dataesr/scientific-paragraphs-categorization huggingface dataset:
Fragmentation Comparing the estimated masses and column densities to the corresponding values of the original CORE study of 20 highmass star-forming regions in the more evolved evolutionary stages of high-mass protostellar objects (HMPOs), massive young stellar objects (mYSOs), and ultracompact Hii regions  (Beuther et al. 2018) , we find that the masses are covering a similar range. In contrast to that, the column densities of the original CORE sources are between ∼10 23 and ∼10 25 cm -2 , about one order of magnitude larger than what we find here for the two younger regions. While this may be attributed to the younger evolutionary stage of the ISOSS sources presented here, we caution that the spatial resolution of the original CORE data was about a factor of 2 better (because of uniform weighting there and natural weighting here, see Sect.
Such observational conditions would imply that the column density probability density func-Fig. 15. Temperature map for ISOSS23053 derived from the H 2 CO line emission. Both panels show, in color scale, the derived temperatures with black contour levels of 50, 100, and 150 K. The high temperatures in particular at the eastern edge of the map are not real but are caused by the lower signal-to-noise ratio there. The yellow contours in the top and bottom panels present the 1. 3 mm continuum and the integrated SiO(5-4) emission, respectively. The 1. 3 mm continuum contours are in 3σ steps (1σ ∼ 0. 16 mJy beam -1 ). The SiO emission is integrated from -61 to -43 km s -1 clipping all data below an approximate 4σ threshold of 20 mJy beam -1. Contour levels are from 10 to 90% (step 10%) of the integrated peak emission of 1.
Conclusions and summary In the context of the NOEMA large program CORE, which studies, among other topics, the fragmentation processes during high-mass star formation, we studied two young regions of intermediate-to high-mass star formation, namely ISOSS22487 and ISOSS23053. We used NOEMA and the IRAM 30 m telescope to produce high spatial resolution (∼0. 8 ) mosaics to investigate their larger-scale fragmentation and cloud formation processes. In both regions, many fragments can be identified in the 1. 3 mm continuum emission, and a diversity of spectral lines can be imaged over the entire field of view. We concentrate on the fragmentation based on the continuum emission, and on the kinematics of the clouds based on the DCO + , H 2 CO, and SiO emission. A forthcoming paper will present the entire spectral line data and a chemical analysis  (Gieser et al.
in prep. ). The 1. 3 mm continuum data reveal 15 and 14 cores in ISOSS22478 and ISOSS23053, respectively. Most of them are associated with filamentary sub-structures in the cloud. Depending on the assumed temperatures of the cores, we estimate slightly different core masses. These differences then result in mass-size relations varying possibly between M ∝ r 2 and M ∝ r 3. Hence, while the data are consistent with the classical mass-size relation of r 2 expected from the 3rd Larson relation for regions with constant mean column densities, a steeper relation cannot be ruled out. The latter could be explained because the cores in these maps have sharp boundaries and, thus, the lower iso-contours in the mm emission do not have much larger areas than higher iso-contours. Such steeper mass-size relation would correspond to similar mean densities for the cores.
Furthermore, the correlation of the core masses with their nearest-neighbor separations is consistent with thermal Jeans fragmentation. In contrast to the original CORE sample of slightly more evolved sources, here, we barely find separations at our spatial resolution limit. Hence, our data resolve the large-scale fragmentation of the parental gas cloud well. The kinematic analysis reveals very different observational signatures between the two regions. While ISOSS22478 is more filamentary in nature with several velocity components along the length of the filament, ISOSS23053 shows at the locations of the most massive cores a very steep velocity jump between two velocity components. The velocity gradient is roughly 50 km s -1 pc -1 measured in DCO + (3 -2) and even ∼122 km s -1 pc -1 measured in SiO(5 -4).
