dataesr/scientific-paragraphs-categorization huggingface dataset:
DATA AVAILABILITY STATEMENT The DES Y1 catalog is available in the Dark Energy Survey Data Management (DESDM) system at the National Center for Supercomputing Applications (NCSA) at the University of Illinois. It can be accessed at https://des. ncsa. illinois. edu/releases/ y1a1/key-catalogs. The pipeline used for the measurement is publicly available at https://github. com/hocamachoc/ 3x2hs_measurements. Synthetic data produced by the analysis presented here can be shared on request to the corresponding author.
We then investigate the variability of the convectionrelated surface structure as a source for parallax measurement uncertainty. Methods. We use state-of-the-art three-dimensional radiative hydrodynamics simulations of convection with CO5BOLD and the post-processing radiative transfer code OPTIM3D to compute intensity maps in the Gaia G photometric system. We calculate the variabiltiy, as a function of time, of the intensity-weighted mean (or the photo-center) from the synthetic maps. We then select the RSG stars in the cluster and compare their uncertainty on parallaxes to the predictions of photocentre displacements. Results. The synthetic maps of RSG show extremely irregular and temporal variable surfaces due to convection-related dynamics. Consequentially, the position of the photo-center varies during Gaia measurements between 0.
aanda. org (Davies & Beasor 2018). RSG also play a key role in formation channels for gravitational wave sources through common envelope evolution (e. g. Belczynski et al. 2016; Klencki et al. 2021). It is thus imperative to investigate the physical properties of these important sources of astrophysical information in as much detail as possible. A crucial step is the determination of RSG luminosities based on the accurate measurement of distances. For Milky Way RSG the use of Gaia parallaxes seems ideal for this purpose. Gaia (Gaia Collaboration 2016) is an astrometric, photometric, and spectroscopic space mission performing a whole sky survey including a large part of the Milky Way. The most recent release (Gaia Early Data Release 3, Gaia Collaboration 2021) provides parallaxes of unprecedented accuracy.
Gaia EDR3 cluster χ Per data: parallaxes and measurement uncertainty The nearby young and well-studied cluster χ Per has a relatively large population of RSGs  (Currie et al. 2010 , and references therein). Davies & Beasor (2019)  have used Gaia DR2 data to determine a distance of d = 2. 25 ± 0. 15 kpc. With the improvements obtained by Gaia EDR3 we can now repeat the distance determination and concentrate on a comparison of main sequence stars and RSG parallaxes. In a first step, we focus on bright main sequence stars with Gaia EDR3 magnitude G ≤ 10. 8 mag and effective wavenumber ν eff ≥ 1. 5µ -1. Following  Davies & Beasor (2019)  we use Gaia EDR3 proper motions as a criterium for cluster membership. We regard all objects with (p α -p c α ) 2 σ 2 α + (p δ -p c δ ) 2 σ 2 δ ≤ 1 (1) as cluster members.
Acknowledgements. This work is funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Fundation)  under Germany's Excellence Cluster Strategy -EXC-2094 -390783311. AC acknowledges support from the  French National Research Agency (ANR)  funded project  PEPPER  ( ANR-20-CE31-0002 ). BF acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  Grant agreement No. 883867 , project  EXWINGS ) and the  Swedish Research Council (Vetenskapsrådet , grant number  2019-04059 ). This work was granted access to the  HPC resources of Observatoire de la Côte d'Azur -Mésocentre SIGAMM  and  Swedish National Infrastructure for Computing (SNIC)  at  UPP-MAX.
This demonstration suggested that the problem was closed, and so Planck devoted his efforts to understanding the thermodynamics of the system described by such function. However, contradictory data soon emerged. Under the supervision of Rubens, Hermann Beckmann measured the temperature dependence of far-infrared blackbody radiation for the first time in 1898 using the reststrahlen bands of fluorite. A disagreement with the assumed distribution was noted, but the quality of the evidence was insufficient for Rubens, who considered the deviations to be consistent with instrumental error  [2, 39]. However, when Lummer and Peter Pringsheim reported similar anomalies in measurements with increasingly wider temperature and spectral ranges, Rubens and PTR's Ferdinand Kurlbaum set out to improve Beckmann's reststrahlen experiment to test the theory to its extreme, using four blackbody cavities spanning a temperature range from -180 to 1600 • C.
