dataesr/scientific-paragraphs-categorization huggingface dataset:
SED modelling with CIGALE To model the spectral energy distribution (SED) of our galaxy sample, we use the SED modeling code CIGALE  It uses Bayesian-like analysis to derive the physical properties of galaxies. Its versatility is characterized by the multiple modules modeling the SFH of galaxies, the stellar, dust , and nebular emission, the AGN contribution, as well as the radio emission of galaxies. In particular, the SFH can be handled through analytic, non-parametric, and simulated SFHs  (Boquien et al. 2014; Ciesla et al. 2015 Ciesla et al. , 2017)). In this work, we use the recently added non-parametric SFH module sfhNlevels  (Ciesla et al. 2022) ,  Bruzual & Charlot (2003)  stellar population models, a  Calzetti et al. (2000)  attenu-ation law, and  Dale et al. (2014)  dust emission library.
Non-parametric SFH: the sfhNlevels module In this CIGALE module, the SFH is modeled using a given number of bins with constant SFR. The SFRs of two consecutive bins is compelled by a given prior. In this work, we use a continuity prior following a Student-t distribution  (Leja et al. 2019). A given number of SFH (N SFH ) is computed by randomly selecting the SFR in each bin from the distribution imposed by the prior. This module includes the computation of a parameter called the SFR gradient which characterizes the evolution of a galaxy over the SFR-M * plane. This parameter is defined as an angle (in degrees) and describes the direction of evolution of a galaxy in a given time interval ∆t = t 2t 1 where the SFR changes by ∆log 10 SFR and the mass by ∆log 10 M * : ∇SFR ∆t = arctan ∆log 10 SFR ∆log 10 M *.
Mock analysis To assess the reliability of the parameters derived with CIGALE, we build a mock catalogue mimicking the data sample. For each galaxy of the sample, we simulate mock fluxes from the best fit of their observed fluxes: we start with the flux densities obtained by integrating the best-fit template over the same set of filters as the original one. These mock flux densities are then perturbed by adding a noise randomly selected in a Gaussian distribution with a standard deviation σ corresponding to the error of the original flux density. By running the code on this synthetic sample, for which all the physical parameters are known, we can compare the exact values of the physical parameters and the retrieved ones. This test provides an evaluation of the ability of the code to provide constrained values of the physical parameters we want to study (for instance,  Noll et al.
Possible remaining AGN contamination? As described in  Sect. 2. 2. 1, Jiménez-Andrade et al. (2019)  thoroughly applied a set of criteria to remove AGN host galaxies from their sample of pure star-forming galaxies. However, we add to this a last test fitting the galaxies using the AGN modeling module of CIGALE, skirtor  (Stalevski et al. 2016 ). This CIGALE module implements a modern clumpy two-phase torus model to compute the UV-to-IR SED model of AGN (see  Yang et al. 2020; Buat et al. 2021, for further information). From this SED modelling run, we collect the instantaneous SFR as well the AGN fraction,frac AGN , which quantify the AGN contribution to the total IR luminosity (L IR ). Fig. 8  shows the ratios between SFR Inst SED /SFR radio , color-coded with frac AGN. As a results, 90% of our galaxy sample have a frac AGN lower than 15% (black dots) which is compatible with no AGN presence detected from the UV to IR multi-wavelength data (Ciesla   2 We can not produce a figure similar to Fig.
Conclusions and perspectives We used a mass-complete sub-sample of star-forming galaxies drawn from the VLA 3 GHz project, and described in Jiménez-Andrade et al. (  2019 ), to investigate the difference between the SFR derived from radio observations and the one derived from UV-to-IR SED modeling. We used the SED modeling code CIGALE that includes the new non-parametric SFH models  (Ciesla et al. 2022). The results of our analysis can be summarized as follow: -Approximately 30% our galaxies present a radio SFR ten times larger than the instantaneous SED SFR. This trend affects primarily the galaxies that show a declining SFH activity over the last 300 Myr, i. e. on their way to quench. -By averaging the SFH over different timescales, we found that both SFR indicators converge toward a consistent value, when the SFHs obtained from the best fits are averaged over a period larger than 150 Myr to derive SFR SED.
