dataesr/scientific-paragraphs-categorization huggingface dataset:
• Improving constraints on primordial gravitational waves through removal of Galactic foregrounds: By combining sub-millimeter Prime-Cam measurements with longer wavelength data from Simons Observatory we will improve our understanding of polarized dust foregrounds to help searches for primordial gravitational waves. In particular, Prime-Cam measurements have the potential to reduce bias in constraints on the tensor-to-scalar ratio, r, from SO and thereby aid in constraining models of the early Universe such as cosmic inflation. • Constraints on the role of magnetic fields in star formation, MHD turbulence, and dust grain composition as revealed through polarimetric imaging: Prime-Cam will map hundreds of molecular clouds in polarized dust emission at high fidelity on scales ranging from entire clouds down to the scales where individual stellar systems form.
12. ACKNOWLEDGEMENTS The  CCAT -prime project, FYST telescope and Prime-Cam instrument have been supported by generous contributions from  Fred Young, Cornell University , and the  Canada Foundation for Innovation and the Provinces of Ontario, Alberta , and  British Columbia. The construction of the FYST telescope was supported by the  Großgeräte-Programm of the German Science Foundation (Deutsche Forschungsgemeinschaft, DFG)  under grant  INST 216/733-1 FUGG. The construction of  EoR-Spec  is supported by  NSF  grant  AST-2009767. MA acknowledges partial support from  Fondecyt  through grant  1211951. MA has been supported by the grant " CONICYT + PCI + INSTITUTO MAX PLANCK   DE ASTRONOMIA MPG190030 " and " CONICYT+PCI+REDES 190194. " SKC acknowledges support from  NSF  award  AST-2001866.
EK is supported in part by the  Excellence Cluster ORIGINS , which is funded by the  Deutsche Forschungsgemeinschaft (DFG ,  German Research Foundation)  under Germany's  Excellence Strategy  - EXC-2094-390783311. PDM acknowledges support from the  Netherlands organization for scientific research (NWO)   VIDI grant  (dossier  639. 042. 730 ). JM is supported by the  US Department of Energy  under grant no. DE-SC0010129. DR acknowledges support from the  National Science Foundation  under grant numbers  AST-1614213  and  AST-1910107. DR also acknowledges support from the  Alexander von Humboldt Foundation  through a  Humboldt Research Fellowship for Experienced Researchers. ER acknowledges the support of the  Natural Sciences and Engineering Research Council of Canada (NSERC) , funding reference number  RGPIN-2017-03987.
University of Cologne  and University Bonn CCAT-prime members acknowledge support by the  Collaborative Research Centre  956 funded by the  Deutsche Forschungsgemeinschaft (DFG)  -project  ID 184018867. GRS acknowledges support from  NSERC   Discovery Grants  ( RGPIN-2016-06569  and  RGPIN-2021-04001 ). GJS acknowledges support from the  National Science Foundation  under grant numbers  AST-1716229  and  AST-2009767 , and  NASA  grant  NNX16AC72G. AS gratefully acknowledges funding support through  Fondecyt Regular  (project code  1180350 ) and from the  Chilean Centro de Excelencia en Astrofísica y Tecnologías Afines (CATA)   BASAL  grant  AFB-170002. DTC is supported by a  CITA/Dunlap Institute postdoctoral fellowship. The  Dunlap Institute  is funded through an endowment established by the  David Dunlap  family and the  University of Toronto.
Context. Analysis of the Apache Point Observatory Galactic Evolution Experiment project (APOGEE) data suggests the existence of a clear distinction between two sequences of disc stars in the [α/Fe] versus [Fe/H] abundance ratio space, known as the high-and low-α sequence, respectively. This dichotomy also emerges from an analysis of the vertical distribution of the [α/Fe] abundance ratio. Aims. We aim to test whether the revised two-infall chemical evolution models designed to reproduce the low-and high-α sequences in the [α/Fe] versus [Fe/H] ratios in the solar neighbourhood are also capable of predicting the disc bimodality observed in the vertical distribution of [Mg/Fe] in APOGEE DR16 data. Methods. Along with the chemical composition of the simple stellar populations born at different Galactic times predicted by our reference chemical evolution models in the solar vicinity, we provide their maximum vertical height above the Galactic plane |z max | computed assuming the relation between the vertical action and stellar age in APOGEE thin-disc stars.