Acknowledgements SR is supported by the  STFC  consolidated grant  ST/P000754/1  "  String Theory, Gauge Theory & Duality " and a  Visiting Professorship  at the  University of the Witwatersrand , funded by a  Simons Foundation  grant ( 509116 ) awarded to the  Mandelstam Institute for Theoretical Physics. We thank  Fabien Vignes-Tourneret  and  Vaclav Kostosevec  for discussions which led to the initiation of this project. We also thank  George Barnes ,  Robert de Mello Koch  and  Adrian Padellaro  for interesting discussions on the subject of the paper.
A time-resolved calibration (TRC)  [38]  was performed to correct for any time-dependent drifts arising from power supply instabilities and temperature fluctuations using the mass data acquisition (MAc) software package  [39]. The dominant CaF + and TiO + ions were chosen as the cal-  ibration species for the TRC of 59 Cr + , 61 Cr + and 62 Cr + -64 Cr + masses, respectively. For 65 Cr + , 65 Cu + was used as a reference for TRC. To determine the peak centroids, a hyper-EMG function  [40]  was fitted using the emgfit Python wrapper  [41]. The hyper-EMG function particularly excels in fitting overlapping peaks such as the case of 64 Cr + as can be seen in Fig. 2  [42]. The uncertainty in the determined mass values is a combination of the fitting uncertainty, AME uncertainty of the reference/calibrant ion, and a systematic uncertainty δm/m of 1 × 10 -7  [43, 44].
The authors would like to thank  M. Good  for his continual support,  J. Lassen  and the  TRILIS group  for their help with setting up the Cr lasers scheme for signal optimization, and  S. R. Stroberg  for the imsrg++ code [65] used to perform these calculations. The shell-model diagonalization of VS-IMSRG calculations were done with the KSHELL code [66]. This work was supported by the  Natural Sciences and Engineering Research Council (NSERC)  of  Canada  under grants  SAPIN-2018-00027  and  RGPAS-2018-522453 , by the  National Research Council (NRC)  of  Canada  through TRIUMF, by the  UK Science and Technology Facilities Council STFC  Grant No:  ST/V001051/1 , and by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) -Project-ID 279384907 -SFB 1245. Computations were performed with an allocation of computing resources on Cedar at  WestGrid  and  Compute Canada , and on the Oak Cluster at TRIUMF managed by the  University of British Columbia department of Advanced Research Computing (ARC).
COMPUTATIONAL DETAILS The geometries of nitroxyl, glyoxal, tetrazine, and cyclopentadienone are obtained from ground-state optimizations at the level of CC3/aug-cc-pVTZ. 12, 13 In all calculations, we utilize scalar-relativistic energy-consistent Hartree Fock (HF) pseudopotentials  54, 55  with the corresponding aug-cc-pVDZ Gaussian basis sets. The exponents of the diffuse functions are taken from the corresponding all-electron Dunning's correlation-consistent basis sets. 56 For glyoxal, we also tested the use of an aug-cc-pVTZ basis set and found compatible excitation energies both at the VMC and DMC level. All HF and SA-CASSCF calculations are performed in GAMESS(US)  57  with equal weights on all states. CIPSI calculations are performed with the Quantum Package  58, 59  program. All states of interest are singlets, so determinants are chosen such that the expansions remain eigenstates of S 2 with eigenvalue 0.
For each molecule, the ground and excited states of interest have the same symmetry so the same set of orbitals are used in all state's determinant expansions. 5] [36]  The choice of weights in the CIPSI selection criterion is detailed for each molecule in the Supporting Information. QMC calculations are performed with the CHAMP 60 program using the method detailed in Section 2. 2. Damping parameters ranging from τ SR = 0. 05-0. 025 a. u. are used in the SR method during VMC optimization. A low-memory conjugate-gradient algorithm  61  is used to solve for the wave function parameters in the SR equations. All DMC calculations use a time step of τ DMC = 0. 02 a. u. A value of λ IJ = 1. 0 a. u. is used for all two-state calculations, while multiple values are used for the three-state system, as discussed in the previous section and in the Supporting Information.
The EUSO-Balloon Instrument A global view of the EUSO-Balloon instrument is shown in Fig. 1  -its two main components being the optical bench and the instrument booth. The main driver for determining the general layout came from optical specifications, i. e. the pixel size and the FoV which had to be representative for the UV intensity within the pixel of a standard JEM-EUSO Photo-Detector Module (PDM). An electronic block diagram of the instrument, summarizing the various subsystems and components is shown in Fig. 2. Besides the focal surface detector (PDM) and associated electronics (Data Processor, DP), which are described below, the instrument booth houses the telemetry system (SIREN), CNES specific instrumentation (Flight Chain Instrumentation, Hub), and two battery-packs. The development of the components and sub-assemblies is based on similar JEM-EUSO components and sub-assemblies, which have all been designed and built within the JEM-EUSO collaboration.