Acknowledgements. We thank the referee for his/her comments which helped improve the paper. We also thank  Alessandro Boselli ,  David Elbaz , and  Vernesa Smolčić  for insightful ideas and discussions. This project has received financial support from the  CNRS  through the  MITI interdisciplinary programs. We warmly acknowledge the contributions of the entire COSMOS collaboration consisting of more than 100 scientists. The  HST-COSMOS program  was supported through  NASA  grant  HST-GO-09822. More information on the COSMOS survey is available at https://cosmos. astro. caltech. edu. This research is also partly supported by the  Centre National d'Etudes Spatiales (CNES). OI acknowledges the funding of the  French Agence Nationale de la Recherche  for the project  iMAGE  (grant  ANR-22-CE31-0007 ).
A broadband white probe beam with an energy range of 1. 5-3. 5 eV and repetition rate of 2 kHz was used to measure the changes in the optical properties due to the pump pulse. Figure  1 (a) shows a schematic of the normalincidence experimental setup used for optical pump-probe studies. The pump beam was focused to a spot size of 125 μm at the sample resulting in an energy density of 15 mJ/cm 2. The probe beam had a smaller spot size of 50 μm. Differential reflectivity data were measured by varying the time delay between pump and probe from 0 to 6400 ps. It should be noted that the differential reflectivity signal was normalized to the ground state reflectivity to obtain ΔR/R 0,exp , presented throughout the article.
Ground and transient state (Probe) beam reflectivity of the thin films and heterostructures were calculated using "A Layered Thin Film Transmission and Reflection Coefficient Calculator" Matlab code. [10]  This software calculates the complex reflection and transmission coefficients of a multilayer stack based on the Fresnel equations. This software is based on method described by Pascoe  [16]  and we present a brief summary here. The method utilizes characteristic matrices to determine the reflectivity and transmissivity through an arbitrary number of film layers. This method considers that the propagation of plane waves (probe beam) incident on the infinite planar surface (i. e. , film) through a lossless medium (i. e. , air) can be described using a set of characteristic matrices which take into account the thickness and material properties of each layer, as well as the incident angle.
Here, subscripts 1 and L refer to the first and the last (substrate) layers, η and m ij are same as defined above. As the aforementioned parameters (ε, σ, µ, …) are functions of the optical constants, this method was used to calculate reflectivity throughout the article as a function of n and k (see additional details in supplementary section S2). (2) M total = M 1 M 2 M 3 • • • = In order to calculate the modification of n and k (Δn and Δk), we redefined the calculated transient reflectivity, introduced in the previous section, as a multivariable symbolic function. This function is dependent on parameters such as the probe energy, n and k for each layer, ground state reflectivity, and layers' thicknesses. The fitting variables (Δn and Δk) were added to n and k of the PZT layer. This function was then used to fit the experimental transient reflectivity, at each time delay, using multivariable lsqcurvefit algorithm in Matlab.
e. , ~ 0. 35 ps). Moreover, at longer time delays (> ~ 4 ns) the amplitude of the calculated spectral features shows good agreement with the experimental results. The differences between the measured and the calculated dataset for the first ~ 0. 35 ps following laser excitation, could be due to other factors which dominate at shorter timescales such as carrier excitation, shift currents and thermal stabilization of carriers. [2, 18]  We also note that the amplitude and spectral features of the calculated spectra matched well with the experimental dataset at lower energies, while at higher energies the spectral features are off by about 0. 3 eV. This will be further discussed in the "Discussion" section. However, the simple calculations do reproduce key spectral features in ΔR/R 0 as shown in Fig.
From the results shown in Fig. 3 , we can retrieve the photoinduced changes in n and k, i. e. , Δn and Δk, as a function of time for the entire delay range. Figure  4  presents the photoinduced changes in n [Fig. 4 (a), (b)] and k [Fig. 4(d), (e) ] as a function of delay for all three samples, 100 nm PZT (black), 200 nm PZT (red), and ITO/ PZT/SRO (blue) heterostructure. The supplementary section S4 presents the same datasets in logarithmic scale for clarity. The evolution of n and k were then fitted using exponential temporal fits which included three exponentials (Eq. 4) to capture fast recovery, medium recovery, and slow recovery. Fits using different decay components, i. e. , two or four exponentials were also performed, but ultimately three exponentials were used as they resulted in best fit to the data with the lowest number of degrees of freedom in the fit (see supplementary Section S5 for comparison between multiple decay components).
