dataesr/scientific-paragraphs-categorization huggingface dataset:
The VUDS sample The VIMOS Ultra Deep Survey  (VUDS, Le Fèvre et al. 2015 ) is a spectroscopic redshift survey devised to study galaxy evolution in the high redshift Universe, taking advantage of the multiplexing capabilities of VIMOS on VLT. This analysis is based on a subsample of the 7,843 galaxy redshifts measured in three separate fields (COSMOS, VVDS-02h and ECDFS) where ample photometric data are also available. As reported in  Tasca et al. (2015)  each of the fields has multiband photometry covering at least from broad band u to Spitzer-IRAC 4. 5 µm. The COSMOS field has the most extensive photometric set composed by more than 30 bands including standard broad band and medium band photometry  (Laigle et al. 2016). The broad band photometric databases of VVDS-02h and ECDFS have been presented respectively in Le  Fèvre et al.
Galaxy models The SED fitting is performed using a large database of dusty model galaxies (about 30,000 evolutionary models) obtained varying the physical input parameters. The galactic models have been calculated starting from the extended library of isochrones and SSPs of different chemical compositions and ages. The definition of age throughout the paper means the time passed since  the first stars started forming in a galaxy. In the models we take into account a state-of-the-art treatment of the TP-AGB phase and including the effect of circumstellar dust shells around AGB stars  (Cassarà et al. 2013). Even if the AGB phase is short lived, the AGB stars are very bright, can reach very low effective temperatures and easily get enshrouded in a shell of self-produced dust, which reprocesses the radiation from the underneath stars.
(1994) ;  Tantalo et al. (1996) ;  Portinari & Chiosi (1999) ;  Piovan et al. (2011) ;  Cassarà (2012) ;  Cassarà et al. (2015)  for an extensive discussion on this topic). Following the suggestion of  Tantalo et al. (1996) ;  Portinari et al. (2004) ;  Cassarà (2012)  we adopt three values for ζ for our models, namely 0. 35, 0. 39 and 0. 50. The galaxy models have been calculated at varying the possible values of the optical depth τ V , and hence of the database of dusty SSPs. It is worth underlining the relation between τ V and A v : A v =1. 086 × τ V (more details in Appendix A. 1).
SED fitting tool and procedure We make use of GOSSIP+ (Galaxy Observed-Simulated SED Interactive Program), a spectral/SED fitting software. It has been developed by the PANDORA group at INAF-IASF Milano  (Franzetti et al. 2008 ) and partially modified and completed at LAM in Marseille. The full description of the new version of the sofware is presented in  Thomas et al. (2016) : for the sake of clarity, here we make a short summary of its capabilities. GOS-SIP+ is a software that is able to combine the spectroscopic information with the photometric data points. One major improvement implemented in GOSSIP+ deals with the IGM attenuation. Most current SED fitting softwares use the Madau's prescription  (Madau 1995). This means that for a given redshift a single extinction curve is considered, while GOSSIP+ allows to choose among seven IGM extinction curves at any redshift.
In order to account for the vastly different number of spectral data points with respect to the photometric data points, a combined χ 2 is computed by GOSSIP+ as the sum of the reduced χ 2 of the photometric and of the spectrum data fit, in order to evaluate the A&A proofs: manuscript no. Cassara2016_astroph two different data sets with the same weight. Being the sum of two reduced χ 2 we adopt as a definition of a total reduced χ 2 the following: χ 2 red = 0. 5(χ 2 phot + χ 2 spec ). As for the prominent emission lines in the spectra, GOSSIP+ gives the possibility to define some avoidance regions of the spectra (which are then redshifted to each object redshift) which will be avoided in the fitting procedure. Stellar masses, SFRs, dust absorption, age and metallicity of the stellar populations, UV luminosity and level of IGM extinction are obtained simultaneously employing a χ 2 minimization to find the best-fit model considering the age of the Universe at the ob- served redshift as an upper limit for the choice of the models.