Result. The vertical distribution of the [Mg/Fe] abundance ratio predicted by the reference chemical evolution models is in agreement with that observed when combining the APOGEE DR16 data (chemical abundances) with the astroNN catalogue (stellar ages, orbital parameters) for stars younger than 8 Gyr (only low-α sequence stars). Including the high-α disc component, the dichotomy in the vertical [Mg/Fe] abundance distribution is reproduced considering the observational cut in the Galactic height of |z| < 2 kpc. However, our model predicts an overly flat (almost constant) growth of the maximum vertical height |z max | quantity as a function of [Mg/Fe] for high-α objects in contrast with the median values from APOGEE data. Possible explanations for such a tension are that: (i) the APOGEE sample with |z| < 2 kpc is more likely than ours to be contaminated by halo stars, causing the median values to be kinematically hotter, and (ii) external perturbations -such as minor mergers -that the Milky Way experienced in the past could have heated up the disc, and the heating of the orbits cannot be modeled by only scattering processes.
This suggests that stars that formed in different epochs shared similar star formation processes and evolution. Mikolaitis et al. (2014)  used the spectra of around 2000 FGK dwarfs and giants from the Gaia-ESO survey iDR1, and found that thick-disc stars show a shallower vertical metallicity gradient than those of the thin disc, an opposite [α/Fe] ratio gradient to that of the thin disc, and positive vertical individual [α/M] and [Al/M] gradients (where M is the metallicity). Duong et al. (2018)  used data from the GALAH survey to determine the vertical properties of the Galactic thin and thick discs near the solar neighbourhood. The median [α/M] increases as a function of height, as noted previously by  Schlesinger et al. (2014)  and  Mikolaitis et al. (2014). However, unlike the metallicity, they find that the α-abundance profile does not vary smoothly with |z|.
45 kpc and one describing the high-α population with scale height z 2 = 0. 95 kpc. In the present article, we compare the vertical structure of the [Mg/Fe] distribution as emerging from APOGEE DR16 data in the solar neighbourhood with the results of revised two-infall chemical evolution models presented by ES20 and ES21. To do so, in addition to the predicted chemical composition of simple stellar populations (SSPs, defined as an assembly of coeval and chemically homogeneous stars) formed at different Galactic times, we provide the respective orbital parameters (i. e. maximum vertical height above the Galactic plane |z max |) computed using the recent relation between the vertical action J z and stellar age found for APOGEE thin-disc stars  (Ting & Rix 2019). The main aim of this work is to investigate whether or not the disc bimodality found in APOGEE DR16 stars in the [α/Fe] versus Galactic vertical height  (Vincenzo et al.
APOGEE DR16 data and the astroNN catalogue In this study, we consider Mg and Fe abundances provided by APOGEE DR16  (Ahumada et al. 2020)  for investigating the region with Galactocentric distances between 6 and 10 kpc as computed by  Leung & Bovy (2019)  and reported in the valueadded astroNN 1 catalogue. We note that astroNN is deeplearning software that was applied to APOGEE DR16 spectra in order to determine stellar parameters, distances  (Leung & Bovy 2019) , and ages  (Mackereth et al. 2019 ). In addition, the abovementioned catalogue includes some of the most important orbital properties for stars (i. e. eccentricities, peri/apocenter radii, maximal disc height |z max |, orbital actions, frequencies, and angles) computed by  Mackereth & Bovy (2018)  assuming the MWPotential2014 gravitational potential from  Bovy (2015).
In the calculations, we consider SSPs formed in constant age intervals ∆τ fixed at the value of 0. 005 Gyr (the total number of SSPs is 2740), which is identical to the time-step of the chemical evolution model. Recalling the Ting & Rix (2019) relation introduced in Sect. 4. 1 and the associated discussion, the value of conserved vertical action J z for a SSP with age τ = t Gt B can be estimated using Eq. (  3 ), where t G is the age of the Galaxy. We integrate stellar orbits for different SSPs(τ) using the Galpy 3  package subject to the MWPotential2014 gravitational potential  (Bovy 2015). For the rotational velocity v T at the solar distance R , we use the one computed by  Ablimit et al. (2020) , applying the three-dimensional velocity vector method for Cepheids (v T = 232. 5 ± 0. 83 km s -1 ) that is consistent with the most recent estimation by  Nitschai et al.
ES20 model In this section, we compare the combined APOGEE data + astroNN catalogue with model predictions based on the chemical evolution model M2 of ES20. Figure  10  shows model results with the  Ting & Rix (2019)  relation extended to high-α sequence stars in computing the maximum height |z max |. As for the ES21 model (see Sect. 5. 1), better agreement with the data is obtained when APOGEE DR16 stars with observed vertical heights |z| < 2 kpc are considered. As pointed out in Sect. 5. 1, there is a discrepancy between the regions in the maximum vertical height versus [Mg/Fe] relation most densely populated by APOGEE DR16 stars and the peaks of the predicted distribution of the stellar mass density found by the ES21 model. On the other hand, in Fig. 10  we note that the distribution of the formed stars predicted by the ES20 model throughout the curve in the |z max | versus [Mg/Fe] space shows no tension with the data.
