dataesr/scientific-paragraphs-categorization huggingface dataset:
Every next time the model was started again from 00:00 01. 01. 1948 and initialised with the ocean state obtained at the end of the previous cycle, that is, 24:00 31. 12. 2000. This 'cyclic' integration was performed because the ocean model spin-up needs several hundred years, but the longest available consistent reanalysis data set (NCEP/ NCAR) is only about 60 yr long. The coupled model simulation for every one of the five setups was started from the end of the third repeat cycle of the uncoupled ocean model (setting the ocean state from 31. 12. 2000 to 01. 01. 1948) and from 00:00 01. 01. 1948 using NCEP/NCAR reanalysis data for the atmosphere. With the ocean model running uncoupled (i. e. in standalone mode), an inconsistency in fresh water budget arises. On the one hand, precipitation and river runoff are prescribed from reanalysis or observational data.
As a disadvantage of this approach, we can mention the necessity for additional model  runs for obtaining temporally constant freshwater flux corrections. Heat and momentum fluxes are not adjusted. As a reference for large-scale atmospheric circulation, we consider ERA-40 reanalysis data  (Uppala et al. , 2005). We preferred using ERA-40 data for comparison, since it represents better the climate variables in the Arctic Ocean  (Lindsay et al. , 2014)  than NCEP/NCAR reanalysis, and it is different from the dataset (i. e. NCEP/NCAR) used to force the model on the boundaries. There is a difference between time spans of model simulations (1948Á2007) and the ERA40 reanalysis period (1958Á2001). To analyse the modelled climatological means we used the period of 1958Á 2001 for a consistent comparison with reanalysis data.
7 ). Extension of the Pacific setup into the North Atlantic (AP setup) leads on average to colder temperatures and drastically changes the ice formation in the coupled domain. There are also significant differences in the time series of the simulated total sea ice area in recent decades (Fig. 8 ). The observational sea ice area data for the Arctic Ocean domain were calculated from the ice concentration data from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) and the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/ Imager (SSM/I) Passive Microwave Data dataset  (Cavalieri et al. , 1996)  (http://nsidc. org/data/nsidc-0051. html). The Arctic Ocean domain does not include the Nordic Seas, that is, the GIN (Greenland, Irminger and Norwegian) seas, the Barents Sea and the Kara Sea.
Climate variability The AO pattern obtained from different model configurations is presented in Fig. 9. The AO was computed as the leading mode of MSLP variability poleward of 208N  (Thompson and Wallace, 1998). Our atmospheric model domains do not cover the whole area required to calculate the AO. To avoid this problem we merged the modelled MSLP and the MSLP of the forcing data set (NCEP). EOF analysis was applied to the combined global reanalysis and regionally modelled MSLP. Because of the merging into the NCEP data set we found it reasonable to compare the resulting AO with those obtained from the NCEP MSLP. In Fig. 9 , we show the comparison of the modelled AO with those obtained from NCEP Reanalysis, to analyse possible differences between the modelled data and the data used as boundary conditions.
Acknowledgements The model simulations were performed at the  German Climate Computing Center (DKRZ). We thank  ECMWF  (http://www. ecmwf. int)and NOAA (http://www. esrl. noaa. gov) for providing ERA40 and NCEP reanalysis data. The comments of Sergey Danilov helped to improve the manuscript. D. V. Sein was supported by the  German Federal Ministry of Education and Research (BMBF)  under projects  NORDATLANTIK  (research grant  03F044E ) and  SPACES-AGULHAS  (research grant  03G0835B ). N. V. Koldunov was supported by  ESA/ESRIN (Sea Ice CCI). J. G. Pinto  was partially supported by the  German Federal Ministry of Education and Research (BMBF)  under project ' Probabilistic Decadal Forecast for Central and western Europe ' (MIKLIP-PRODEF, contract  01LP1120A ). W. Cabos was supported by the  Spanish Ministry of Education and Science  under project  CGL2008-05112-C02-02.
Aims. The main aim of this work is the characterization of physical properties of galaxies detected in the far infrared (FIR) in the AKARI Deep Field-South (ADF-S) survey. Methods. Starting from a catalog of the 1 000 brightest ADF-S sources in the WIDE-S (90µm) AKARI band, we constructed a subsample of galaxies with spectral coverage from the ultraviolet to the far infrared. We then analyzed the multiwavelength properties of this 90µm selected sample of galaxies. For galaxies without known spectroscopic redshifts we computed photometric redshifts using the codes Photometric Analysis for Redshift Estimate (Le PHARE) and Code Investigating GALaxy Emission (CIGALE), tested these photometric redshifts using spectroscopic redshifts, and compared the performances of both codes. To test the reliability of parameters obtained by fitting spectral energy distributions, a mock cataloge was generated.
