dataesr/scientific-paragraphs-categorization huggingface dataset:
HST For high-resolution imaging of A370, we use multiband HST data observed in the Hubble Frontier Fields (HFF) surv e y  (Lotz et al. 2017  ) and its successor, the Beyond Ultra-deep Frontier Fields And Le gac y Observations (BUFFALO;  Steinhardt et al. 2020 )  project. Both programs target the same six lensing clusters, though their observation strategies differ in scope: The A370 HFF data consist of 160 HST orbits in seven broad-bands (F435W, F606W, F814W, F105W , F125W , F140W , and F160W) and are largely stacked o v er the field of view of a single instrumental pointing (ACS for the optical bands and WFC3 for the IR bands). This provides incredibly deep imaging (a 5 σ point-source limiting magnitude of 29 in all bands) o v er the crowded central core. Conversely, the BUFFALO data are shallower (16 orbits in total; limiting magnitude ∼27.
In particular, the astrometric precision of each frame Newly acquired shallow (1-h depth) MUSE data appear as numbered green boxes, nearly surrounding the cluster centre. The footprint of previously obtained deep MUSE data (2-8 h depth) is shown as a white contour. We subsequently combine these two data sets to create a contiguous region of spectroscopic co v erage spanning 14 arcmin 2. The coloured image shows the combined Hubble Space Telescope co v erage of A370, using F606W/F814W/F160W filters. Like the spectroscopic footprint, the imaging region also shows a deep core (the Hubble Frontier Field region) and shallower outskirts (the BUFFALO region). is refined to an uncertainty of less than one milliarcsec and geometric distortions are mapped and rectified. In addition an updated estimate of background flux is measured and remo v ed from each frame and an impro v ed cosmic ray rejection algorithm is applied  (Koekemoer et al.
Acquisition The MUSE data used in this work were obtained in ESO program 0102. A-0533(A) (PIs: F. Bauer and D. Lagattuta) and designed to complement and expand the previous deep observations of the A370 core (2-8 h exposure time per pointing) used in L17 and L19 : programs 094. A-0115(A) (PI: J. Richard) and 096. A-0710(A) (PI: F. Bauer). In contrast to the core data, these new fields are shallower; each position is co v ered by a single 1-h Observation Block (OB) with an ef fecti v e e xposure time of 45 min when accounting for o v erheads. Ev ery OB consists of four ∼11-min exposures, the first of which has a PA of 29 deg (east of north) to match the orientation of the deep observations. Subsequent exposures are rotated 90 deg clockwise relative to their predecessors and include a small 0.
Object detection and extraction To maximize the number of redshift candidates we can identify, we use two different techniques to detect sources in the field. Ultimately, both methods utilize SOURCE EXTRACTOR ( SEXTRACTOR ;  Bertin & Arnouts 1996 )  for object detection, but each targets the data in a different way. In the first method, we take advantage of the BUFFALO imaging to select objects that have broad continuum emission (i. e. those that are visible in the HST data). Since we aim to collect as many candidates as possible, we first create median-subtracted versions of the BUFFALO images by subtracting the local background flux from each pixel. We take the background to be the median flux in a 1. 5 × 1. 5 box surrounding a given pixel. This creates a better contrast around faint galaxies that may otherwise be o v erwhelmed by the light of their brighter neighbours, without affecting the cores of these bright galaxies, which are still easily detectable.
To account for the differences in image depth, we also include HST weight maps (also cropped to match the MUSE footprint) in our SEXTRACTOR analysis. In the second method, we run SEXTRACTOR on the MUSE data directly, using the MUSELET routine available in MPDAF. To operate, MUSELET first creates a copy of the cube, replacing the data in each wavelength slice with its equi v alent 'pseudo-narro w-band': the program averages the flux in a narrow wavelength range (centred on the current slice) to make a 'source', then subtracts the average flux measured from the combined regions just redwards and bluewards of the source to remo v e the 'continuum'. This ef fecti vely identifies e xcess flux abo v e the local continuum level, and is an efficient way to detect emission-line objects. Both the source and continuum parameters are scalable in MUSELET , and in this work we set the source wavelength range to be δ λ ≤ 2.
5 Å (or five slices in total) and the continuum range to be 25 Å (or 20 slices) both bluewards and redwards of the source. After creating the narrow-band cube, the software runs SEXTRACTOR on each slice to identify emission peaks and, after combining peaks at different wavelength slices that are spatially 'close' to one another (i. e. within the same seeing disc) into 'multi-line objects', outputs a list of unique detections in the cube, which we call muselet sources. After generating both prior and muselet sources, we extract a spectrum for each object based on the SEXTRACTOR segmentation map created in the detection step. For prior sources we first expand the detection region to account for differences in the MUSE pixel scale and convolve the HST -based segmentation map with the MUSE PSF.
Because the PSF varies as a function of wavelength, we actually create different convolution maps at ten 'anchor' wavelengths (evenly spaced at 500 Å intervals throughout the MUSE spectral range) and interpolate the results o v er the entire data cube. Conversely, for the muselet sources we simply take the SEXTRACTOR detection map of the object's brightest emission line, but projected o v er all wavelengths. In both cases, we then sum all of the spaxels covered by the new detection footprint, weighted using the optimal Horne ( 1986 ) algorithm, to create an initial 1D spectrum. To remo v e an y remaining systematics due to sky variation or the extended flux of bright neighbours, we also estimate a local sky spectrum by summing the 500 nearest 'blank' spax els (spax els that are not associated with any detection object in the field) that fall within a 0.