Acknowledgements. This work is based on observations carried out under project number  L14AB  with the  IRAM NOEMA Interferometer  and the IRAM 30 m telescope. IRAM is supported by  INSU/CNRS (France) ,  MPG (Germany)  and  IGN (Spain). We like to thank an anonymous referee for very useful comments. H. B. and S. S. acknowledges support from the  European Research Council  under the  Horizon 2020 Framework Program  via the  ERC Consolidator Grant   CSF-648505. H. B. also acknowledges support from the  Deutsche Forschungsgemeinschaft in the Collaborative Research Center  ( SFB 881 ) "The  Milky Way System " (subproject  B1 ). A. P. acknowledges financial support from  CONA-CyT  and  UNAM-PAPIIT IN113119  grant, México. DS acknowledges support by the  Deutsche Forschungsgemeinschaft  through  SPP 1833 : " Building a Habitable Earth " ( SE 1962/6-1 ).
For this, we opted for a polyhedral mesh recognized by these advantages. The refinement criteria were carried out with appropriate control volumes size (boundary layer, wake, pressure gradients, etc. ). Figure  5  presents the complete numerical domain generated in polyhedral mesh. In our work, we use the commercial code ANSYS-Fluent Fluid Simulation Software, that is based on the finite volume discretization. Our approach is based on the study of the steady viscous incompressible flow with the Menter's Shear Stress Transport turbulence model (kω SST). The Moving Reference Frame (MRF) or Frame motion method is used to model the fan motion. For velocity-pressure coupling, SIMPLE algorithm is used with MUSCL Third order for the momentum equation and the second order for the rest of equations.
Percival data acquisition The data from the mezzanine card is passed to a cluster of Linux servers, via a deep buffer switch. The function of the deep buffer switch is to reroute the data comprising one frame to the same server, according to the address provided in the unique UDP header. As the addresses are changed from frame to frame in a round robin fashion, the computing load is evenly distributed among the different Linux servers of the cluster. The Linux servers grab the frames, format the data properly, if desired apply the calibration parameters, and send the data to storage. Two parallel processes on a single server have been used for the measurements reported in this paper; we envisage to use four servers to acquire and process data of the detector at its maximum speed. The data acquisition software which runs on each of the Linux servers is the OdinData framework  (Yendell et al.
The P2M-Control interface The detector is controlled through a 1 Gbit s À1 Ethernet connection on the P2M-CarrierBoard. A process running on one of the servers sends the commands to the camera through the TCP/IP protocol. The control software (OdinControl) provides a webserver which accepts standard http requests at specific urls, in the same way a web-browser fetches html pages from the internet and displays them to the user. The webserver runs on a host in the network: requests are routed by the webserver into a Percival adapter where Percival-specific code is available to interpret and respond to them. There are several ways to send http requests to the webserver: either as a Python module (offering the user Python commands that could be combined in scripts), or as an html/javascript webpage suite (which sends http requests using javascript ajax and presents an html user-interface to the user).
(2) Fixed-gain calibration, that determines how to convert ADC outputs to meaningful physical parameters (electrons collected by the photodiode). The photon transfer curve (PTC) method, described in detail by  Janesick (2007) , is used for this purpose. The procedure requires exposing the array to a constant photon flux (and is eventually repeated at different intensities). A photon source in the visible regime (such as a LED of well defined wavelength) can be used for this purpose. Some key-performance parameters (noise, full well, etc. ) can also be extracted from the same datasets used to extract the calibration parameters: they will be described below, and then summarized in Table  1  in Section 5.
Noise The noise of the detector can be calculated from dark datasets. Given a set of images (typically 500-1000), acquired in dark condition, with the same integration time and at a stable temperature, the r. m. s. noise at the detector output is calculated by taking, for each pixel, the standard deviation of the detector output. This is converted to an equivalent noise charge (e. n. c. ) at the input by applying the e/ADU ratios mentioned above. To measure the results discussed in this section, crosstalk effects have been suppressed by operating operating in interleaved mode as described in Section 5. 1. The noise of the detector depends substantially on the system gain. In low-flux conditions, the correlated 'Sample' and 'Reset' images (provided at the detector output) are to be digitally subtracted: this operation of correlated double sampling (CDS) reduces significantly the non-uniformities in dark images and, more importantly, suppresses reset noise and slow-signal variations (due, for example, to slow bias shift).