Acknowledgements. The authors thank the referee  F. Vincenzo  for various suggestions that improved the paper. E. Spitoni  thanks  A. Recio-Blanco  and  P. A. Palicio  for useful discussions. E. Spitoni received funding from the  European Union 's  Horizon 2020 research and innovation program  under  SPACE-H2020  grant agreement number  101004214  ( EXPLORE  project). E. Spitoni acknowledges support from the  ERC   Consolidator Grant  ( Hungary) programme  (Project  RADIOSTAR , G. A. n. 724560 ). Funding for the  Stellar Astrophysics Centre  is provided by The  Danish National Research Foundation  (Grant agreement no. :  DNRF106 ). E. Spitoni  and  V. Aguirre Børsen-Koch  acknowledge support from the  Independent Research Fund Denmark  ( Research  grant  7027-00096B ). This work was partially supported by the program  Unidad de Excelencia María de Maeztu   CEX2020-001058- M.
I. INTRODUCTION Nuclear level densities (NLDs) and photon strength functions (PSFs), also called γ -ray or radiation strength functions, represent average properties of the nucleus in the regime of excitation where individual levels and transition probabilities by γ decay are not readily accessible by experimental or theoretical means. They are key ingredients for statistical calculations of the reaction cross sections involving γ rays via the Hauser-Feshbach approach  [1] , like inelastic scattering or neutron capture reactions. These cross sections are important for nuclear network calculations, for example in stellar nucleosynthesis models  [2, 3]  or in nuclear technology applications, usually relying on evaluated nuclear data libraries  [4, 5]  or astrophysical databases  [6]. Individual levels can be observed and their properties (spin, parity, widths, and eventually decay paths) determined almost exclusively only in two regions: at the lowest excitation energies and in the neutron resonance region just above the neutron separation energy S n.
The DANCE data  [11]  from the Los Alamos Neutron Science Center indicated a lower limit of 11μ 2  N for odd uranium isotopes. The calculation therein showed that the presence of a SC with a strength of ≈15μ 2 N is responsible for about half of the total neutron capture cross section. In the present paper we explore data from the 4π total absorption calorimeter (TAC) of the n_TOF facility at CERN for three uranium isotopes. Data for these nuclei have been taken previously and analyzed in terms of neutron capture cross sections for 234 U  [22, 23] , 236 U  [24, 25] , and 238 U  [26, 27]. Most of the used experimental procedures are detailed in those references and we only summarize the main aspects here. We reanalyzed these data and extracted sum-energy and multistep cascade (MSC) spectra. These spectra were then confronted with predictions of statistical model simulations of γ -ray cascades based on several models for NLDs and PSFs.
B. Data processing The signals from each individual BaF 2 crystal were sampled using digitizers at a rate of 5e8 samples/s. The signal consists of two components with decay times of 0. 7 ns (fast) and 630 ns (slow). The fast and slow components allow the precise determination of the arrival time and the deposited energy, respectively. The ratio of their amplitudes then allows the discrimination of γ rays from background α particles caused by the natural radioactivity of the Ra contaminant in the BaF 2 crystals  [22, 33]. In the analysis we considered only signals corresponding to a deposited energy above a 250 keV threshold, which is slightly above the hardware one. The γ -ray signals within a coincidence window of 20 ns were considered to belong to the same TAC event. Each TAC event is characterized by (i) a corresponding neutron energy determined by the time-of-flight technique from the time of signal arrival, (ii) a number of firing crystals called crystal multiplicity m, and (iii) energies deposited in individual crystals, E i.
A. Algorithms Individual γ -ray cascades were simulated within the statistical approach utilizing the Monte-Carlo DICEBOX code  [38]. The fluctuations between artificial nuclei as generated by DICEBOX are depicted by the color ±1σ bands when comparing the simulated spectra to their experimental counterparts. We simulated 20 artificial nuclei with 10 5 cascades within each. Many different combinations of models of NLD and E 1, M1, and E 2 PSFs were used. As the description of γ decay within the statistical approach is surely inadequate at the lowest excitation energies, the information on discrete levels and their decay was taken from the ENSDF database  [39] [40] [41]  up to excitation energies of 820, 760, and 830 keV for the compound nuclei 235 U, 237 U, and 239 U respectively. The TAC response to these cascades was obtained using the code based on the GEANT4 package  [42].
