dataesr/scientific-paragraphs-categorization huggingface dataset:
Note that only odd gradients contribute to G grav as the average-magnetic-field components given by even gradients cancel out for 199 Hg atoms and UCNs. Several methods to combine cycle-by-cycle CsM data with magnetic-field maps were investigated using synthesized magnetic-field readings, which worked as follows. We calculated field values at the CsM positions using the magnetic-field-map data and varying the coefficients G syn ℓ,m randomly using a Gaussian distribution (equation (  17 )). In addition, a uniformly distributed random offset in the range of ±120 pT  [57]  was added at each CsM location, accounting for possible sensor offsets. In total, 200 random fields were synthesized and analyzed using eight different methods  [49]. The optimal method was selected by minimizing the difference between the synthesized and the fitted G grav up to fifth degree using equation  (10) , ∆G grav = G syn grav -G fit grav , ( 16 ) referred to as the deviation.
For a better estimation on G grav , we combined both the online CsM data and magnetic-field maps taken at a different time using a dedicated device for mapping. The optimal method to incorporate both results was determined using synthesized data. By applying this optimized method to measurement data, G grav was estimated with an unprecedented precision of 4. 05 pT cm -1. By extracting R at G grav = 0 after correcting for all known systematic effects, a new limit on the product of the scalar and the pseudoscalar couplings, corresponding to the monopole-dipole interaction, gives g s g p λ 2 < 8. 3 × 10 -28 m 2 (95% C. L. ) in an interaction range of 5 µm < λ < 25 mm. This limit improves our previous experiment by a factor of 2. 7, the best limit obtained with free neutrons. With the n2EDM apparatus at the PSI, we plan to search for a nonzero nEDM with a sensitivity of about 1 × 10 -27 e • cm.
Acknowledgments We acknowledge the excellent support provided by the PSI technical groups and by various services of the collaborating universities and research laboratories. In particular, we acknowledge with gratitude the long term outstanding technical support by  F Burri  and  M Meier. We thank the UCN source operation group BSQ for their support. We acknowledge financial support from the  Swiss National Science Foundation  through Project Nos. 117696 ,  137664 ,  144473 ,  157079 ,  172626 ,  126562 ,  169596 ,  178951  (all PSI),  181996  (Bern),  162574  and  172639  (both ETH). The group from  Jagiellonian University Cracow  acknowledges the support from the  National Science Center, Poland , through Grant Nos. UMO-2015/18/M/ST2/00056 ,  UMO-2020/37/B/ST2/02349  and  2018/30/M/ST2/00319 , as well as by the  Excellence Initiative-Research University Program  at the  Jagiellonian University.
This work was supported by the  Research Foundation-Flanders (BE)  under Grant No. G. 0D04. 21  N. We acknowledge the support from the  DFG (DE)  on  PTB core  facility center of ultra-low magnetic field  KO 5321/3-1  and  TR 408/11-1. We acknowledge funding provided by the  Institute of Physics Belgrade  through a grant by the  Ministry of Education, Science and Technological Development of the Republic of Serbia. This work is also supported by  Sigma Xi  Grants #  G2017100190747806  and #  G2019100190747806 , and by the award of the  Swiss Government Excellence Scholarships (SERI-FCS)  #  2015. 0594.
The parameters of the theoretical models are typically optimized for heavier nuclei. When used to predict the behavior of lighter nuclei, the predictions are insufficient in most cases. The neutron decay channel of the photonuclear reaction on 13 C is shown in Fig. 1  as a representative example. The Photo-Absorption of Nuclei and Decay Observation for Reactions in Astrophysics (PANDORA) project has been initiated to study the photonuclear reaction of the light nuclei. The project relies on interdisciplinary research across the fields of experimental and theoretical nuclear physics and particle astrophysics. A precise and systematic dataset consisting of the photoabsorption cross sections and branching ratio of each decay channel will be established through the experimental nuclear physics component of the project.