Acknowledgments The work at  UC Davis  was supported by  National Science Foundation  ( DMR-1902652 ). Research at the  Center for Nanoscience  and Nanotechnology was supported by the  French RENATECH  network and by the  French National Research Agency (ANR)  -Project  UP-DOWN  (Project No. ANR-18-CE09-0026-04 ). Work performed at the  Center for Nanoscale Materials , a  U. S. Department of Energy Office of Science User Facility , was supported by the  U. S. DOE, Office of Basic Energy Sciences , under Contract No. DE-AC02-06CH11357.
Molecules were polarized by 2 ms-duration microwave pulses. The Free Induction Decay (FID) signals were detected using a heterodyne detection at 30 MHz and digitized at a sampling rate of 120 MHz. A fast Fourier transform of the FID signal allowed to obtain the spectral domain signal, where transitions were observed as Doppler doublets due to the coaxial arrangement of the jet and the Fabry-Perot cavity. The frequency grid, linked to the number of data points, was set to 1. 8 kHz representing the line accuracy.
Theoretical calculations First, a conformational search was made using molecular dynamic Monte-Carlo based-method (MCMM) with extended torsional sampling parameters, aiming to investigate all possible structures. In order to select the obtained structures, electronic structure calculations were performed using Gaussian16 rev C. 01 software  29  on the PhLAM laboratory high performance computing cluster. Equilibrium structures of each conformer were optimized using density functional theories (DFT), namely B3LYP  30, 31  and B98  32  , with respectively Pople split-valence triple-zeta basis set 6-311++G(d,p)  33  and Dunning's correlation consistent basis set aug-cc-pVXZ (X = T, Q). 34  Structures were also optimized ab initio using the second-order Møller-Plesset perturbation theory  35  (MP2) with Dunning's correlation consistent triple zeta basis set aug-cc-pVTZ.
Monohydrate species of a-naphthaldehyde The cis-a-I conformer is a nearly prolate (k = À0. 710) asymmetric rotor, with a strong dipole moment along the a-axis Thus, scans around the predictions, based on the theoretical constants, for the a-type transitions of the R-branch were conducted to observe lines corresponding to the characteristic pattern of a nearly prolate species with groups of lines separated of about B+C. After optimizing the experimental conditions (carrier gas pressure, vaporization temperature and polarisation power), we obtained a set of low intense transitions that follows that pattern. Those lines were fitted to a semi-rigid rotor Watson Hamiltonian in the A-reduction in the I r representation 44 using the SPFIT/ SPCAT software package. 45 Then, an iterative process of predicting-assigning further lines was followed to include finally up to 112 a-and b-type transitions in the analysis (J max = 20, K a,max = 5) reproduced at instrumental accuracy (RMS error of 1.
6 kHz). Results of the fit are shown and compared to calculated values in Table  1  and the list of assigned transitions can be found in Table  S8  of the ESI. † The good agreement between theoretical and experimental rotational constants clearly indicates that the observed monohydrate corresponds to the cis-a-I structure, for which coordinates are provided in Tables  S2  and S3  of ESI. † Further explorations were conducted to find higher energy monohydrates, particularly cis-a-II, without success. In order to estimate our detection level, the signal-to-noise ratio (SNR) of the most intense line observed for cis-a-I J 19  18 0;18 was measured. Then, the ratio between its calculated intensity and the one of the corresponding line for cis-a-II (both out of SPCAT program at a rotational temperature of 10 K) has been devided by the measured SNR.
Acknowledgements We acknowledge support by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ): grant  BR 4000/7-1  and by the  GSI-IN2P3  agreement under contract number  13-70. This project has, furthermore, received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  824093  ( STRONG-2020 ). Also we thank the  COST Action THOR ,  CA15213. The computational resources have been provided by the  LOEWE-Center for Scientific Computing  and the " Green Cube" at GSI, Darmstadt.
We conduct a systematic search for transients in 3 yr of data (2017-2019) from the Atacama Cosmology Telescope (ACT). ACT covers 40% of the sky at three bands spanning from 77-277 GHz. Analysis of 3 day mean-subtracted sky maps, which were match filtered for point sources, yielded 29 transient detections. Eight of these transients are due to known asteroids, and three others were previously published. Four of these events occur in areas with poor noise models and thus we cannot be confident they are real transients. We are left with 14 new transient events occurring at 11 unique locations. All of these events are associated with either rotationally variable stars or cool stars. Ten events have flat or falling spectra indicating radiation from synchrotron emission. One event has a rising spectrum indicating a different engine for the flare.