For this purpose, we used CIGALE (Code Investigating GALaxy Emission 1  ,  Noll et al. 2009 ), which provides physical information about galaxies by fitting spectral energy distributions (SEDs) covering wavelengths from the UV to FIR. Our main aim is to study this galaxy sample to determine the main characteristics of their star formation and dust emission. The paper is organized as follows. In Sect. 2 we present the data used for our analysis. Section 2. 1 describes the spectroscopic redshifts in the sample. In section 3 we present the main input parameters used by CIGALE  (Noll et al. 2009 ) to fit the SEDs of our galaxies, and in section 3. 1 we discuss selection criteria used for our analysis. In the same section we also present tests of reliability of the parameters estimated by CIGALE.
Data The main aim of our work is to build a galaxy sample with highquality fluxes from the UV to the FIR. As the starting point, we use the ADF-S multiwavelength cataloge  (Małek et al. 2010) , based on 1 000 ADF-S sources which are the brightest in the WIDE-S (90 µm) AKARI band. This cataloge was created by cross-identification of the ADF-S point source catalog (based on 90 µm) with publicly available databases, mainly the SIMBAD 2  and the NASA/IPAC Extragalactic Database 3  (NED). The search for ADF-S counterparts was performed within the radius of 40" around each source which corresponds to the apperture size in two FIS AKARI bands, N60 and WIDE-S, centered on 65 and 90 µm, respectively  (Sirahata et al. 2008). Most of the ADF-S counterparts are located at an angular distance from the source that is significantly smaller than the nominal resolution of the FIS detector (∼7").
A few counterparts that are found at larger angular distances from the source are more likely chance coincidences. The angular distribution of the final sample used for the analysis presented in this paper is discussed in Sect. 4 (for the spectroscopic redshift sample) and Sect. 5 (for the photometric redshift sample). We have also checked angular sizes of the counterparts for all possible sources. We have found that for 10% of our sample the major axis of the optical counterpart was larger than the radius of the aperture used in the WIDE-S AKARI band, and among them, 18 galaxies (4% of the total galaxy sample) have angular sizes a few or even tens times larger than the aperture radius of the AKARI WIDE-S band. We decided to use these data for the subsequent analysis, but -as will be discussed below -all the sources with angular sizes significantly exceeding aperture size were finally rejected from the final sample because of the poor quality of their fitted SEDs.
Some of the ADF-S sources have possible multiple counterparts; however, in the final sample we left only galaxies for which the visual inspection confirmed that their identifications are most likely the correct ones, and the contamination from other sources can be neglected. Among these, the sample of sources which we could successfully cross-identify with previously known extragalactic objects (observed by other missions) contains 545 galaxies. In addition to the data presented in  Małek et al. (2010) , additional measurements, mostly from Wide-field Infrared Survey Explorer (WISE,  (Wright et al. 2010 )) and Galaxy Evolution Explorer (GALEX,  (Dale et al. 2007 )), as well as additional information from public databases (mainly the SIMBAD, NED and NASA/IPAC Infrared Science Archive 4  -IRSA) were used in our analysis.
In ∼ 15% of cases Spitzer/MIPS measurements were taken from the NED database  (Scott et al. 2010) ; the remaining Spitzer/MIPS measurements used for our analysis were made directly from images. In Table  1 , we summarize the main data used in the paper. We have also checked the angular sizes of the counterparts for all possible sources. We have found that for 10% of our sample the major axis of the optical counterpart was larger than the radius of the aperture used in the WIDE-S AKARI band, and among them, 18 galaxies (4% of the total galaxy sample) have angular sizes a few or even tens of times larger than the aperture radius of the AKARI WIDE-S band. We decided to use these data for the subsequent analysis, but -as will be discussed below -all the sources with angular sizes significantly exceeding aperture size were finally rejected from the final sample because of the poor quality of their fitted SEDs.