Two complementary methods will be employed to obtain the experimental data, i. e. , virtual photon excitation through rela-Fig. 1  Photoneutron cross sections, 13 C(γ , xn), as a function of excitation energy (photon-energy), showing one of typical cases where experimental data are mutually inconsistent and theoretical predictions are not satisfactory. The theoretical cross sections are calculated using the reaction code TALYS with two models, the Brink-Axel Lorentzian  [8, 9]  (dashed line) and the Goriely's hybrid  [10]  (solid line). They are plotted in comparison with the available experimental data taken from the EXFOR database  [11]. The data L0131  [12]  (cross marks) were measured using a tagged-photon beam, M0363  [13]  (diamond) with a Bremsstrahlung photon-beam, and L0048  [14]  (square) through the positron-annihilation in-flight method.
Predictions will be evaluated using the experimental data to estimate model uncertainties and for further development. The obtained experimental data and model predictions will be implemented in a general reaction calculation code, TALYS to be made available for applications in various fields. An important outcome of the PANDORA project will be a better characterization of the photo-interactions of UHECR, which is an essential ingredient of the theoretical modeling of their extra-galactic propagation and acceleration in astrophysical sources. This phenomenon is briefly described in Sect. 5. The remainder of this paper is organized as follows. The experimental methods for measuring the photoabsorption cross-sections and decay branching ratios are described in Sect. 2. The theoretical models are described in Sect.
Methods The experimental nuclear physics part of the PANDORA project aims to systematically measure the photoabsorption cross-sections and the p, n, α, and γ decay branching ratios for stable nuclei in the mass region below A = 60. Two modern experimental methods will be employed. One is the virtual photon excitation through relativistic proton scattering applicable at the Research Center for Nuclear Physics (RCNP) in the Osaka University, Japan, and iThemba LABS, South Africa  [5, 28, 29]. The other method is real photon excitation through high-intensity quasi-monoenergetic γ beams produced by the laser Compton scattering (LCS) at Extreme Light Infrastructure-Nuclear Physics (ELI-NP), Romania. The combination of complementary facilities is essential for obtaining a high-quality systematic dataset.
The photoreaction cross-sections can be estimated using the TALYS nuclear reaction code  [89] , which considers all types of direct, pre-equilibrium, and compound mechanisms to estimate the total reaction probability and the competition between the various open channels. Therefore, the photoreaction cross-section is usually estimated at energies of up to 50 MeV. Using a large variety of nuclear structure models to describe such cross-sections is relevant because it allows us to estimate the corresponding typical theoretical error.
Reaction calculations To estimate the photoreaction cross-sections of interest in this study, the different reaction mechanisms had to be simulated. One of the modern reaction codes called TALYS [146] includes many state-of-the-art nuclear models to cover all the main reaction mechanisms encountered in light particleinduced nuclear reactions. It provides a complete description of all the reaction channels and observables. The code includes photons, neutrons, protons, deuterons, tritons, 3 He, and α-particles as both projectiles and ejectiles, and singleand multi-particle emissions and fission. All the experimental information on nuclear masses, deformation, and lowlying state spectra is considered, whenever available  [147]. If not, various local and global input models have been incorporated to represent the nuclear structure properties, optical potentials, level densities, γ -ray strengths, and fission properties.
The TALYS code was designed to calculate the total and partial cross-sections, residual and isomer production cross-sections, discrete and continuum γ -ray production cross-sections, energy spectra, angular distributions, doubledifferential spectra, and recoil cross-sections. Furthermore, TALYS estimates the thermonuclear reaction rates relevant to astrophysics  [148]. The treatment of the photon strength function is particularly relevant to this study. Different models of the dipole strength function are available in TALYS in particular, the Simple Modified Lorentzian model (SMLO)  [149] , or the Gogny D1M+QRPA model  [150] , but the experimental strength can also be directly introduced. In addition, for the present project, two recent improvements have been brought to the TALYS code: (i) an updated description of the nuclear level density of light nuclei within the constanttemperature model  [151] , where the model parameters have been adjusted individually to all light targets of interest in the present project, and (ii) the account of isospin forbidden transitions (IFT) both in the single and multiple particle emission channels.