B. Tested models There are many models of PSFs and NLD available in the literature; see, e. g. , reviews  [20, 21, 44]. We decided to check our spectra against predictions based on a few of them which are either proposed in the recent reviews or describe relevant experimental data, namely, (i) one of the widely used model combinations available in the RIPL-3 database  [21]  consisting of the analytical generalized Lorentzian (GLO) model for E 1 PSF in combination with the spin-flip (SF) Lorentzian for M1 PSF coupled with the constant-temperature (CT) NLD model  [45] , hereafter called RIPL-3, (ii) the PSF models proposed in the recent review  [20]  based on microscopic calculations combined with some phenomenological parts used in conjunction with microscopically Hartree-Fock-Bogoliubov (HFB) based plus combinatorial NLD  [46] , hereafter called IAEA-19, (iii) the original model interpretation of the Oslo PSF  [7]  and NLD  [47] , based on the (d, t ) 237 U and (d, p) 239 U data, hereafter called Oslo, consisting of the enhanced generalized Lorentzian (EGLO) E 1 model, the Lorentzian SC and SF M1 modes, and the CT NLD model from Ref.
[21]PSF contained an additional Lorentzian at 7. 3 MeV with width of 2 MeV and maximum cross section of 15 mb. b235,237U. The value was taken from systematics in the RIPL-3 database[21]and was not adjusted. 21] 11. 11 1. 12 243. 3 13. 41 4. 98 426 6. 61 4. 00 2. 35 Oslo a , b [7] 11. 40 4. 20 572 14. 40 4. 20 1040 2. 15 0. 80 0. 45 2. 90 0. 60 0. 40 6. 61 4. 00 7. 00 Oslo a , c [7] 11. 40 4. 20 572 14. 40 4. 20 1040 2. 00 0. 80 0. 40 2. 80 1. 20 0. 30 6. 61 4. 00 7. 00 DANCE [11] 11. 28 2. 48 325 13. 73 4. 25 384 2. 15 0. 80 0. 60 2. 90 0. 60 0. 53 6. 61 4. 00 1. 50 MGLO(1. 8) 10. 90 2. 30 358. 0 13. 96 4. 75 459. 0 2. 15 0. 80 0. 98 2. 90 0. 60 0. 82 6. 61 d 4. 00 d 3. 05 a c239 U. d.
We would like to acknowledge the financial support of CAMPUS-FRANCE and the French Embassy in Tunisia (Dr. Pierre Durand de Ramefort) for the SSHN grant, of the POLYAM project, of the High Ministry of Education and Research in Tunisia for doctoral grant. Region Auvergne Rhone Alpes is acknowledged for the Pack Ambition International Project, EMBAI #246413. CNRS is acknowledged for the IRP NARES. The research leading to these results has received funding from the European Union Horizon 2020 (TUNTWIN) research and innovation program under grant agreement n° 952306. The authors acknowledge the financial support of the EU H2020 WIDESPREAD Program entitled Bionanosens grant agreement # 951887.
Funding: We would like to acknowledge the financial support of CAMPUS-FRANCE and the French Embassy in Tunisia (Dr. Pierre Durand de Ramefort) for the SSHN grant, of the POLYAM project, of the High Ministry of Education and Research in Tunisia for doctoral grant. Region Auvergne Rhone Alpes is acknowledged for the Pack Ambition International Project, EMBAI #246413. CNRS is acknowledged for the IRP NARES. The research leading to these results has received funding from the European Union Horizon 2020 (TUNTWIN) research and innovation program under grant agreement n° 952306. The authors acknowledge the financial support of the EU H2020 WIDESPREAD Program entitled Bionanosens grant agreement # 951887.
This work considered a relatively simple model of lens distortion and required careful initialization of the optimization to converge due to high sensibility to local minima. Heinze et al. (2016)  improved the previous model by considering more sophisticated models of the main lens distortions. They introduced new parameters including the tilt and shift for the main lens. They are able to distinguish each micro-lens type, calibrating then the distance between the MLA and the sensor for each one but in separated calibration processes. The projection model and the metric calibration procedure are incorporated in the RxLive software of Raytrix GmbH. Strobl et al. (2016)  presented a step-wise calibration approach to overcome the fragility of the initialization which hinders the final optimization.
