correction to take into account the systematic differences in stellar masses when measured using the two different NIR datasets (grizW 1W 2 and griz[3. 6][4. 5]). We explicitly express the stellar mass estimate M within R 500 of each cluster as follows. M = 10 -ε × M ,BCG + D dpj f mask δ (z) M ,sat , (5) where M ,sat is the integrated stellar masses of non-BCG cluster galaxies that lie within the cluster R 500 ; δ (z) is the linear model as a function of cluster redshift z taking the photo-z bias into account (see Section 3. 3. 1); f mask is the masking correction due to the unobserved area in the footprint; D dpj = 0. 71 is the deprojection factor  (Lin et al. 2003)  converting the galaxy distribution from the volume of a cylinder to a sphere by assuming a NFW model with concentration C 500 = 2; ε = -0.
031 is the correction for the systematic between different NIR datasets (see Section 3. 3. 1); we apply the correction 10 -ε to bring the stellar masses from the basis of grizW 1W 2 to griz[3. 6][4. 5]-therefore-by definition, ε = 0 for the case of using Spitzer datasets. Using a higher concentration C 500 = 3. 2, which would be correct if the galaxy populations in clusters were completely dominated by passively evolving galaxies  (Hennig et al. 2017) , would result in a higher deprojection factor of D dpj = 0. 76. Therefore, we estimate that there is an associated deprojection systematic in the stellar mass estimates that is at the level of ≈ 5%. The resulting M of each cluster is listed in the Table  2.
Cluster halo mass M 500 systematic uncertainties The uniformity of our sizable sample of galaxy clusters that have been selected through their SZE signatures over a wide redshift range of 0. 25 z 1. 25 represents one of the major strengths of this work. Moreover, we analyze the multi-wavelength datasets in the same manner for every system, and in doing so we further avoid systematic uncertainties that can creep in with different treatments by a variety of codes and authors. These two elements of our current analysis enable a reduction in systematic uncertainties in comparison to many previous studies. Nevertheless, halo mass related systematics remain. We quantify the impact of systematic uncertainties due to remaining uncertainties in the SZE observable to mass scaling relation (i. e. , equation (3).
Sources of Accretion onto Halos In Section 5, we have presented the baryon content of galaxy clusters from a large, approximately mass-limited cluster sample (M 500 4 × 10 14 M ) and the variations of these baryonic quantities with halo mass and redshift. Extrapolations of the halo mass trends from our sample to lower masses appear to be in good agreement with results from previous studies that have focused on clusters and groups at lower mass scales (M 500 5 × 10 13 M ). The emerging trends are that all baryonic components of clusters show strong, non-self similar dependences on cluster halo mass but that there is no statistically significant evidence of redshift trends. The models we fit to the current dataset assume redshift and mass trends are separable, and indeed our data provide no evidence of tension with this model.
25 z 1. 25, (2) for each cluster, we study the full baryonic mass (the ICM and stellar content) and the halo mass M 500 using consistent methods on uniform multi-wavelength datasets (optical, NIR, X-ray and mm-wavelength), which dramatically reduces the systematic uncertainties that were present in the previous work, and (3) we derive the scaling relations in a Bayesian framework that fully accounts for various selection biases, especially those associated with the intrinsic scatter of the SZE ζ to mass relation that we use to infer cluster mass M 500. With increased statistical power and reduced systematic uncertainties, we are in a position to more quantitatively constrain the contributions to cluster growth from lower density regions that lie outside the virial region, a region that is often referred to as the field.
Impact of Adopting a Planck CMB Cosmology Prior As described in Section 3. 1, we estimate the cluster mass M 500 using the SZE observable ξ and redshift together with the best fit SZE observable to mass scaling relation from a previous analysis (de  Haan et al. 2016 ) that used the SPT cluster counts together with Y X measurements and an externally calibrated Y X to mass scaling relation. As noted in  Bocquet et al. (2015) , the SZE observable to mass scaling relation parameters can shift when including the cosmology priors from the Planck CMB anisotropy dataset. As quantified in de  Haan et al. (2016) , analysis of the Planck CMB measurements and the SPT cluster dataset within a flat ΛCDM cosmology gives SZE observable to mass scaling relation parameters:  (3. 53, 1. 66, 0. 73, 0. 20). These parameters imply cluster masses M 500 that are typically 22% larger in comparison to the default masses used in this work.
ACKNOWLEDGEMENTS I-Non Chiu  thanks  Chien-Ho Lin ,  Keiichi Umetsu ,  Yen-Ting Lin  and  James Chan  for support and useful discussions during this work. We thank the anonymous referee for the constructive comments that lead to the improvement of this paper. We acknowledge the support by the  DFG Cluster of Excellence "Origin and Structure of the Universe" , the  DLR  award 50 OR 1205 that supported  I. Chiu  during his PhD project, and the  Transregio program TR33  "The  Dark Universe". The data processing has been carried out on the computing facilities of the  Computational Center for Particle and Astrophysics  ( C2PAP ), located at the  Leibniz Supercomputer Center (LRZ) in Garching. The South Pole Telescope is supported by the  National Science Foundation  through grant  PLR-1248097.
The DES data management system is supported by the  National Science Foundation  under Grant Number  AST-1138766. The DES participants from Spanish institutions are partially supported by  MINECO  under grants  AYA2012-39559 ,  ESP2013-48274 ,  FPA2013-47986 , and  Centro de Excelencia Severo Ochoa   SEV-2012-0234. Research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  (FP7/2007-2013) including  ERC  grant agreements  240672 ,  291329 , and  306478. This work is based in part on archival data obtained with the  Spitzer Space Telescope , which is operated by the  Jet Propulsion Laboratory, California Institute of Technology  under a contract with  NASA. This publication makes use of data products from the  Wide-field Infrared Survey Explorer  (Wright et al.
2010b), which is a joint project of the  University of California, Los Angeles , and the  Jet Propulsion Laboratory/California Institute of Technology , funded by the  National Aeronautics and Space Administration. This publication also makes use of data products from NEOWISE, which is a project of the  Jet Propulsion Laboratory/California Institute of Technology , funded by the  Planetary Science Division of c 0000 RAS ,  MNRAS 000 ,  000-000  Baryon Content of Galaxy Clusters 23 the  National Aeronautics and Space Administration. This research has made use of data obtained from the  Chandra Data Archive  and the  Chandra Source Catalog , and software provided by the  Chandra X-ray Center (CXC)  in the application packages CIAO, ChIPS, and  Sherpa. This  research made use of ds9, a tool for data visualization supported by the  Chandra X-ray Science Center (CXC)  and the  High Energy Astrophysics Science Archive Center (HEASARC)  with support from the  JWST Mission  office at the  Space Telescope Science Institute  for 3D visualization.