This is the pedestal that should be dark enough to permit detection of an UHECR track. A detailed description of the analysis and results to infer the background UV light intensity observed by EUSO-Balloon is reported in  (Abdellaoui et al. 2019 ). Here we summarize the key aspects and results of the analysis. Figure  12  shows the average normalized count rates N as a function of the packet time. Several breaks are present due to the interruption of measurements necessary to switch between different data acquisition modes which were part of the technological tests foreseen for this flight. The conversion of the digital counts in each pixel into UV intensity has to take into account many aspects. Among them, the most relevant are: entrance aperture of the optics and its throughput, MAPMT detection and filter efficiencies, the pixel's FoV and GTU duration.
The WRF model was applied to the EUSO-Balloon observations to check its reliability in evaluating atmospheric vertical profiles to determine the height of clouds eventually detected by the IR camera. At first, the accuracy of the WRF simulated profiles were tested by comparing them to radiosounding data observed close to the balloon flight path. Then, the analysis was extended to the entire EUSO-Balloon scene by testing the simulated profiles on the satellite spectroradiometer MODIS, used as a reference sensor. Finally, by applying a methodology based on WRF, the brightness temperature information retrieved by IR camera images was translated into CTH (an example is reported in the following section). The details of these analyses can be found in  (Merino et al. 2015)  and  (Tabone et al. 2015).
Three radiosounding stations close to the EUSO-Balloon flight were considered: Moosonee, Green Bay and Gaylord. The soundings were recorded on August 25, 2014 at 00:00 UTC. A fourth real vertical temperature profile, recorded in the first 40 minutes of flight by a thermometer installed on the balloon, until it reached about 17 km of altitude, was used in addition. Here, the WRF model version 3. 6 was used to simulate the atmospheric conditions over the entire balloon flight. The model was initialized by global analyses provided by the ECMWF (European Centre for Medium-range Weather Forecasts) global model (ECMWF 2016) with grid-spacing of 0. 125 • × 0. 125 •. The unique MODIS data available for the entire balloon flight were the ones observed in the scan that occurred above Canadian lands on August 25, 2014 at 02:59 UTC.
A possible explanation this behavior is the saturation of pixels inside the track, which shifts the weight of the timing fit. The result of splitting the dataset into high and low energy settings is also shown in Fig. 21. The expected mean value of the distribution should be slightly above 90 •. The reason is that the laser was mounted to produce horizontal tracks (meaning a 0 angle of 90 • ) when the helicopter had a horizontal attitude. However, the helicopter was slightly turned sideways towards the ground by approximately 1 • -2 • to fly a circular pattern. The estimation of the bank angle uses the velocity v of the helicopter and the assumption of truly circular flight pattern (θ = arctan(v 2 /(R • g))) of turning radius R. The velocity and position of the helicopter were recorded using the on-board GPS at a 1 Hz rate.
4 × 10 -3. Therefore, the probability of a fake event (beginning at a given time) is (P 0 ) 3 = 2. 5 × 10 -9. The number of packets recorded during the EUSO-Balloon flight and used in this analysis were 259,400. With 128 GTUs each, this amounts to a total of 3. 3 × 10 7 GTUs. The probability of finding a fake event in the whole data set is around 8%. If instead, T = 4 is chosen for the threshold value, the individual fluctuation probability is P ( ≥ 3) = 3. 2 × 10 -5 , which brings the probability of finding one fake event in the whole data set down to a totally negligible value. Apart from the expected signals produced by the light system of the helicopter flying under EUSO-Balloon, the search did not lead to the detection of well identified signals with a possible meteorological or astronomical origin.
Conclusions and Perspectives The EUSO-Balloon flight in 2014 allowed a full scale end-to-end test of most of the key technologies and instrumentation employed in the following projects of the JEM-EUSO program. A measurement of the UV intensity in different atmospheric and ground conditions was achieved. The data allowed the development, on real data, of the same methodology that will be used by future missions of the JEM-EUSO program in space to determine the exposure. The estimation of the absolute UV intensity proved that the general behavior of the instrument is fairly well understood. The cross-correlation of the UV and IR maps allowed detecting clouds in the UV band. This result is very useful in view of a spacebased detector such as JEM-EUSO as it will help to select high-quality cosmic ray data.
