dataesr/scientific-paragraphs-categorization huggingface dataset:
Context. The Planck data releases have provided the community with sub-millimetre and radio observations of the full-sky at unprecedented resolutions. We make use of the Planck 353, 545 and 857 GHz maps alongside the IRAS 3000 GHz map. These maps contain information on the cosmic microwave background (CMB), cosmic infrared background (CIB), extragalactic point sources and diffuse thermal dust emission. Aims. We aim to determine the modified black body (MBB) model parameters of thermal dust emission in total intensity and produce all sky maps of pure thermal dust, having separated this Galactic component from the CMB and CIB. Methods. This separation is completed using a new, sparsity-based, parametric method which we refer to as premise. The method comprises of three main stages: 1) filtering of the raw data to reduce the effect of the CIB on the MBB fit.
2) fitting an MBB model to the filtered data across super-pixels of various sizes determined by the algorithm itself and 3) refining these super-pixel estimates into full resolution maps of the MBB parameters. Results. We present our maps of MBB temperature, spectral index and optical depth at 5 arcmin resolution and compare our estimates to those of GNILC as well as the two-step MBB fit presented by the Planck collaboration in 2013. Conclusions. By exploiting sparsity we avoid the need for smoothing, enabling us to produce the first full resolution MBB parameter maps from intensity measurements of thermal dust emission. We consider the premise parameter estimates to be competitive with the existing state-of-the-art solutions, outperforming these methods within low signal-to-noise regions as we account for the CIB without removing thermal dust emission through over-smoothing.
Introduction The meticulous separation of the cosmic microwave background (CMB) from astrophysical foregrounds is a pivotal step in extracting cosmological information from the Planck data. For intensity data several component separation techniques are currently in use; techniques such as SEVEM  (Fernández-Cobos et al. 2012) , NILC  (Delabrouille et al. 2009) , SILC  (Rogers et al. 2016) , SMICA  (Delabrouille et al. 2003 ) and L-GMCA  (Bobin et al. 2014 ) are specialised to recover only the CMB signal from all other emission sources. At present only Commander  (Eriksen et al. 2008) , a Bayesian parametric technique, can produce estimates of the CMB as well as the astrophysical foregrounds. Most recently, a generalised version of NILC, coined GNILC, has been applied to Planck HFI data to recover the thermal dust emission (Planck Collaboration 2016c).
This is not the case for polarisation data, where an updated version of Commander, SMICA and GNILC are all capable of producing estimates for polarised thermal dust and synchrotron emission (Planck Collaboration 2018b). As our interest lies in the recovery of astrophysical foregrounds as well as the CMB and we begin our analysis using intensity data only, we initially focus on the specific problem of acquiring pure thermal dust emission maps tackled by Commander and GNILC. Thermal dust emission is the dominant diffuse Galactic emission within Planck HFI data. Within the three highest HFI channels  (353, 545 and 857 GHz)  the other non-negligible contri-butions to the total intensity are the CMB, the cosmic infrared background (CIB), extragalactic point sources and instrumental noise. Whilst the resolvable extragalactic point sources can be masked out, the unresolved extragalactic background emission that makes up the CIB requires a more global solution.
The CIB makes two contributions to the total intensity measured at a particular frequency: a constant additive offset level and variations around this mean level referred to as the CIB anisotropies (CIBA). Planck Collaboration (2014b) provide a method to determine the CIB offset level using correlations with H I data; while the CIB offset level can be subtracted, the CIBA cannot. The CIBA contribution at each frequency can be approximated as Gaussian distributed noise at Planck resolutions (Planck Collaboration (2014b); Planck Collaboration (2016c)) but at scales smaller than a few arcminutes the CIBA traces structure formation at high redshifts  (Béthermin et al. 2013). Though not strictly a component separation problem, as there is only one well-defined diffuse emission source within an almost-Gaussian background, the Planck HFI data are an excellent testbed for techniques which aim to identify and characterise emission sources.
It should be noted that the single spectral index MBB is not the only possible model,  Meisner & Finkbeiner (2015)  show that when considering frequencies both below and above 353 GHz the use of two spectral indices (one for low frequencies, one for high) yields an improvement in the fit. The latest Planck HFI data processing paper (Planck Collaboration 2018a), however reveals an improvement in the calibration of the 100, 217 and 353 GHz maps. Therefore it is possible that the need for a two-spectral index model was due to the calibration discrepancy between frequencies above and below 353 GHz in the previous data releases. More physically motivated dust particle models are also under consideration  (Jones et al. (2017) ;  Guillet et al. (2018) ), as are models which account for spectral deviations from the average MBB fit along a line-of-sight using Taylor expansion  (Chluba et al.