The agreement between spectroscopy and photometry is crucial to produce a good fit. As discussed in  Thomas et al. (2016) , it can happen that there is a mismatch between these two datasets and this affects the quality of fit. In Thomas et al. (  2016 ) a visual inspection was performed in order to define the quality of the fit. We decided to select the final sample of galaxies in the following way: as a first step, we excluded galaxies with values of the total reduced χ 2 red >5, and as a second step, we excluded galaxies with values of reduced photometric χ 2 phot >5. Finally, a visual inspection of the excluded galaxies confirmed that in this way we retain only galaxies with a reliable fit. High values of χ 2 are almost always due to a very reduced set of photometric data and/or a spectrum with low S/N, and thus the physical properties that can be inferred could be highly uncertain.
g. PAHs and very small graphite grains), influencing the MIR emission in particular; -Ion: the ionization model of PAHs; τ V : the optical depth of the cloud at a fixed wavelength (for example, in the V-band). The key parameter is the optical depth of the MCs. Its effect on the spectrum emitted by the cloud is that the higher the optical depth, the greater amount of energy is shifted toward longer wavelengths. The amount of IR re-emitted luminosity first quickly increases at increasing optical depth, then becomes less sensitive to τ V and tends to flatten out for high τ V  (Piovan et al. 2006a). The database of dusty SSPs has been calculated considering: 1. Z: five values of metallicity and 40 values of age for each metallicity; 2. R: scale radius of the cloud normalized to the SSP mass. R links the mass of the sources of radiation to the dimension of the cloud and its effect is to change the position of the FIR peak due to dust emission.
Acknowledgements. The authors would like to thank the anonymous referee whose criticism has helped to improve the quality of the manuscript. DM gratefully acknowledges LAM hospitality during the initial phases of the project. LPC acknowledges the developers of the software TOPCAT (http://www. starlink. ac. uk/topcat/). LPC thanks  Paolo Franzetti  for his help and invaluable discussions. LPC acknowledges  EU   FP7  funded project  DustPedia. DustPedia  is a collaborative focused research project supported by the  European Union  under the  Seventh Framework Programme  (2007-2013) call (proposal no. 606847 ), with participating institutions:  Cardiff University, UK ;  National Observatory of Athens, Greece ;  Ghent University, Belgium ;  UniversiteParis Sud , France;  National Institute for Astrophysics, Italy and CEA (Paris), France.
A technique for estimating the age-depth relationship in an ice core and evaluating its uncertainty is presented. The age-depth relationship is determined by the accumulation of snow at the site of the ice core and the thinning process as a result of the deformation of ice layers. However, since neither the accumulation rate nor the thinning process is fully known, it is essential to incorporate observational information into a model that describes the accumulation and thinning processes. In the proposed technique, the age as a function of depth is estimated by making use of age markers and δ 18 O data. The age markers provide reliable age information at several depths. The data of δ 18 O are used as a proxy of the temperature for estimating the accumulation rate. The estimation is achieved using the particle Markov chain Monte Carlo (PMCMC) method, which is a combination of the sequential Monte Carlo (SMC) method and the Markov chain Monte Carlo method.
In this hybrid method, the posterior distributions for the parameters in the models for the accumulation and thinning process are computed using the Metropolis method, in which the likelihood is obtained with the SMC method, and the posterior distribution for the age as a function of depth is obtained by collecting the samples generated by the SMC method with Metropolis iterations. The use of this PMCMC method enables us to estimate the age-depth relationship without assuming either linearity or Gaussianity. The performance of the proposed technique is demonstrated by applying it to ice core data from Dome Fuji in Antarctica.
1. Since the vertical profile of the age ξ is associated with the profile of A, the information from the δ 18 O data is also effective for improving the estimate of the age ξ. At several depths, we can also use reliable age values given by age markers. We used such age values as tie points when estimating the age-depth relationship. The age, depth, and uncertainty (2σ ) for each tie point used in the paper are shown in Table  1. The first point was determined from the Antarctic Cold Reversal to Holocene transition and the second one was determined from the beryllium 10 peak. These two points were given by  Parrenin et al. (2007). The subsequent 23 points were determined from the relationship between O 2 / N 2 and the summer insolation  (Kawamura et al. , 2007). Both the δ 18 O data and the tie points were considered when estimating the age ξ.