Data The Atacama Cosmology Telescope (ACT) was a 6 m offaxis Gregorian telescope located in the Atacama Desert in Chile at an altitude of 5200 m  (Fowler et al. 2007). AdvACT was the third-generation ACT instrument. It simultaneously housed three optics tubes with a total field of view spanning ∼1°. 5  (Thornton et al. 2016) , each containing a set of lenses, low-pass filters, and a single AdvACT detector array  (Ho et al. 2017; Choi et al. 2018 ). The ACT data set considered here covers the three bandpasses f090 (77-112 GHz), f150 (124-172 GHz), and f220 (182-277 GHz) using the three dichroic polarization-sensitive transition edge sensor bolometer arrays PA4 (f150+f220), PA5 (f090+f150), and PA6 (f090 +f150). The beams' FWHM are 2 0, 1 4, and 1′, respectively, for bands f090, f150, and f220.
During observations, the telescope scanned the sky in azimuth at a fixed elevation at a scan speed of ∼1. 5°s -1. When the sky was rising, it took ∼6 minutes for a point on the sky to be gradually swept across by the detectors in PA4 and PA5. Then, after ∼3 minutes this process would repeat for the detectors in PA6. This order was reversed when the sky was setting. The bolometer signals, i. e. , measured optical power fluctuations, were stored in time-ordered data (TOD) files, each containing a roughly 10 minute time series for all detectors in a single array. A selection process was applied to TOD data to cut off defective detectors or dark detectors that were not optically coupled to the sky signal. The data were then calibrated to sky temperature fluctuations using calibration observations on planets with known temperature models, such as Saturn and Uranus.
Counterparts We searched for counterparts for each transient event in the SIMBAD 34  (Wenger et al. 2000)  database. The results of this search are summarized in Table  5. We searched for any Gaia objects  (Mou & Webster 2021)  within 1′ of each event and list the SIMBAD identification. We calculate the probability of a chance association given the density of Gaia stars  (Mou & Webster 2021)  with the counterpart's magnitude or lower within the counterpart's separation of the transient's position. We exclude any counterparts with a chance association probability greater than 10%. We also searched for counterparts of the transients found in  Naess et al. (2021a) , which are recovered in this analysis. The associations agree with the original findings. In addition to the Gaia source catalog, we search for galactic objects in the Gaia extragalactic catalog  (Mou & Webster 2021)  using VizieR  (Ochsenbein et al.
Discussion and Conclusions We have demonstrated a pipeline of transient detection and characterization using ACT data, and presented a catalog of 21 mm transients from 3 day maps, including three of which were previously detected, and four events that cannot be confirmed as real transients. After assessing the spectral indices and counterpart associations of these events, two classes of objects emerge: flares associated with stars with flat or falling spectra, indicating radiation from synchrotron emission, and stellar flares with rising spectra, indicating thermal emission. Note. The chance of a false association is calculated using the density of Gaia sources with the same magnitude or brighter of the counterpart. If the Gaia object is not found in the SIMBAD database, then the Gaia identification number is listed.
Acknowledgments The Authors would like to thank  Carles Badenes  for the helpful discussions and comments. This work was supported by the  U. S. National Science Foundation  through awards  AST-0408698 ,  AST-0965625 , and  AST-1440226  for the  ACT  project, as well as awards  PHY-0355328 ,  PHY-0855887 , and  PHY-1214379. Funding was also provided by  Princeton University , the  University of Pennsylvania , and a  Canada Foundation for Innovation (CFI)  award to  UBC. ACT operates in the  Parque Astronómico Atacama in northern Chile  under the auspices of the  Agencia Nacional de Investigación y Desarrollo (ANID ; formerly  Comisión Nacional de Investigación Científica y Tecnológica de Chile , or  CONICYT). Computations were performed using  Princeton Research Computing  resources at  Princeton University.
This research has made use of data and/or services provided by the  International Astronomical Union's Minor Planet Center. This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France. Y. L. acknowledges support from the  KIC Postdoctoral Fellowship. This work was supported by a grant from the  Simons Foundation  ( CCA 918271 , PBL). E. C. acknowledges support from the  European Research Council (ERC)  under the  European Unionʼs Horizon 2020 research and innovation program  (grant agreement No. 849169 ). S. K. C. acknowledges support from  NSF  award  AST-2001866. R. D. thanks  CON-ICYT  for grant  BASAL CATA FB210003. C. H. C. acknowledges the  FONDECYT Postdoc  fellowship  3220255. A. D. H. acknowledges support from the  Sutton Family Chair in Science, Christianity and Cultures  and from the  Faculty of Arts and Science, University of Toronto.