Spectroscopic redshift information The redshift information was originally only available for 48 galaxies among the 545 galaxies identified in  Małek et al. (2010) ; for an additional 5 galaxies, we found redshifts in NED after first publication. Additionally, we used spectroscopic redshifts for ADF-S sources observed at 90µm, measured by  Sedgwick et al. (2011). Combining data from  Małek et al. (2010) ,  Sedgwick et al. (2011) , and new data from the NED and SIMBAD databases, gives us 173 galaxies with known spectroscopic redshifts. Nearly all of the sources in these sample are nearby galaxies, with a mean redshift of 0. 089 ± 0. 014, and a median value of 0. 058. The range (0 < z < 0. 25) corresponds to the range used by  Sedgwick et al. (2011)  to calculate the far-infrared luminosity function of local star-forming galaxies.
Only five galaxies (5. 29%) have no measurements from the WISE survey (MIR bands); 85. 26%   (2011)  catalog. These galaxies also belong to the sample of the 1 000 sources brighter than 0. 0310 Jy from the ADFS catalog that are analyzed in this paper. The first 1 000 of brightest sources from the ADF-S catalog in WIDE-S band are marked with gray × symbols. Sources with spectroscopic redshifts are shown as full circles. galaxies from our sample are detected in all 2MASS bands (J, H, K s ); half of our sample was also detected in the UV (FUV and NUV bands from GALEX survey); 23 galaxies have at least one IR measurement in the IRAS database. For the whole sample the data cover the optical to FIR part of spectrum. Missing FUV measurements for one half of the sample might result in a worse fitting for the spectra, but additional tests with and without GALEX data confirmed the homogeneous quality of fitted spectra in our sample.
We concluded that the obtained results can be used for analysis with the same significance for the whole sample. In most cases the lack of UV measurement is probably caused by the selection effects: at the measured redshift, the expected UV flux is too low to be detected by GALEX. The percentage of available measurements in NIR, MIR, and FIR suggests that our sample consists of galaxies that are bright in IR and rich in dust. A high ratio of sources visible in UV implies ongoing star formation processes in these galaxies (i. e. , the presence of numerous young stars emitting in UV). All available measurements from the ADF-S database were used for the SED fitting.
2005). Templates with α SED 1 represent very active star-forming galaxies. Normal galaxies have 1 < α SED < 2. 6, where α SED ∼ 2. 5 means quiescent  (Dale et al. 2001; Dale & Helou 2002). Galaxies with α SED higher than 2. 5 seem to have their FIR emission peak at even longer wavelengths than most quiescent galaxies ever studied  (Dale et al. 2005). In other words,  Dale & Helou (2002)  describes the progression of the far-infrared peak toward shorter wavelengths for increasing global heating intensities  (Dale et al. 2001). The CIGALE code uses 64 templates for α SED values from 0. 0625 to 4. 0. After a number of tests, we decided to use eight of them in our analysis. The list of input parameters of the CIGALE code is shown in Table  2. Redshift (either spectroscopic or photometric) is also used as an input parameter in SED fitting.
We adopt a model of emission from stars given by  Maraston (2005) , with an IMF described by  Kroupa (2001). The star formation history used in CIGALE is the combination of models for young and old populations. In our analysis we adopted an exponentially decreasing star formation rate for the old stellar population, starting 13 Gyr ago, and constant SFR (the so-called box model) for the young stars, starting 0. 025 to 1 Gyr ago. The CIGALE code provides results for more than ten parameters based on SED model-fitting, using as a scaling parameter galaxy mass (M gal ). Examples of the best-fit models obtained from CIGALE are given in Figure  3.
Then, the last step of checking estimated parameters is to run CIGALE code on the mock sample, using the same set of input parameters as in the first iteration, and compare the parameters of the artificial cataloge and analyzed observed sample. Figure  4  compares the output parameters of the mock cataloge versus values estimated by the code. The comparison between the results from the mock and real cataloges shows that CIGALE gives a very good estimation of parameters concerning 1. star formation history, based on the  Maraston (2005)  model and IMF from  Kroupa (2001) : -stellar mass (M star ); -star formation rate (SFR); -the age-sensitivity D4000 index defined as the ratio between the average flux density in the 400-410nm range and that in the 385-395 nm range  (Boquien et al. 2012 ). 2.
Uncertainties of the physical parameters The CIGALE code derives properties of a galaxy and the associated uncertainties by analyzing the probability distribution function for each parameter. The code generates the output parameter file in which deviations from measured fluxes in all fluxes, errors used in the fitting process, weighted mean values, and stan-dard deviations for all physical parameters are listed  (Noll et al. 2011). To compute uncertainties for the parameters CIGALE uses probability distribution functions (PDFs) described in detail in  Noll et al. (2009). Uncertainties obtained for the physical parameters by CIGALE are shown in Figure  4  on the vertical axes. Additionally, the mean uncertainties of the parameters used in our analysis are shown in Figure  7  as a dP, where P is the name of parameter.