After smoothing and recomposition back into the pixel domain they can fit the MBB model, or in fact any model, to determine thermal dust model parameters. Commander (Planck Collaboration 2016a) give two 353 GHz thermal dust estimates, both determined from an MBB fit but to different data sets. One of the dust estimates is produced at N side 256 with a 1 degree FHWM from a combination of the Planck LFI data, WMAP  (Bennett et al. 2013 ) and Haslam  (Haslam et al. 1982)  data. The second, at N side 2048 with a 7. 5 arcmin FHWM, uses only the Planck HFI data (excluding the 100 GHz observations) and only fits for the thermal dust spectral index, setting the thermal dust temperature at each pixel to the values determined from the low frequency fit. As Commander is a Bayesian method, the thermal dust fit makes use of priors: a Gaussian distribution with a mean of 1.
Method The motivation behind premise is the abundance of astrophysical information now available; we choose to make use of astrophysical models for the foreground components and aim to accurately determine these model parameters. premise is a sparsity driven methodology (Parameter Recovery Exploiting Model Informed Sparse Estimates) and although the data are fit in the pixel domain, the filtering and parameter refinement steps take advantage of the sparse nature of thermal dust emission within the wavelet domain. In this section we recap the premise method and detail the preprocessing steps specific to determining thermal dust emission from Planck HFI data. We encourage the interested reader to consult  Irfan & Bobin (2018)  for the mathematical derivation of the premise algorithm. premise has three main steps: -Filtering -first we filter the data to suppress the CIBA and instrumental noise.
We employ the GNILC technique of using a covariance matrix formed from CIB and instrumental noise estimates but add to this method by exploiting the sparsity of thermal dust emission in the wavelet domain. As the covariance matrix is calculated for the N frequency by N pixel CIB plus instrumental noise matrix any correlations across frequencies within the noise and CIB simulations are represented. -Super-pixel fit -then we avoid the computational cost of completing a pixel-by-pixel MBB fit to the filtered data by fitting to super-pixels. The unique aspect of this step being that the super-pixel areas are selected by the premise algorithm itself to ensure that the regions where the MBB parameters vary the slowest are given the largest areas to average over. An initial super-pixel area of 128X128 pixels (roughly four by four degrees) is set in order to provide the first fit to the MBB model; this "first pass" provides the algorithm with a map of reduced chi-squared values which it uses to select the final super-pixel areas.
-Parameter refinement -lastly we determine the optimum parameter values per original resolution pixel. Using the super-pixel, parameter maps as initial guesses we evoke a gradient descent at each pixel to minimise the least squares estimator. Once the gradient descent converges, we threshold the parameters in the wavelet domain to prevent nonsparse, noise terms from contributing to the final parameter estimates. We ensure MBB parameter maps at the original map resolution by using the empirical data, i. e. the total intensity maps containing thermal dust, CIB and instrumental noise, as the observational data in our gradient descent. The filtered maps are only used within the Super-pixel fit step. A flow diagram summarising these three main steps is presented in Fig. 1 ; as both the filtering and the super-pixel fit steps are required to obtain the initial MBB parameter estimates they are combined within the 'Parameter Initialisation' block of the method flow diagram.
2) 2. The raw data and nuisance estimates are divided into overlapping super-pixels of area 8X8 pixels with an overlapping ratio of 0. 5. We differ from GNILC in this respect as they choose instead to smooth the data and nuisance estimates. 3. The N obs by N obs nuisance covariance matrix is calculated as: R nus = 1 Npix N × N T (2) 4. The covariance matrices of the binned total intensity maps are calculated for each bin: R tot = 1 bin area X × X T and then whitened: R -1/2 nus R tot R -1/2 nus. 5. The eigenvectors of the whitened R tot are calculated for each bin and ordered. 6. The Marcenko-Pastur distribution is used to select eigenvalues which deviate significantly from unity. Here we again differ from GNILC, which uses the Akaike Information Criterion to select the eigenvalues which represent the signal sub-space.