Micro-images array Finally, each micro-lens produces a micro-image (MI) onto the sensor. The set of these micro-images has the same structural organization as the MLA. The data can therefore be interpreted as an array of micro-images, called by analogy the micro-images array (MIA). The MIA coordinates are expressed in image space. Let δ i be the pixel distance between two arbitrary consecutive micro-images centers c c c k,l. With s the metric size of a pixel, let ∆ i = sδ i be its metric value, and ∆ µ be the metric distance between the two corresponding micro-lens centers C C C k,l. From similar triangles, the ratio λ between them is given by λ D d + D = ∆ µ ∆ i ⇐⇒ ∆ µ = λ ∆ i = D d + D • ∆ i. ( 4 ) We make the hypothesis that ∆ µ is equal to the micro-lens aperture.
Software environment All images have been acquired using the MultiCamStudio free software (v6. 15. 1. 3573) of the Euresys company. We set the shutter speed to 5 ms. While taking white images for the pre-calibration step, we set the gain to its maximum value. For Raytrix data, we use their proprietary software RxLive (v4. 0. 50. 2) to calibrate the camera, and compute the depth maps used in the evaluation. Our source code has been made publicly available: https://github. com/comsee-research/libpleno, and https:// github. com/comsee-research/compote.
Datasets We build four datasets with different focus distance h: for the 50 mm lens, R12-A for h = 450 mm, R12-B for h = 1000 mm, and R12-C for h = ∞; for the 135 mm lens, R12-D for h = 1500 mm. Each dataset is composed of: white raw plenoptic images acquired at different apertures (N ∈ {4, 5. 66, 8, 11. 31, 16}) using a light diffuser mounted on the main objective for pre-calibration, free-hand calibration target images acquired at various poses (in distance and orientation), separated into two  Examples of calibration targets acquired for the R12-B dataset are given in Figure  7  along with their 3D poses. A summary for each dataset is given in Table  1 , indicating checkerboard information and the distances at which the targets have been acquired for calibration and for the controlled evaluation.
Simulation environment In order to validate our model on Lytro-like plenoptic camera configuration, i. e. , unfocused plenoptic camera (UPC), we propose to evaluate our model in a simulation environment. We built our own simulator based on raytracing to generate images. Similar to the real-world dataset, we generated a dataset, named UPC-S, composed of several white images taken at different apertures (with N ∈ {2, 4, 5. 6}), various checkerboard poses for calibration and validation, and for evaluation, checkerboard images with known translation along the z-axis. Details are also given in Table  1. We used the Lytro Illum intrinsic parameters reported in Table  4  of  Bok et al. (2017)  as baseline for the simulation. They have been converted into our parameters and reported in Table  3. The MLA organization is hexagonal row-aligned, and composed of 541 × 434 (width × height) micro-lenses of the same type (I = 1).
Pre-calibration To estimate the parameters Ω , we set α = 2. 357, and since the camera is in Galilean internal configuration, we use R = -ρ • s, following Eq. (  25 ). Figure  5  shows the microimage radii as function of the inverse f -number with the estimated lines for dataset R12-B. Their distributions are represented by the violin-boxes. For N = 5. 66, we can see that radii distributions overlap, and that radii values are slightly overestimated as they do not fit exactly the borders of the micro-images. In practice, we only use white images that present distinguishable radii distributions in the estimation process, usually corresponding to small apertures. In case of R12-B, only white images at N = 11. 31 and N = 8 are used. The corresponding coefficients for all datasets are summarized in Table  2.
(2017)  was not able to detect corner observations on our datasets. Therefore, we used the same observations for our method (noted BAP in Table 3), Noury et al. (  2017 ) method (NOUR), and Nousias et al. (2017) method for each type (NOUS1, NOUS2, and NOUS3), which allowed us to focus the comparison on the camera model only. Finally, we provide the calibration parameters obtained from the RxLive software (RTRX) corresponding to the model of  Heinze et al. (2016) , and compare our depth measurements to their depth maps. Initialization. We initialize λ from Eq. (  28 ). Its value for each dataset is reported in Table  2. The difference between the initial value of λ and its value computed from optimized camera parameter is less than 0. 024 %, which validates the use of the initial value from Eq.
Therefore, in order to continue comparison, we manually set the initial parameters close enough to a solution. In contrast, we can see that the optimized parameters for BAP and NOUR are close to initial values, which shows that our pre-calibration step provides a strong initial solution for the optimization process. Intrinsic camera parameters. Optimized intrinsic parameters are reported for each dataset and for all the evaluated methods in Table  3. First, BAP, NOUR and NOUS all verify the condition F ≈ D + 2d when the focus is set at infinity (R12-C). Second, the focal lengths obtained from NOUR, NOUS and RTRX change significantly given the focus distance, and the ones obtained from NOUS even vary according to the micro-lens types. In contrast, only BAP shows stable parameters across all three R12-A,B,C datasets.