3 describes young galaxies, while old, elliptical galaxies are concentrated around a typical D4000 value of 1. 85. Most objects in our sample (58. 9%) have low values of D4000 (< 1. 5). Galaxies with D4000 lower than 1. 3 comprise 17. 8%. This suggests that our sample consists of dust-rich star-forming spiral or lenticular galaxies. We have checked the morphology distribution for all 73 galaxies in our sample; for half of them the classification is given in the NED database; we have found that 50% of our sample belongs to the spiral, and 49% to lenticular galaxies, respectively. The result is consistent with  Małek et al. (2010). We found that D4000 is inversely correlated with SFR (see Figure  8 ). A linear correlation with stellar mass is also noticable: the more the massive galaxy is, the higher its SFR is at the same D4000 level.
Simple checks in the NED database did not reveal any morphological nor environmental peculiarities for a group of galaxies characterized by low SSFR and high stellar mass seen in the the bottom-right corner in Figure  11. The distribution of the heating intensity α SED shown in Figure  7  confirms the conclusion that most of our sample consists of normal, nearby galaxies, active in star formation. Only 7. 5% of our galaxies have α SED higher than 2. 6, which is the limit for normal galaxies given by  Dale & Helou (2002). The mean value of α SED for 92. 5% of galaxies with α SED < 2. 6 is equal to 1. 9 ± 0. 7, with a median value of 1. 9, which confirms that our sample is not extremely active, but still very bright in the Fig. 8. The relation between D4000 and SFR. Open squares represent galaxies with 11 < logM star < 11.
Photometric redshifts As explained in Sect. 2. 1, spectroscopic redshifts (z spec ) are now available for 173 galaxies out of the 545 galaxies identified by  Małek et al. (2010). In our sample, 416 sources are identified in public catalogs as galaxies, with additional photometric information but no redshift. To enlarge our sample, we estimated photometric redshifts (z phot ) of the entire sample. We used two codes: Photometric Analysis for Redshift Estimate (Le PHARE,  Arnouts et al. 1999; Ilbert et al. 2006) , and Code Investigating GALaxy Emission (CIGALE,  Noll et al. 2009). We performed a test on a sample of 95 galaxies with known z spec and at least six photometric measurements in the whole spectral range. Le PHARE estimates photometric redshifts based on a χ 2 fitting method between the theoretical and observed photometric catalog.
(2006). The distribution of ∆z is shown in Figure  12. A possible way to improve the estimation of photometric redshifts through including FIR data is provided by CIGALE. Originally, CIGALE was not developed as a tool for estimation of z photo , but since it uses a large number of models covering the whole spectrum including IR, it may be expected to provide better z photo than the software using mainly optical to NIR data, especially in the case of IR-bright galaxies. The final redshifts assigned by CIGALE are based on the reduced best-fit χ 2 value of the fit. In the same sample of the 95 galaxies which were also given to Le PHARE, CIGALE found redshifts for all of them. Only seven of these turned out to have catastrophic errors, and even these were similar to them in the case of photometric redshifts given by Le PHARE (mean value of |z spectro -z LePhare |/(1+z spectro ) for the catastrophic errors if equal to 0.
Conclusions We built a large multiwavelength catalog of 519 ADF-S galaxies brighter than 0. 0301 Jy in the WIDE-S (90µm) AKARI band. For 129 of them, spectroscopic redshifts were obtained from public databases and  Sedgwick et al. (2011)  surveys. The CIGALE  (Noll et al. 2009)  program for fitting spectral energy distribution, was used first to estimate photometric redshifts, and then to fit SED models to our sample. Although CIGALE was not created to estimate z phot , it is more efficient than the standard code (i. e. , Le PHARE), which is based mainly on optical and NIR data. Spectral energy distributions were fitted for all 256 galaxies with known redshift (spectroscopic or photometric estimated by CIGALE), and for 186 galaxies the χ 2 reduced parameter for the fit was lower than 4. The reliability of the retrieved parameters, for galaxies with known spectroscopic redshifts, was checked using a mock cataloge of artificial galaxies.