Projects of RIKEN and JSPS KAKENHI Grant (22340063, 23340081, and 24244042) , the  Italian Ministry of Foreign Affairs and International Cooperation , by the  Italian Space Agency  through the  ASI INFN  agreement n. 2017-8-H. 0and contract n. 2016-1-U. 0,by the  'Helmholtz Alliance for Astroparticle Physics HAP ' funded by the  Initiative and Networking Fund of the Helmholtz Association, Germany , by  Slovak Academy of Sciences  MVTS JEM-EUSO as well as  VEGA  grant agency project  2/0076/13 , and by the  National Science Centre in Poland  grant  2017/27/B/ST9/02162. The Russian team is partially supported by the  State Space Corporation ROSCOSMOS and M. V. Lomonosov Moscow State University  through its " Prospects for Development" programme. The US team is supported by the following NASA grants:  NNX13AH54G ,  NNX13AH55G ,  NNX13AH53G ,  NNX13AH52G  and  NNX16AG27G.
This work was supported in part by the  ECSEL Joint Undertaking  Project  5G_GaN2  under Agreement 783274, in part by the  Projects of Scientific Grant Agency [Vedecká Grantová Agentúra (VEGA) ] under Grant  1/0733/20 , and in part by  Slovak Research and Development Agency, Ministry of Education, Science, Research and Sport ,  Slovak Republic  under Grant  APVV-21-0365.
Toy models The toy models were generated using the mk_snia_toy_model. py script (see Sect. 4) using the --highni (for the normal SN Ia model) and --lowni (for the low-luminosity SN Ia model) options. Both models have a total mass M ej = 1. 0 M ⊙ , a kinetic energy E kin = 10 51 erg, and are calculated at the time t f = 2 days post explosion. The models have an exponential density profile (e. g. Jeffery 1999) , ρ = ρ c e -v/v e , (1) where v e = E kin 6M ej ≈ 2895 km s -1 (2) is the e-folding velocity, and ρ c = M ej 8πv 3 e t 3 f ≈ 6. 32 × 10 -10 g cm -3 (3) is the central density at the chosen time. The ejecta consist of n spherical shells indexed by i = 1,. , n. We use a uniform velocity grid with width ∆v = 50 km s -1. Each shell then has an inner and outer velocity coordinate given by: v 0,i = (i -1)∆v and v 1,i = v 0,i + ∆v.
The mean opacities include processes of photoionisation, free-free absorption, Thomson scattering on free electrons, and Rayleigh scattering on neutral hydrogen. The contribution of lines to the expansion opacity is A163, page 6 of 25 evaluated by the generalised formula of  Castor et al. (1975)  or by the formula of  Blinnikov (1996)  using the Sobolev approximation for line opacity. The expansion opacity in an expanding medium is treated with a thermalisation parameter set to 0. 9 as recommended by  Kozyreva et al. (2020)  to model SNe Ia. The sources of atomic data for processes in continuum can be found in  (Utrobin 2004). Oscillator strengths of lines are taken from the Kurucz line database 6  containing nearly 530 000 lines. Energy level data are from the atomic spectra database of the National Institute of Standards and Technology.
The atomic data used for the bound-bound transitions were taken from Kurucz CD 1. This is a larger set of atomic data than CD 23. The details are described in Appendix A. 3. For this comparison study, the co-moving frequency grid for the thermalised photons (i. e. not the γ-rays) used 17 664 bins, with equal logarithmic spacing, ranging from 10 14 Hz to 2 × 10 16 Hz. The output spectra used 1044 frequency bins over the range 1. 1 × 10 14 Hz to 2 × 10 16 Hz (∼150 Å to ∼2. 7 µm). The time steps for the homologous expansion and radioactive decay began at approximately a few hours at the start of the calculation, and grew to no longer than 1 day, with a maximum time-step growth rate of 10%. One million γ-ray packets were emitted at each time step. As these γ rays deposited their energy, their packets were discarded, while one thermally sourced photon packet was emitted for each discarded γ-ray packet.