7. The selected eigenvectors (U s ) give the mixing matrix (F = R 1/2 nus U s ) which can be used to obtain the least-squares optimisation of thermal dust emission. We, however choose to add an additional factor to the optimisation performed by GNILC. We recapture thermal dust information lost through binning by adding a penalisation factor, to favour sparsity, into the least-squares minimisation. This penalisation factor takes the form of discrepancies between the raw data and binned dust signal (∆) which are sparse in the wavelet domain. The L2,1-norm is used to identify which discrepancies are sparse: k i ∆ ν i [k] 2 , where k is the pixel number. 8. It should be noted that a user-defined threshold value is required to identify significant wavelet coefficients, i. e. those which are high enough above the noise level to be considered signal.
Temperature and Spectral Index The super-pixel fit produces initial estimates of the MBB temperature and spectral index. As these initial estimates are well informed we can use them to seed a gradient descent algorithm to determine the optimum temperature and spectral index per pixel. First we calculate an initial guess for optical depth at 353 GHz by minimising the reduced χ 2 at each 5 arcmin pixel (k) between the total intensity (thermal dust plus CIB plus instrumental noise) data and our spectral model of thermal dust emission (S ). Our spectral model is formed using the quadtree estimates for temperature and spectral index produced by the super-pixel fit of the algorithm. We weight the data according to our CIBA plus instrumental noise estimates (n): τ 0 353 [k] = ν (S [k]X[k]) /n 2 [k] ν (S [k]S [k]) /n 2 [k] , (3) where X[k] is a vector containing the total intensity measurement at pixel k at each frequency and S [k] = B(T [k], ν) × ν 353 GHz β[k] × 10 20 × CC -1 [k], (4) where CC -1 are the inverse colour corrections for the four bandpasses and the factor of 10 20 is the conversion factor from power to MJy.
After the gradient descent step we now have two all-sky maps, one of temperature and one of spectral index. These maps are at the full Planck resolutions of 5 arcmin as the gradient descent step uses the total intensity (thermal dust plus CIB plus instrumental noise) data, not the filtered data from section 3. 2. Using the spherical wavelet transform we soft threshold these maps in the wavelet domain (leaving the coarse scale untouched) to remove the effect of any non-sparse elements in the total intensity maps. The threshold values are calculated at each of the five wavelet scales as twice the mean absolute deviation (MAD) of the temperature/spectral index. As variance cannot be calculated per pixel we divide the data into patches of 32X32 pixels for the MAD calculation. Thresholding removes non-sparse information from the wavelet scales and so if, for a particular region and angular scale, the signal is completely subdominant to the noise then we loose the signal for that region at that resolution.
We also exclude information from the first wavelet scale as this corresponds to individual pixel variations, so changes in temperature and spectral index across scales smaller than the Planck beam FWHM. These spurious variations are produced by the inclusion of the CIBA in the total intensity data used for the gradient descent. We apply a total point source mask, made from the multiplication of the 353, 545 and 857 GHz Planck HFI masks, to the residual matrix ((X -Model)) in order to prevent the point sources from contributing to the final temperature and spectral index estimates. Therefore, temperature and spectral index values within the total point source mask remain at the initial, super-pixel estimate. The process of gradient descent followed by thresholding within the wavelet domain is then repeated to ensure a robust solution.
Optical depth at 353 GHz The MBB parameters are heavily degenerate when fit in the presence of CIBA and noise. Both Planck Collaboration (2014b) and Planck Collaboration (2016c) try to combat this degeneracy through smoothing. The premise super-pixel MBB fit is specialised to select super-pixels sizes based on the spatial variation of the dust temperature and spectral index, as of such the same method is less responsive to the overall normalisation factor (the optical depth). To break the parameter degeneracies we return to the total intensity (thermal dust plus CIB plus instrumental noise) data for additional information. We calculate our optical depth estimate at 5 arcmin using Eq. (3) but this time our spectral model is formed using the 5 arcmin estimates for temperature and spectral index produced by the refinement step of the algorithm.