For the latter, a phenomenological correction reflecting the hindrance of dipole emission in self-conjugate nuclei is introduced, as detailed in Ref. [152]. Including IFT corrections can lead to rather different predictions of photoemission cross-sections in targets, such as 16 O or 17 O, as illustrated in Fig. 13. Based on the statistical model of Hauser-Feshbach, TALYS is a successful code for medium-and heavy-mass target nuclei. Such a model assumes that the capture process occurs with the intermediary formation of a compound nucleus in thermodynamic equilibrium. The energy of the incident particle is then shared almost uniformly by all the nucleons before releasing the energy through particle emission or γ -de-excitation. The formation of a compound nucleus is generally justified by assuming that the level density in the compound nucleus at the projectile incident energy Fig.
Thus it has a dominant contribution to the UHECR nuclei with a photodisintegration mean free path for the Lorentz factor ranging from ∼ 10 7 to 10 11 (Fig. 16 ). The most significant early study of UHECR nuclei propagation was certainly performed by  Puget, Stecker, and Bredekamp (PSB)  [173], who treated, in great detail, the propagation of nuclei (A ≤ 56) and provided simple parametrizations based on the available data for the GDR and quasideuteron (QD) excitation cross sections. More recently,  Khan et al. [172]  proposed new estimates of the GDR cross-section based on a theoretical calculation using the TALYS nuclear reaction code  [89] , which showed better agreement with the available data than that with the previous parametrizations from PSB  [173]. The improvement in the UHECR estimates owing to these new calculations rely not only on the use of microscopic cross sections but also on the implementation of a photodisintegration network involving many nuclei in contrast to the previous methodology, where only one given nucleus was considered for each A below 56.
Uncertainties in the model predictions will also be studied. The results will be implemented in the reaction calculation code TALYS for application in various fields. The photodisintegration process of ultra-high-energy cosmic rays in inter-galactic propagation using the theoretical predictions and uncertainty originating from the accuracy of the nuclear model predictions are the focus of the PANDORA project. The first beamtimes of the project's nuclear physics experiments are planned for 2023 at the RCNP and iThemba LABS. The first commissioning of the LCS γ -ray facility at the ELI-NP is expected at the end of the year 2023. Systematic data will be obtained in 5-10 years. Simultaneously, the development of nuclear models  [91]  and their application to UHECR propagation simulations  [174]  are ongoing.
Acknowledgements This work is supported partly by  Grants-in-Aid for Scientific Research   "KAKENHI" in Japan  with grant numbers of  JP19H00693  and  22K18715 ,  Japan-South Africa Bilateral  Funding from  JSPS  with a grant number of  JPJSBP 120216502  and from  NRF  with grant number  132993 , the  National Research Foundation of South Africa  through Grants No. 129411 ,  85509 , and  118846 , grants of the  Romanian Ministry of Research, Innovation and Digitization ,  CNCS -UEFISCDI , project number  PN-III-P4-PCE-2021-0595 , within  PNCDI III , and the contract  PN 23 21 01 06 ,  Pioneering Program   of RIKEN   for Evolution of Matter in the Universe (r-EMU ), and  Interdisciplinary Theoretical and Mathematical Sciences program  (iTHEMS, https://ithems. riken. jp) at  RIKEN , the  Deutsche Forschungsgemeinschaft (DFG ,  German Research Foundation ) -Project -ID 279384907 -SFB1245 , the  State of Hesse  within the  Research Cluster ELEMENTS  (Project  ID 500/10.
006 ), the grant " Nuclear Photonics" within the   LOEWE program , and  EXC-2094 -390783311  under Germany's Excellence Strategy. S. G. acknowledges financial support from the  F. R. S. -FNRS (Belgium). E. L. acknowledges funding from the  US-NSF CAREER  Grant  PHY-1654379  and  US-NSF  Grant  PHY-2209376. Z. Y. acknowledges the support from the  National Key R&D Program of China  (Grant Nos. 2023YFE0101500 ,  2022YFA1605100 ) and the  National Natural Science Foundation of China  (Grant No. 12275006 ). Funding Open access funding provided by  Osaka University.