STELLA The multi-group radiation hydrodynamics code STELLA  (Blinnikov & Bartunov 1993; Blinnikov et al. 1998 Blinnikov et al. , 2006) )  is capable of computing the evolution of the radiation field coupled to the hydrodynamics, as well as the bolometric light curve, spectral energy distribution, and resulting broad-band magnitudes and colours. Therefore, STELLA does not require the condition of homologous expansion and is capable of treating shock propagation and any dynamical processes taking place in the SN ejecta. Energy deposition from 56 Ni and 56 Co radioactive decay is treated in a one-group diffusion approximation with an absorption opacity of 0. 05 Y e cm 2 g -1 according to  Swartz et al. (1995). The energy of positrons is thermalised locally. The ionisation and level populations of a limited set of species (H, He, C, N, O, Ne, Na, Mg, Al, Si, S, Ar, Ca, stable Fe, stable Co, and stable Ni) is treated in the LTE approximation.
Radiation is not treated in equilibrium with the matter. The colour temperature is estimated as a black body temperature via the least-squares method. The opacity includes photoionisation, free-free absorption, and electron scattering processes assuming LTE for the plasma and line interactions. Radioactive 56 Ni and 56 Co contribute to the stable Fe when the line opacity is calculated. The expansion opacity is calculated according to  (Eastman & Pinto 1993). The thermalisation parameter for the line opacity treatment is set to 0. 9  (Kozyreva et al. 2020). The line opacity is calculated using a data base of 153 441 spectral lines partly from  Kurucz & Bell (1995) ,  Verner & Yakovlev (1995), and Verner et al. (1996b). The spectral energy distribution is computed in the wavelength range from 1 Å to 50 000 Å.
SuperNu SuperNu is a multi-group LTE radiative-transfer code that employs Implicit Monte Carlo (IMC) and Discrete Diffusion Monte Carlo (DDMC;  Wollaeger et al. 2013; Wollaeger & van Rossum 2014). IMC solves the thermal radiative-transfer equations semi-implicitly by treating some absorption and emission as instantaneous effective scattering (see e. g. Fleck & Cummings 1971). DDMC optimises IMC in optically thick regions of space  (Densmore et al. 2012 ) and ranges of wavelength  (Abdikamalov et al. 2012; Densmore et al. 2012 ) by replacing many low mean-free-path scattering events with single leakage events. SuperNu can apply IMC and DDMC in both static and homologous, semi-relativistically expanding atmospheres. The code has been verified by analytic and semi-analytic radiative transfer tests  (Wollaeger et al.
2020  for a study with SuperNu using expansion opacity). Opacity in SuperNu includes free-free  (Sutherland 1998) , and bound-free  (Verner & Yakovlev 1995; Verner et al. 1996a ) processes, as well as the bound-bound opacities from the Kurucz line lists 8 , and the standard elastic Thomson scattering opacity. Weak lines in the Kurucz data set are omitted from the SuperNu line list where the opacity is dominated by stronger lines. The total number of available lines for the present simulations is 591 288. This list was motivated by studies using the PHOENIX code in the work of van Rossum (2012), using the full line list (>10 7 lines) as a benchmark. For the simulations in this work, opacity is computed in 1000 logarithmic wavelength groups from 100 to 32 000 Å. Each simulation presented for SuperNu uses 4 194 304 source packets per time step, with maximum active packet populations of between 100 and 200 million.
TARDIS then calculates Sobolev optical depths for line interaction, and opacities for continuum processes. These values are then used in the subsequent Monte Carlo step, which produces summary statistics for updating the opacities. The other convergence criterion is a match of the integrated packet output luminosity and the requested luminosity, which is achieved through iterative adjustments of the inner temperature. To handle atomic data, the TARDIS collaboration has developed an additional package named CARSUS  (Pássaro et al. 2019)   10  with documentation available online 11. CARSUS can read atomic data (masses, ionisation energies, levels, lines, photoionisation cross sections, and collisional cross sections) from NIST  (Kramida & Ralchenko 1999) , Chianti  (Dere et al. 2019 (Dere et al.
, 1997)) , CMFGEN  (Hillier & Miller 1998; Dessart & Hillier 2010) , and Kurucz  (Kurucz 2009). For the models in this paper, we used Kurucz CD 23 as the source of atomic data (see Appendix A. 4 for the full description). The setup files for TARDIS that are used in this work are available online 12  using an atomic data set from Kurucz CD 23. In the setup used for the code comparison work, we run TARDIS in a mode that self-consistently finds a temperature stratification given an inner boundary velocity and output luminosity. We used the mean bolometric luminosity of the other comparison codes for our output luminosity. The structure of the toy01 and toy06 models was set at 520 shells between 9000 and 35 000 km s -1. The DDC models were truncated to 40 shells between 9000 and 35 000 km s -1.