Acknowledgements. This work is based on observation with AKARI, a  JAXA  project with the participation of  ESA. This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France , the  NASA/IPAC  Extragalactic Database (NED) which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration. This research has made use of the  NASA/ IPAC Infrared Science Archive , which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration. KM and AP were financed by the research grants of the  Polish Ministry of Science  N  N203 512938 , and  UMO-2012/07/B/ST9/04425. Our research were conducted in the scope of the  HECOLS International Associated Laboratory.
taken from the SIMBAD database without any additional corrections. Survey Band Wavelength No. all GALEX FUV 0. 1µm 78 GALEX NUV 0. 2µm 98 APM a B j 0. 4µm 401 SIMBAD (b) B 0. 4µm 55 SIMBAD (c) R 0. 6µm 30 SIMBAD (d) J 1. 2µm 66 2MASS J 1. 2µm 204 SIMBAD (e) H 1. 7µm 66 2MASS H 1. 7µm 204 SIMBAD ( f ) K 2. 2µm 66 2MASS Ks 2. 2µm 204 WISE W1 3. 4µm 280 WISE W2 4. 6µm 280 WISE W3 12µm 280 IRAS Band-1 12µm 18 MIPS 24µm 23. 7µm 100 IRAS Band-2 25µm 19 IRAS Band-3 60µm 35 AKARI N60 65µm 116 MIPS 70µm 71. 4µm 15 AKARI WIDE-S 90µm 545 IRAS Band-4 100µm 30 AKARI WIDE-L 140µm 65 AKARI N160 160µm 9.
Image coaddition was done using a median coadd rather than by using a weighted mean as in DESDM. Photometric calibration in the ugriz filters for the SN-X3, VVDS F14, and COSMOS fields was done by matching against overlapping bright stars from the SDSS Data Release 9 database  (Ahn et al. 2012). This was done to calibrate each individual CCD on each separate DECam exposure, before image coaddition. In the Y band for all fields, and in all filters for the SN-C3 field (which did not overlap SDSS), we picked a fiducial exposure for each field, adopted the typical DECam CCD-by-CCD photometric zeropoints as determined from DES SV data, and then tied the photometry for subse- 30 • 60 • 90 • 120 • 150 • 180 • 210 • 240 • 270 • 300 • 330 • RA -75 • -60 • -45 • -30 • -15 • 0 • 15 • 30 • 45 •.
RVMz RVMz is an empirical photo-z code based on the relevance vector machine algorithm from  Tipping (2001) , a Bayesian sparse kernel method for regression. The relevance vector machine (RVM) has characteristics similar to the support vector machine, but includes a Bayesian treatment for the determination of the model weights. This has the advantage that the parameters governing model complexity and noise variance are found in the training run itself, and therefore the RVM does not require cross validation to optimize these parameters. We use the RVM implementation in the R-package kernlab from  Karatzoglou et al. (2004). To obtain photo-z estimates, we used MAGDETMODEL magnitudes (grizY ) and colors (gr, ri, i-z, z -Y ) as input. We reconstruct the pdf by combining the uncertainties in the datasets and the model.
ANNz2 ANNz2 is a new major version of the public photo-z estimation software, ANNz  (Collister & Lahav 2004 ), which will be made public in 2015. The new code incorporates several machinelearning methods, such as artificial neural networks (ANNs), boosted decision trees (BDTs,  Freund & Schapire (1997) , described below in 4. 1. 7) and k-nearest neighbors (KNNs). The different algorithms are used in concert in order to optimize the photo-z reconstruction performance, and to estimate the uncertainties of the photometric solutions. This is done by generating a wide selection of machine-learning solutions with e. g. , different ANN architectures, initialized by different random seeds. The optimization is performed by ranking the different solution according to their performance, which is determined by the respective photo-z scatter of each solution.
BPZ The BPZ (Bayesian Photometric Redshifts) photo-z code from  Benitez (2000)  and  Coe et al. (2006)  is a template-based method that returns the whole probability density distribution p(z|mi) that the galaxy is at redshift z when its magnitudes in each band are mi, and also a single photo-z value com-puted as the maximum of p(z|mi). Following Bayes Theorem, p(z|mi) is the product of a likelihood and a prior probability function that represents our previous knowledge of the redshift and spectral type t distributions of the sample in the analysis. In the likelihood, we use the five MAGDETMODEL magnitudes. • Templates: We use the eight spectral templates that BPZ carries by default based on  Coleman, Wu & Weedman (1980) ;  Kinney et al. (1996) , and add two more interpolated templates between each pair of them by setting the input parameter INTERP=2 (option by default).