The sweep windows for the streak camera range between 0. 5 ns and 1 ms. The streak tube is coupled to an ORCA-Flash 4. 0 V2 Digital CMOS camera with 1344 (wavelength) Â 1016 (time) pixels in the working area and 6. 5 μm pixel size; sometimes, this is binned 2 Â 2, i. e. , 672 Â 508 pixels to improve data handling. The readout speed of the camera is. 100 frames/s. Two different collection modes, enclosing trigger and sequential mode, are used. In sequential mode, exposure to the CMOS only occurs during a single sweep regardless of how many events are incident, meaning that the visible signal from the interaction of each NIR or XFEL pulse is recorded on the final spectrogram, separated in the time domain by their intrinsic pulse separation (see Sec. II A). In most cases, the experimental data is taken using this mode.
An example of the streak camera acquisition of optical light emitted from the W calibration lamp, designed to be a thermal gray-body source at a set temperature, is illustrated in Fig. 6. The horizontal axis represents time (here a 5 μs sweep window), and the vertical axis contains spectral information covering 451-955 nm. Poor optical sensitivity in the intensity measurement at the edges of the spectrogram are observed, so we restrict the wavelength range when fitting temperatures to 575-775 nm. All reference lamp calibrations were obtained with slits and spatial filtering in place, in order to probe the precise optical path from the sample in the respective transmission function. From spectral features present in the calibration spectrograms, datasets with distinct event timings, and data on spectrally distinct light sources (i.
Further measurements are required to determine the absolute timings of the other streak windows. A linear sweep rate approximation is found to be accurate enough for most analyses (including all those presented here), however, non-linear corrections are required in certain scenarios, for example, in modeling fluorescence at smaller PSF where the precise location of the fluorescence peak becomes important. Fluorescence peak position can also provide absolute information on x-ray timing within the streak window. Thus, where it was possible, the fluorescence of a particular target was used to determine the global timings of streak images collected for that target, i. e. , time zero is set to be the time at which the first pulse in any given train reaches the target. If insufficient or indistinct fluorescence emission was present for data on a particular target, then experimental timings were estimated from the timing of separate YAG:Ce screen fluorescence or from the onset of optical emission.
This implies insufficient optical emission to determine sample temperature. It should be noted that in high noise datasets the standard deviation of intensity in a binned spectrum can exceed the average intensity even after this exclusion is applied, but there is sufficient signal above zero for reliable fitting. 75. Least-squares fitting returns temperatures below 1500 K. 11, 13, 48, 51, 57, 58  3. The emissivity determined from fitting the data to Eq. (  1 ) (ϵ fit ) is 1000 times smaller or 100 times larger than the emissivity determined from fitting the emission spectrum from the calibration lamp (ϵ cal ), i. e. , 100 Á ϵ cal , ϵ fit , 0:001 Á ϵ cal. This criterion accounts for physical limits on emissivity variation and the possibilities for different size emitting areas observed. 4. The magnitude of temperature uncertainty (Sec.
H. Temperature error We use two independent ways to calculate the error on the temperature that is determined through least squares fitting: (a) from the in-built error from the curve_fit function in the SciPy python package 72 that is used for fitting and (b) from the curvature of χ 2 in temperature-emissivity space. The built-in Python error estimate, which is based on the minimization of the goodness-of-fit parameter χ 2 and calculated from the square-root of the diagonal of the covariance matrix, is consistently the smaller of the two. The curvature of χ 2 space is assessed by plotting χ 2 , given by χ 2 ¼ X i (I(λ i ) À I P (λ i , T)) 2 σ 2 I ¼ (T À T fit ) 2 σ 2 T þ χ 2 min ( 3 ) as a surface about the minimum, i. e. , as a function of the temperature and emissivity, then assessing the local curvature as 76 σ 2 T ¼ 2 @ 2 χ 2 @T 2 À1 : (4) In Eq.
26 MHz with a streak window of 20 μs (Fig. 19 ). Fluorescence emission is evident from additional datasets collected at lower power, which shows characteristic strong emission in the blue. To determine the expected fluorescence emission in this case, we surveyed the optical emission from a short streak window, e. g. , 5 μs, at varying XFEL pulse energies and converted it to the longer streak window during modeling, e. g. , 20 μs. The spectral shape and time dependence of the expected fluorescence spectrum is determined the same way as described above. To adapt from a 5 μs window to a 20 μs window, we assume the spectral and temporal shape [Fig. 19   by fluorescence and that at later times thermal emission is dominant. Thus, only temperature measurement obtained early in the record differs significantly before and after fluorescence emission is removed while there is minimal change in temperature at later timesand none at all after the XFEL pulse train is completed [Fig.