In the following, we explored the system behaviour for an auto-adaptive gain configuration using the triplet high/medium/low gain. In order to interpret the detector output in this case, an additional calibration step is necessary (with respect to what is described in Section 3) to measure the detector transfer function over a wide range of charge-collection inputs. As for the PTC measurement, a photon source in the visible regime can be used for this purpose, and key-performance parameters can be extracted from the same datasets.
Acknowledgements The research described in this paper was carried out in part (post-processing of the sensor, Section 2. 2) at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration  ( 80NM0018D0004 ). The authors gratefully acknowledge the support of  Alacron Inc. in providing access to the delta-doping process. Open access funding enabled and organized by Projekt DEAL.
I. INTRODUCTION GENIE is an international collaboration of scientists working on a global analysis of neutrino scattering data and on the incorporation of modern theoretical inputs and experimental data into robust and predictive semi-empirical comprehensive neutrino interaction simulations. GENIE develops and maintains a suite of well-known software products for the experimental neutrino community, which includes its popular Generator product  [1]. With the recent release of the GENIE Generator v3, a substantial change in the way that the GENIE Collaboration approaches the process of developing, validating, characterising, tuning and releasing comprehensive neutrino interaction simulations came into sharp focus. The focus of the GENIE Collaboration has always been the development of universal comprehensive models, handling all probes and targets and simulating all processes across the entire kinematic phase space relevant for neutrino experiments.
All such configurations, that are easily invoked and run out of the box, combine modelling elements in a way that is as consistent as possible, and are validated, characterised and tuned as a whole. This important development was underpinned by a substantial upgrade of GENIE capabilities for systematic model validation, model characterization through comparisons to large collections of complementary scatter-ing data with neutrino, charged lepton, and hadron probes, and the development of an advanced global analysis of scattering data. The GENIE global analysis was made possible through the continued development of curated data archives, and the successful large-scale refactoring and interfacing to the Professor tool  [2]  of a very extensive set of GENIE codes that implement comparisons to data, within a framework that allows the efficient manipulation of large ensembles of simulated events produced from a constellation of alternative models.
The interface to the Professor tool enabled the efficient implementation of complex multi-parameter brute-force scans and removed substantial global analysis limitations by decoupling it from event reweighting procedures that, for all but the most trivial aspects of our physics domain, require substantial development time and are not exact, or even possible at all. Professor 'reduces the exponentially expensive process of brute-force tuning to a scaling closer to a power law in the number of parameters, while allowing for massive parallelisation'  [3]. The Professor package has been extensively used for the tuning of Monte Carlo generators in the collider community. The above developments allowed the GENIE Collaboration to fulfil its dual purpose described in its mission statement: GENIE develops a popular Monte Carlo event generation platform and implements, within its platform, universal and comprehensive physics simulations for lepton scattering, as well as simulations for several Beyond the Standard Model processes.
But, in addition, and separately from the previous mission, GENIE develops a global analysis of scattering data for the tuning and uncertainty characterization of comprehensive neutrino interaction models. The GENIE Generator is the main outlet for the GENIE global analysis results, and our goal is that, for each supported comprehensive model, several selected tuned versions shall be made available. Typically, nuclear modifications to the crosssection are computed separately, and the decomposition of the total cross-section into the possible exclusive final states proceeds via separate hadronization, intranuclear rescattering and particle decay codes. Therefore, bare-nucleon cross-section are a crucial first modelling component to tune in the process of building a global fit of all relevant scattering data.
Tunes for several aspects of GENIE modelling, including neutrino-induced hadronization and nuclear cross-sections for low-multiplicity channels, are near completion and will be released and published in the future. This paper summarises the results of the first analysis performed within the GE-NIE global analysis framework, revisiting the GE-NIE bare-nucleon cross-section tune and, in particular, the tuning of the empirical non-resonance background contribution to one and two pion final states. A similar, albeit much simpler, analysis underpinned the tune of the well known and widely used comprehensive model that was included as the default model throughout the very long GENIE v2 series of releases. At that time, not sufficiently explored and understood tensions between inclusive and exclusive data, and an executive decision to anchor the GENIE v2 model on inclusive data, led to some expected and well known discrepancies with exclusive data that were increasingly brought into focus as new experiments started performing increasingly precise measurements of low-multiplicity, exclusive final states  [4].