Results We aim to assess our thermal dust MBB parameters through comparisons with existing thermal dust templates and external data sets. Within this section we shall refer to the results obtained by Planck Collaboration (2014b) as '2013'; whilst the method dates from this time the results themselves have been reproduced here using PR2 data and it is these updated results which we use in this work. 4. 1. Temperature and Spectral Index: spatial distributions Fig. 3  shows the all-sky MBB temperature and spectral index for thermal dust emission as calculated by premise, 2013 and GNILC. The differences between the parameter estimates are given in Fig. 4. Visually, for both the temperature and spectral index maps, the premise and 2013 maps look the most similar, differing from the GNILC temperature and spectral index maps noticeably at high latitudes.
ACKNOWLEDGEMENTS We acknowledge stimulating discussions with  Emilia Witkowska ,  Sandro Stringari  and  Gabriele Ferrari. This work has been supported by  ERC  ( Synergy Grant  UQUAM). K. J. G. acknowledges funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under the  Marie Sklodowska-Curie Grant  Agreement No. 701894. LKB is a member of the  SIRTEQ network of Région Ile-de-France.
Acknowledgments. We wish to thank  Stephen Angus ,  Kanghoon Lee  for useful comments, and  Wonyoung Cho  for valuable help at the early stage of the project. This work was supported by the  National Research Foundation of Korea  through the Grants,  NRF-2016R1D1A1B01015196  and  NRF-2018H1D3A1A01030137  ( Korea Research Fellowship Program ).
Different types of charged hadrons are distinguished using information from two ring-imaging Cherenkov (RICH) detectors  [10]. Photons, electrons and hadrons are identified by a calorimeter system consisting of scintillating-pad and preshower detectors, an electromagnetic and a hadronic calorimeter. Muons are identified by a system composed of alternating layers of iron and multiwire proportional chambers  [11]. The magnetic field deflects oppositely charged particles in opposite directions which can lead to charge-dependent acceptance effects. The configuration with the magnetic field pointing upwards (downwards) bends positively (negatively) charged particles in the horizontal plane towards the centre of the LHC ring. Periodically reversing the magnetic field polarity throughout the data taking sufficiently cancels these acceptance effects for the precision of this measurement.
For the B + → J/ψ K + channel, this selection results in a good signal purity, and no further selection criteria are needed. Two vetoes are applied for the B + → J/ψ ρ + channel to reject B + → J/ψ K + and B + → J/ψ π + decays combined with a random π 0 : the mass of the combination of the J/ψ and the charged pion candidate, evaluated under both the pion and kaon mass hypotheses, must be outside a 50 MeV/c 2 window around the known B + mass. In addition to the preselection discussed above, a multivariate selection is performed on the B + → J/ψ ρ + channel using an artificial neural network from the TMVA package  [22, 23] , mainly to reduce combinatorial background. The 12 input variables are as follows: the flight distance and direction angle of the B + candidate, defined as the angle between its momentum and the vector connecting its primary and decay vertices; the transverse momentum of the B + and ρ + candidates; the maximum transverse momentum of the two muons, the χ 2 IP of the charged pion; the minimum χ 2 IP of the two muons; the quality of the B + candidate vertex; the change in the vertex quality when adding the closest track that is not part of the signal candidate; the quality of a kinematic fit of the full decay chain; the quality of the π 0 identification; and the p T asymmetry in a cone around the flight direction of the B + meson, defined as ( i p T ij p T j )/( i p T i + j p T j ), where i runs over all final state tracks of B + → J/ψ ρ + and j over all other tracks in a cone around the B + meson flight direction.
For all variables good agreement is found. The neural network selection criterion is optimized by maximizing the figure of merit N sig / N sig + N bg , where N sig is the expected number of signal decays and N bg is the estimated background yield, both between 5200 MeV/c 2 and 5450 MeV/c 2. The value of N sig is calculated using the ratio of the previously measured B + → J/ψ ρ + and B + → J/ψ K + branching fractions  [14] , the observed number of B + → J/ψ K + decays, and the efficiencies of the B + → J/ψ ρ + and B + → J/ψ K + channels. The value of N bg is obtained by extrapolating the shape of the background into the signal region. The optimized cut rejects 99. 4% of background events in both, the 2011 and 2012 data samples, while retaining 49% (45%) of signal events in the 2011 (2012) data samples.