Acknowledgments We would like to thank  Asaad Elkhidir ,  Aidan Herderschee ,  Donal O'Connell ,  Radu Roiban ,  Matteo Sergola ,  Fei Teng  and  Ingrid Vazquez-Holm  for coordination on ongoing work. We thank  Congkao Wen  for initial collaboration on this project and interesting discussions on related topics,  Fabian Bautista ,  Brando Bellazzini ,  Donato Bini ,  Alessandra Buonanno ,  Thibault Damour ,  Riccardo Gonzo ,  Pavel Novichkov ,  Jan Plefka  and  Rodolfo Russo  for several interesting conversations,  Zheyong Fan  for aid on numerical integrations,  Roman Lee  for help with the LiteRed2 package, and  Alex Owen  for computer assistance. AB, GB, JG and GT would like to thank the  Kavli Institute for Theoretical Physics at the University of California, Santa Barbara , where their research was supported in part by the  National Science Foundation  under Grant No.
PHY-1748958. This work was supported by the  Science and Technology Facilities Council (STFC)  Consolidated Grants  ST/P000754/1  "String theory, gauge theory & duality" and  ST/T000686/1  "Amplitudes, strings & duality", and by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No. 764850  "SAGEX". The work of GRB and JG is supported by an  STFC quota studentship. GC has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No. 847523  "INTERACTIONS". SDA's research is supported by the  European Research Council , under grant  ERC-AdG-885414. This research utilised  Queen Mary's Apocrita HPC facility , supported by  QMUL Research-IT.
5 GeV. Events selected by the hadron trigger or by any other trigger independently of the signal candidate are used in this analysis. The software trigger requires a two-, three-or four-track secondary vertex with a significant displacement from any primary pp interaction vertex. At least one charged particle must have a transverse momentum p T > 1. 6 GeV/c and be inconsistent with originating from a PV. A multivariate algorithm  [15, 16]  is used for the identification of secondary vertices consistent with the decay of a b hadron. Simulation is required to calculate reconstruction and selection efficiencies, and to determine shapes of invariant-mass distributions. In the simulation, pp collisions are generated using Pythia  [17]  with a specific LHCb configuration  [18]. Decays of unstable particles are described by EvtGen  [19] , in which final-state radiation is generated using Photos  [20].
Acknowledgements We express our gratitude to our colleagues in the CERN accelerator departments for the excellent performance of the LHC. We thank the technical and administrative staff at the LHCb institutes. We acknowledge support from  CERN  and from the national agencies:  CAPES ,  CNPq ,  FAPERJ  and  FINEP (Brazil) ;  MOST  and  NSFC (China) ;  CNRS/IN2P3  (France);  BMBF ,  DFG  and  MPG (Germany) ;  INFN (Italy) ;  NWO (Netherlands) ;  MNiSW  and  NCN (Poland) ;  MEN/IFA (Romania) ;  MICINN (Spain) ;  SNSF  and  SER (Switzerland) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  DOE NP  and  NSF (USA). We acknowledge the computing resources that are provided by  CERN ,  IN2P3 (France) ,  KIT  and  DESY (Germany) ,  INFN (Italy) ,  SURF (Netherlands) ,  PIC (Spain) ,  GridPP  ( United Kingdom ),  CSCS (Switzerland) ,  IFIN-HH (Romania), CBPF (Brazil) ,  Polish WLCG (Poland)  and  NERSC (USA).
International collaborations are important for developing science systems. Using a dataset of South African university scientists, we ask whether social capital relevant to international collaboration held by one scientist spills over to local colleagues. Distinguishing between different ways of acquiring foreign ties, we find that 20% of our cases resemble the most-studied form of international collaborations, via the unique ties of an individual with specific characteristics, e. g. , foreign research training. In all other cases, both personal and local peers' international social capital is relevant for foreign tie formation. Underlining the systemic functioning of science, international social capital is activated through scientific collaboration among local scientists. The mediating effect of local scientific collaboration is present across all scientific fields and holds for scientists trained locally or abroad.