We applied this PMCMC method to the data of the ice core at Dome Fuji. The estimates of the age-depth relationship and the parameters were successfully obtained. for the result without using the last five age markers. The difference of each tie point from the median of the posterior (black crosses) and the difference between the estimate with all the tie points and the estimate without using the last five tie points (grey line) are also shown. The main advantage of the proposed technique is that it can be applied to general nonlinear non-Gaussian situations. Since the relationship between accumulation rate and a temperature proxy is typically nonlinear, it is not necessarily justified to assume linearity and Gaussianity when using a temperature proxy to date an ice core. The PMCMC method allows us to use various kinds of data that are expected to have a nonlinear relationship with the model variables.
We report on an exceptional large-scale coronal pseudostreamer/cavity system in the southern polar region of the solar corona that was visible for approximately a year starting in February 2014. It is unusual to see such a large closed-field structure embedded within the open polar coronal hole. We investigate this structure to document its formation, evolution and eventually its shrinking process using data from both the PROBA2/SWAP and SDO/AIA EUV imagers. In particular, we used EUV tomography to find the overall shape and internal structure of the pseudostreamer and to determine its 3D temperature and density structure using DEM analysis. We found that the cavity temperature is extremely stable with time and is essentially at a similar or slightly hotter temperature than the surrounding pseudostreamer.
Solar Rotational Tomography The first step in our analysis is to estimate the three-dimensional distribution of the coronal plasma emissivity. We used the SRT software TomograPy, described in  Barbey et al. (2013) , an opensource program freely available in the Python Package Index 1. The software performs fast tomographic inversions using one of several parallelized-projection algorithms. Various types of solar image data can be used as input including multiple UV wavelengths and white-light observations. In the case of optically thin plasma, the intensity I b , produced by collisional emission lines and continua in a given UV or EUV waveband b, integrated along the LOS l, can be expressed as I b = ∞ -∞ R b (n e , T e )n 2 e dl (1) where R b is the response function to a unit volume of plasma of electron temperature T e and density n e of the instrument.
e. , the volume element of the 3D reconstruction grid, and n j is the noise associated with the image pixel j. Equation (3) can then be rewritten in matrix notation I = P e + n (4) where I contains all the pixels of every image of size N and e the reconstruction cube of size M. The projection matrix P, called the projector in TomograPy, takes into account the position and the orientation of the spacecraft for each pixel of each image used in the reconstruction. To compute the exact path of each LOS through the various voxels, the software uses a parallelized implementation of the Siddon algorithm  (Siddon, 1985)  in C. Designed for Cartesian grid, this algorithm computes the projection or back-projection operations very quickly, and the huge projection matrix of size M × N does not need to be stored in memory.
(2012a). For each waveband the synthetic emissivities e synth b are calculated through e synth b (T c , n e , σ ) = n 2 e ∞ 0 R b (T e , n e ) N (log T e -log T c ) d log T e = n 2 e (R b * N )(T c , σ ). ( 12 ) We note that the synthetic emissivities are equal to the convolution product of the temperature response function by the Gaussian local DEM. They correspond to the expected emissivity values for all possible combinations of the three local DEM parameters n e , T c , and σ. The response temperature R b (T e ) of the six AIA coronal bands have been computed using version 7. 1 of the CHIANTI atomic database  (Dere et al. , 1997; Landi et al. , 2013). The temperature varies from log T e = 5 to log T e = 7 in steps of 0. 0025 log T e , whereas the density covers the range from 10 7 to 10 10 cm -3 in steps of 0.
01log n e. The local DEM width varies linearly in 80 steps from σ = 0 to σ = 0. 8 log T e. This choice of sampling leads to pre-computed emissivity cubes with ∼ 2 × 10 7 elements for each AIA band, which are easily manageable data cubes. The final result of the our analysis gives us the emissivity in each bandpass for each voxel location, as well as the local DEM obtained from these emissivities. From the local DEM analysis, we extract the temperature (i. e. , the central temperature of the Gaussian local DEM), the density, and the Gaussian local DEM width, which corresponds the thermal width of the plasma, for each voxel. Thus, in conjunction with the time analysis described in the next section, we can track the evolution of both morphological and thermal properties of our observed large-scale polar pseudostreamer.