In our models, we assume the model geometry is fixed (see Fig. 24 ) and employ an isobaric approximation for thermal analysis, i. e. , that the heating and cooling can be treated as if it occurred at constant pressure. 31 This is suitable for making comparisons with the presented datasets. Temperature changes in the sample after initial heating are due to heat diffusion through the target; radiative heat losses from the sample (by way of thermal emission) have a negligible effect on the results (confirmed by running models with and without radiative losses). Inclusion of latent heat effects  39, 80  had a minor effect on the models, but as this did not significantly affect first-order comparisons with the data, it was left out in the models reported here. Temperature dependencies of parameters were included where available, whereas pressure changes with temperature were expected to have only a minor effect and were not included.
This is only relevant to very narrow samples: e. g. , for the 25 μm diameter Olivine disks compressed in DAC HIBEF30, comparable in size to the x-ray beam, the correction factor is 300=245 ¼ 1:23; while it is computed as unity for the other samples analyzed with the finite element technique. Model results for four datasets are shown in Fig. 25  with sample and model parameters given in Table  IV. Model parameters are initially set based on the known geometry, pressure, and literature properties of samples under pressure and temperature; where the model fails to predict the data, certain parameters are optimized to improve the fit as shown in the table. Comparison of the models with the data and the meaning of this analysis is discussed in Sec. VII E.
7 ), with a notable case being that of ruby. In one DAC, a larger ruby piece (used as a pressure marker, see Sec. IV) was targeted independently with the XFEL (Fig. 26 ). The XFEL-excited ruby luminescence also exhibited conventional fluorescence characteristics, including line emission at 696 nm (under pressure of 5 GPa) and a relaxation time measured to be 250 μs, comparing well with literature values using laser-induced optical fluorescence. 99 This suggests that XFEL irradiation with SOP can allow in situ ruby pressure measurements without additional laser excitation, including during dynamic pressure conditions. As ruby was localized in samples, it was easily avoided in practice. Data are currently insufficient to establish the degree of sample contributions to observed fluorescence in most cases in the DAC, with observed variations in fluorescence amplitude likely to be due to different diamonds and stress states.
E. Finite element modeling implications In some cases, FEA closely predict the observed SOP temperatures (Fig. 25 ). This is not unexpected for the freestanding Ta foil [Fig. 25(a) ] since the nominal beamline transmission (30%) was calibrated to ablation damage data on Ta foil during the same experiments,  39  however, this still shows the consistency of the independent SOP measurement of temperature with model predictions. 62 Similarly, a completely independent experiment on olivine [Fig. 25(c)] , which reaches only modest temperatures, is closely predicted (considering that emission likely emerges from the full volume of the sample in this case). For high-Z foils in the DAC [Figs. 25(b) and 25(d)], significantly lower radiation absorption than expected must be assumed in order to reproduce the observed temperatures, and in some cases other parameters, e.
ACKNOWLEDGMENTS We thank  H. Sinn  for experimental assistance and  T. Tschentscher  and  S. Pascarelli  for fruitful discussions. We acknowledge European XFEL in Schenefeld, Germany, for provision of X-ray free-electron laser beamtime at  Scientific Instrument HED (High Energy Density Science)  and would like to thank the staff for their assistance. The authors are indebted to the  Helmholtz International Beamline for Extreme Fields (HIBEF) user consortium  for the provision of instrumentation and staff that enabled this experiment. We acknowledge  DESY (Hamburg, Germany) , a member of the  Helmholtz Association HGF , for the provision of experimental facilities. Parts of this research were carried out at PETRA III (beamline  P02. 2 ) through proposal I-20190367 EC. The research leading to this result has been supported by the  project CALIPSOplus  under the Grant Agreement No.
acknowledge support from the  DFG  Project No. KO-5262/1. S. M. and J. C. acknowledge support from the  MetalCore  project, supported by the  French government  through the  Programme Investissement d'Avenir (I-SITE ULNE/   ANR-16-IDEX-0004  ULNE) managed by the  Agence Nationale de la Recherche. O. B. B. acknowledges the support of the Scottish.