The fraction of B + → J/ψ π + π 0 decays that do not proceed via the ρ + resonance is measured to be (8. 4 +6. 1 -6. 2 )% in the m π + γγ interval from 400 MeV/c 2 to 1100 MeV/c 2. ) Data Sum B + → J/ψ ρ + B + → J/ψ π + π 0 (non res) Combinatorial B + → J/ψ K * + part reco B 0 s → J/ψ φ part reco B + → J/ψ K * + mis ID ) Data Sum B + → J/ψ ρ + B + → J/ψ π + π 0 (non res) Combinatorial B + → J/ψ K * + part reco B 0 s → J/ψ φ part reco B + → J/ψ K * + mis ID LHCb 2012 The value of A CP is calculated using A CP = A CP raw -A prod , (5) where A CP raw is the raw asymmetry, determined from a fit to the 2011 and 2012 data sets, split by the charge of the B meson, where all fit components are modelled as in the branching fraction measurement; and A prod is the production asymmetry of B + mesons in LHCb.
The shape of the angular acceptance is taken from the full simulation of B + → J/ψ ρ + decays. Each sample is fitted with the baseline description, which does not include the interference term. The largest relative difference in the signal yield with respect to the generated value is taken as a systematic uncertainty. To investigate possible exotic resonance contributions, the invariant masses of J/ψ π + and J/ψ π 0 are inspected and no excess compared to the expectation is found. Given the nature of the systematic uncertainties on the π 0 and charged-particle reconstruction efficiencies, the selection efficiency and the removal of multiple candidates, their correlation in 2011 and 2012 is set to 1. All other uncertainties either result from a common fit to the combined data sets of 2011 and 2012 or are treated as uncorrelated.
Acknowledgments We thank  E. Kiritsis  for valuable discussions and suggestions. GP would like to thank  M. Jarvinen  and  F. Nitti  for giving useful suggestions, and the  Steklov Mathematical Institute  for hospitality during the initial stages of this work. AG thanks  Gleb Arutyunov ,  Eoin Ó Colgáin ,  Bum-Hoon Lee ,  Chanyong Park  and  Sunyoung Shin  for useful conversations. AG is also very grateful for the warm hospitality  DESY Theory Group, APCTP (Pohang)  and  Sogang University (Seoul).
After purification, the 3 He, 4 He, 20 Ne, 21 Ne and 22 Ne were sequentially analyzed using the Noblesse mass spectrometer (Nu instruments) located at the Institut de Physique du Globe de Paris (IPGP), France, following the method described in  (Chavrit et al. , 2016). Together with an air standard for Ne, the isotopic compositions of the homemade gas standard used to calibrate the data are the following: 3 He/ 4 He=7. 73 RA, 20 Ne/ 22 Ne=9. 94, 21 Ne/ 22 Ne=0. 0309. On average, the furnace blank values, made at a temperature of 1200°C, were 6×10 -9 cm 3 STP for 4 He and 3. 6×10 -12 cm 3 STP for 22 Ne. Seven duplicates were analyzed in order to assess the reproducibility of the measurements. Duplicates of each sample were all from the same residual fraction which was split into several.
If the signal came exclusively from fluorapatite, this mineral would need to have an abundance of 0. 3-16%. To evaluate the presence of F in several of the samples, we performed SEM-EDS elemental mapping for some of the residual fractions of the samples (noncarbonate fractions) including sample 3404 (See Annex 3 of the supplementary material for the details on the SEM measurements). The maps show some spots with a F-enrichment (Figure  6 ). It is not possible to get reliable quantification of the data because the samples were powders that were consequently not flat and Au-coated. Nevertheless, the decarbonated fraction of sample 3404 is the one which seems to visually contain the most abundant F-bearing phases amongst the 5 analyzed samples (Figure  6 ), particularly compared to sample 3428 which has a similar amount of non-carbonate fraction.
Acknowledgments We thank the  Oman Ministry of Oil and Gas for permission  to publish this paper. We thank Stephan Borensztajn  for assistance during with the SEM measurements and  Cécile Gautheron  for providing the Durango fluorapatite. FM thanks the  European Research Council  under the European Community's  H2020 framework program / ERC  grant agreement #  637503  (Pristine), the  Agence Nationale de la Recherche  for a chaire d'Excellence Sorbonne Paris Cité (IDEX13C445) and the  Plateform PARI. FM and MM thank the  UnivEarthS Labex program  ( ANR-10-LABX-0023  and  ANR-11-IDEX-0005-02 ). P. Sarda ,  J. Hopp  and the two other anonymous reviewers are thanked for their constructive reviews. This is IPGP contribution number  4027.