OBSERVATIONS The large scale pseudostreamer that is the subject of this paper lasted around 1 year, beginning in February 2014. We used the  SWAP 174 Å and AIA 94, 131, 171, 193, 211, 335  Å UV coronal images to follow its evolution and produce the tomographic and local DEM inversions. To observe the formation process of this long-lived pseudostreamer, the SWAP data time series begins on 2013 December 19, about 2 months before its formation. We tracked its evolution until 2015 March 31. For AIA, we used observations from 2014 January 1 until 2015 February 28. In March 2015 two gaps in the AIA data prevent us from computing accurate tomographic reconstructions: from March 1 to March 5 and from March 8 to March 14. The SWAP data were fully calibrated using the SolarSoft IDL p2sc_prep routine which removes dark current, corrects for the flat field, deconvolves the point spread function, and corrects the image so that the Sun is round, centered, and solar north is up.
To create high signal-to-noise images, 100 min of data were median-stacked to create a single frame for input into TomograPy. The processing steps and the median-stacking of the images was described in more detail by  Halain et al. (2013)  and  Seaton et al. (2013). The median-stacking suppresses shorttime-scale dynamics of the Sun, which improves the tomographic inversion accuracy. For each tomographic inversion, 17 days of data at a cadence of 100 min (totalling ∼240 median-stacked frames) were used. To examine the thermal distribution of the corona, we used AIA images from the six coronal waveband channels. For each waveband, the data were first rotated, translated,scaled and normalized, using a Python function equivalent to the SSW routine aia_prep. pro. Then, 10 images (1 frame per minute) were averaged together to create a single frame with high signalto-noise ratio.
05 R ⊙. This was done by tracing the latitudinal emissivity profile in our data cubes for the longitude corresponding to the central meridian of the time centered image of the SWAP data used. The central position of the cavity was chosen to be the local minimum of the profile, closest to the emissivity peak associated with the pseudostreamer. In both Figures  3, 4  (see also the movie online), a part of the projections, in the longitude area opposite to the marked cavity position, is smoother and presents lower emissivity. This artifact, which is more pronounced at higher altitudes, is due to the fact that we used 17 days of data (approximately half a solar rotation), as opposed to a full rotation. In the Siddon algorithm, the LOS are stopped once they hit the photosphere, and therefore some parts of the pole are poorly constrained in the reconstruction process.
Using simulations, we determined that at 1. 10 R ⊙ , they produce an additional error around 10-15 % in the reconstructions. This is consistent with the increase of the σ/µ ratio of about 15% in average observed on the bottom right panel of Figure  8. Radial properties of both the cavity and streamer are presented in Figure  9. The top and bottom panels show the temperature and density variations with respect to the altitude for both the cavity (red solid line) and the the pseudostreamer (blue solid line). The measurements are made from the constant longitude corresponding to the central meridian of the central SWAP image of the data series used for the July 14th reconstruction. The cavity center is defined as the local minimum of the latitudinal emissivity profile in the SWAP 174 Å channel (indicated by the black plus sign), while the the pseudostreamer edge is defined as the emissivity peak just equatorward of the cavity.
Unfortunately, in most practical case, the covariance is too big to be kept in memory (see  Barbey et al. , 2013 , for more details). Comparatively, the random errors involved in the SWAP and AIA data are more likely to be smaller than in comparison to the uncertainties associated with the tomographic reconstruction process, and can be neglected. Quantifying how much the time variation of the corona affects the reconstruction is not trivial and is beyond the scope of this paper. However, the standard deviation-to-mean ratio, σ/µ described in Section 5 can give us an estimate of the degree of temporal variation. Inspecting the online movie showing the SWAP polar caps and the associated σ/µ ratio, we can derive an upper limit on the emissivity uncertainty, at least for the steady lifetime of the pseudostreamer system (i.
SUMMARY In this work, we present a full analysis of an exceptional long-lived pseudostreamer/cavity system, observable for almost a year, starting in February 2014. We used SWAP data and 6 AIA coronal wavebands to study its evolution, examining both its 3D morphology and thermodynamical properties. At first, a streamer encircles the entire pole, associated with a polar-crown cavity. By dint of a gradual displacement toward the south pole, a pseudostreamer is formed above the south pole in February 2014, which remains visible for approximately a year. Following that, the pseudostreamer gradually shrink until its disappearance in March 2015, at which point the southern polar coronal hole once again dominates the pole. We used a combination of tomography and DEM analysis to track the evolution of the 3D thermodynamical properties of this unusually long-lived pseudostreamer/cavity system.