A. Datasets included in the fit and their systematics In the current work, we consider Hydrogen and Deuterium data from the ANL 12FT, BNL 7FT, FNAL 15FT and BEBC bubble chamber experiments. The data represent integrated cross sections for different incoming neutrino energy bins for • ν µ and νµ CC inclusive scattering  • ν µ and νµ CC quasi-elastic scattering  [29, 39, 53, [57] [58] [59] [60] [61] [62] [63] [64] [65]  • ν µ and νµ CC single-pion production  [58, [66] [67] [68] [69] [70] [71] [72] [73] [74]  - ν µ + n → µ -+ n + π + -ν µ + p → µ -+ p + π + -ν µ + n → µ -+ p + π 0 -νµ + p → µ + + p + π - -νµ + n → µ + + n + π - • ν µ CC two-pion production [75] -ν µ + p → µ -+ n + 2π + -ν µ + p → µ -+ p + π + + π 0 -ν µ + p → µ -+ n + π + + π - Not all of the available historical data has been used for the fit, as some datasets were superseded or reanalysed, as in the case of ANL 12FT and BNL 7FT, datasets.
The latest analysis are used. A detailed summary of the datasets used in the fit is shown in Tab. II and in Fig. 3. Some of the datasets included in the tune consider Hydrogen-Neon mixtures. The nuclear effects of the neon in the target mixture are shown to be negligible  [76]. Low energy bins have a higher contribution to the χ 2 due to energy smearing and lack of unfolding in measurements. Hence, data points with E ν < 0. 5 GeV are removed from the fit. In total, the tune is performed with 169 data points from bubble chamber experiments. Different analysis methods were implemented in each experiment, such as cuts applied on the W invariant mass, the outgoing muon momentum or the total longitudinal momentum of the final state. The associated GENIE prediction has been corrected by applying the same cuts to the generated events.
Moreover, datasets from the same experiments are not independent as they share the same neutrino flux, detector, analysis methodology, etc. Although it is clear that some correlated uncertainties exist, the data releases do not contain any information about the correlation between them. In the GENIE database, we added a systematic error to the datasets of 15%. The methodology used to include them in the fit is detailed in Sec. V C. Other free nucleon data on heavier targets are available but used only for comparison with the GENIE prediction. No correction for nuclear effects is considered for deuterium targets.
9 GeV [73] νµ + n → µ -+ p ANL 12FT 7 0-2 2 H [57] 8 0-2 (3) Charged current cross section on isoscalar targets as a function of the incoming neutrino energy. Breakdown of quasi-elastic, one and two pion production and deep inelastic processes is shown. The predictions are computed using the G18 02a 00 000 configuration. The data on hydrogen and deuterium targets from Tab. II is shown if available from ANL 12FT ( ), BNL 7FT (•), BEBC ( ) and FNAL ( ). summarises the parameter pre-fit values and the allowed ranges. Previous fits to data are taken into account for the determination of the ranges  [78, 79]. Each of the parameters have a different sensitivity to each dataset, as different scattering mechanisms are involved. The response of each parameter in the inclusive and exclusive cross sections is studied by varying each of them independently within the studied range.
A. Likelihood construction The GENIE integrated cross-section prediction is denoted with σ i th (E k |θ) , where E k is the neutrino energy, θ is a vector  [81]  of the adjustable physics parameters introduced in Sec. IV B, and i is any of the 10 reaction processes considered in the work presented in Tab. II. Using σ i th (E|θ), we produce the corresponding prediction for the k-th energy bin of the j-th dataset for the i-th reaction type, σ ij th (E k |θ) = ε ij (E k , θ) σ i th (E k |θ) (7) where ε ij (E k , θ) are dataset-dependent efficiencies expressing the fraction of events from the i-th process that survive the kinematical cuts imposed by the experiment, see Tab. II. The statistical error due to the MC sample size is also evaluated and this is denoted δσ ij (E k |θ). Performing a multi-parameter brute-force scan and tune using σ ij th (E k |θ) is computationally inefficient.