For our timeanalysis we used a 1 day sliding-window in time, and then constructed standard deviation-to-mean ratio over successive reconstructions. This allow us to discriminate static structures from the temporal and geometrical artifacts, and ensure that the observed features in our reconstructions are real. To our knowledge, this is the first time that this kind of approach has been used to analyze tomographic reconstructions. Our results are summarized as follows: • The cavity depletion, relative to the surrounding pseudostreamer varies in the range of 20-40% depending on height, • The cavity density depletion decreases with height, from a maximum of about 40% at the cavity lower boundary to ∼0% at the cavity top, • The cavity density slowly decreases with time as the pseudostreamer shrinks, • The volume enclosed within the pseudostreamer is systematically hotter than the surrounding open-field plasma, • The cavity temperature is essentially at similar or slightly higher temperatures than the pseudostreamer, • No cavity hot cores were observed in our dataset, • Two regimes in cavity density and thermal distribution are observable during the pseudostreamer lifetime.
ACKNOWLEDGMENTS CG acknowledges financial support from the  Spanish Ministry of Economy and Competitiveness (MINECO)  under the  2011 Severo Ochoa Program MINECO   SEV-2011-0187. The authors would like to thank the technical staff at the  Computer Science Department of the Institut d'Astrophysique Spatiale, Orsay, France , for allowing us to use their computational facilities. CG would like to thank in particular  Claude Mercier  and Elie soubrié for helpful suggestions in the DEM inversion code parallelization. The Atmospheric Imaging Assembly on the  Solar Dynamics Observatory  is part of  NASA 's  Living with a Star program. CHIANTI is a collaborative project involving the  NRL (USA) , the  Universities of Florence (Italy)  and  Cambridge (UK) , and  George Mason University (USA). LR and DS acknowledge support from the  Belgian Federal Science Policy Office (BELSPO)  through the  ESA-PRODEX program , grant No.
On-ground calibration and measurement setup Figure  2  shows a block diagram of the setup used for the frequency response calibration. All magnetic field sensors of the FIELDS instrument suite were placed in a mu-metal can and the stimulus signal was applied using a current generator and a solenoid coil. AFG and DFG were digitized by the respective front-end electronics, while SCM signals first passed through a preamplifier and were then sampled by the DSP unit. The data streams were then processed by the FIELDS central data processing unit (CDPU). The current generator and the CDPU were synchronized by a common 1 Hz time reference clock. All data recorded within FIELDS were time stamped relative to this reference. Furthermore all instruments within FIELDS were synchronized using common master and synchronization clocks.
Acknowledgements. The dedication and expertise of the Magnetospheric Multiscale (MMS) development and operations teams are greatly appreciated. Work at  JHU/APL ,  UCLA ,  UNH  and SwRI was supported by  NASA  contract number  NNG04EB99C. The French involvement (SCM) on MMS is supported by  CNES  and  CNRS. We acknowledge the use of burst L1A data from digital and analog fluxgate magnetometers and search coil magnetometers. The data are stored at the MMS Science Data Center https://lasp. colorado. edu/mms/sdc/and are publicly available. The Austrian part of the development, operation and calibration of the DFG was financially supported by rolling grant of the  Austrian Academy of Sciences  and the  Austrian Space Applications Programme  with the contract number  FFG/ASAP-844377. Edited by: V.
The rotational spectrum of 3-pentyn-1-ol, CH 3 -C≡C-CH 2 CH 2 OH, was measured using a molecular beam Fourier transform microwave spectrometer operating in the frequency range from 2 to 26. 5 GHz. A two-dimensional potential energy surface was calculated at the MP2/6-311++G(d,p) level of theory for a conformational analysis, yielding five conformers. The most stable conformer exhibits C 1 symmetry and was assigned in the spectrum by comparison with the results from quantum chemical calculations. The barrier to internal rotation of the propynyl methyl group CH 3 -C≡C-was found to be only 9. 4552(94) cm -1. Molecular parameters and internal rotation parameters could be accurately determined using the program xiam and belgi-C 1. The internal rotation barrier was compared with those of other molecules containing a propynyl methyl group.