B. Treatment of systematic uncertainties A number of nuisance parameters, each with a corresponding prior, can also be used to tackle the problem of correlated datasets. As seen in Sec. IV A, there are different datasets coming from the same experiments (ANL 12FT, BNL 7FT, BEBC, and FNAL 15FT). Each of these experiments share the same flux (from either a neutrino or an antineutrino beam), analysis procedure, etc. Therefore, there is a correlation between the datasets, even though it has not been quantified in the data releases. A possible approach is to add nuisance parameters that can connect datasets from experiments that used the same neutrino beam  [82]. As the main systematic uncertainty comes from the fluxes, the nuisance parameters will act as scaling factors for our predictions ( σ ij th (E k |θ)) and are same for datasets sharing the same flux.
Some of the ANL 12FT and BNL 7FT data were already corrected for the flux normalization  [68]. Due to this correction, the associated systematic error is smaller and, accordingly, a more restricted nuisance parameter is applied to the re-analysed datasets. These restricted parameters take into account other common systematics like reconstruction procedures, so they multiply all the predictions related to the same experiment. At the end of this procedure, each prediction can be scaled by up to 2 nuisance parameters, one for the flux and one for the remaining systematics. Thus, a total of 9 independent nuisance parameters are used to account for the correlation between datasets. They are all the available combinations of experiment and neutrino flux exposure (ν µ and νµ ) plus the restricted parameters for re-analysed data: f = f ANL (ν µ ), f ANL Re (ν µ ), f BNL (ν µ ), f BNL Re (ν µ ), f BNL (ν µ ), f BEBC (ν µ ), f BEBC (ν µ ), f FNAL (ν µ ), f FNAL (ν µ ) Quasi-elastic data for hydrogen and deuterium targets is included in the tune in order to constrain the nuisance parameters.
C. Discussion of priors The likelihood is corrected using priors on parameters of interest (θ) and nuisance parameters (f ). Priors allow us to incorporate in this analysis the appropriate pre-fit uncertainties and correlations for the parameters of interest. Only Gaussian priors are considered at present. The priors applied to each nuisance parameter f j have a peak at 1 and different standard devi-TABLE (IV) Nuisance parameters, f j , per experiment (ANL 12FT, BNL 7FT, BEBC or FNAL 15FT) and neutrino beam (ν µ or νµ ). Priors consider the systematic uncertainty applied to each dataset as δf j , where j is one of the datasets under study. The allowed range is [0, 2] for nuisance parameters considered in the tune. Parameter Prior f ANL (νµ) 1 ± 0. 14 f ANL Re (νµ) 1 ± 0. 05 f BNL (νµ) 1 ± 0.
14 f BNL Re (νµ) 1 ± 0. 05 f BNL (νµ) 1 ± 0. 15 f BEBC (νµ) 1 ± 0. 15 f BEBC (νµ) 1 ± 0. 15 f FNAL (νµ) 1 ± 0. 15 f FNAL (νµ) 1 ± 0. 15 ations δf j. In general the total scaling factor applied to non-re-analysed datasets are constrained by a conservative 15 % δf Gaussian prior, except for those nuisance parameters that act on the same experiment. Thus, the BEBC and FNAL 15FT experiments have only one associated scaling factor δf BEBC = δf FNAL = 0. 15 for both neutrino and anti-neutrino fluxes; the same is true for f BNL (ν µ ). Up to two nuisance parameters can be applied to ANL 12FT and BNL 7FT data (i. e. f AN L (ν µ ) and f AN L Re (ν µ )). The ANL 12FT and BNL 7FT restricted nuisance parameters, f ANL Re (ν) and f BNL Re (ν), have δf = 5%. δf ANL and δf BNL are such that ANL 12FT and BNL 7FT non-re-analysed datasets data are constrained by an overall 15% Gaussian.
This would not be the case otherwise as the SIS region data would prefer much higher cross-section values for the DIS contribution. The prior on S DIS provides a good solution for this problem because the degeneracy between DIS and non-resonant background parameters gives us multiple ways to accommodate good agreement between data and GENIE predictions in the SIS region. In other words, the introduction of the S DIS prior breaks the degeneracy without adding more datasets to the fit.