SOURCE INSPECTOR also highlights any other extracted sources within a ∼ 10 arcsec box centred on the current target so the user can easily identify nearby contaminants or cross-match priorand muselet -identified sources. When the user is satisfied with a redshift choice for a given object, the entry is saved into a data base and the next target object is shown. Once all members of a team observe their object list, the team meets to compare results and resolve any conflicts. These can include disagreements o v er the identified redshift, the confidence in the chosen redshift (from low to high), or matches between nearby sources. Final parameters for each object are chosen, and a 'resolved' redshift list is recorded (again using SOURCE INSPECTOR ) and sent for final combination. In this last step, we combine all lists and do a final cross-check to eliminate any duplicates that exist between different lists or between the new lists and the original redshift catalogue from L19.
We optimize the lens model using the publicly available LENSTOOL software. During model optimization, we fix the centroid of the halo to the observed position of the cluster member, and following previous modelling efforts (e. g. L17 and L19 ) we also fix the core radius to be 0. 15 kpc. The remaining six parameters ( θ , ε, σ 0 , r cut , , and θ ) are allowed to freely vary. The resulting fit is excellent, with almost negligible rms residuals between the predicted and observed positions of multiple-image constraints ( < 0. 03). We present the final best-fitting model parameters in Table  1. With this model we then make use of the public software FRAPY  (Patr ício et al. 2019 )  to adjust the velocity map with a parametric model of a rotating disc accounting for the lensing distortion (Fig.
Similar to  Granata et al. ( 2022 )  we are also integrating cluster member velocity dispersion measurements into the model, allowing us to reduce our reliance on luminosity-based scaling relations in fa v our of the more robust Fundamental Plane  (Mahler et al. , in preparation). Furthermore, using the full MUSE data set as a template we are beginning to calibrate background galaxy distributions more accurately in weakand combined strong + weak-lensing models, providing important impro v ements to BUFFALO science outputs. Besides modelling, the high density of spectroscopy combined with HST -resolution imaging makes this data set a valuable resource for studying elements of galaxy evolution, especially for the significant background galaxy sample at medium and high redshifts ( z > 1). Along similar lines, the spectra of the complete data set (which we are making publicly available 4  ) will add to the growing library of SED-fitting standards, and in particular our investigation into the luminosity and spatial distributions of spectroscopically confirmed red and blue cluster members can serve as a template for generating more accurate cluster-member selection functions in similar clusters without comprehensive spectroscopic coverage.
This suggests that there is need for additional co v erage, and highlights the value in continuing our campaign to expand the A370 footprint. Broader spectroscopic co v erage would be especially helpful: even though the 14 arcmin 2 MUSE footprint presented here is considerably larger than any of its predecessors, it accounts for only 40 per cent of the primary A370 BUFFALO field -there is still significant ground to co v er. Extending the MUSE mosaic to fill the BUFFALO field of view would maximize the ef fecti veness of our data combination, allowing us to use the analysis techniques outlined in this paper o v er a ∼32 arcmin 2 region of the sky, reaching distances ∼1. 3 Mpc from the projected cluster centre. This would easily encompass all of the existing GGL candidates, and possibly reveal other hidden systems (much like GGL-F) that are not readily apparent in imaging alone.
Likewise, by identifying cluster members to the edge of the BUFFALO footprint, we can continue quantifying the physical properties of galaxies out to even larger radii, and also push the caustic method analysis into the predicted range of r 500 , providing for a more accurate estimate of M 500 and M 200 ; this would allow us to directly compare our enclosed mass contours to complementary techniques (such as X-ray gas temperature and weak lensing) that provide mass estimates at similar cluster-centric distances. We note that spectroscopic co v erage will be particularly useful for validating the redshifts of all galaxies in the outermost regions of the BUFFALO footprint, since the imaging in these areas lack significant highresolution IR co v erage, limiting photo-z ef fecti veness. Wider co v erage is not the only way we can enhance our view of A370, ho we v er, as deeper MUSE data will also impro v e the results.
Following the arguments outlined in the previous few paragraphs, BUFFALO-WINGS would co v er the full HST footprint of each BUFFALO cluster, using at least two OBs of MUSE data in each pointing. This would significantly expand the capabilities of this already promising pilot study, and with an estimated ∼8000 redshifts, would stand as a powerful le gac y product in its own right. While this would require a non-trivial amount of telescope resources, this work demonstrates such an effort would be well spent, providing considerable benefit to the larger astronomical community. At the very least, the high density of WINGS spectra will considerably impro v e our understanding of cluster member populations, providing a more accurate selection function for identifying cluster members with traditional/MOS instruments such as Hectospec (even though, by definition, this co v erage would still be incomplete compared to the IFU).
Figure1. Data of the Abell 370 (A370) cluster used in this work. Newly acquired shallow (1-h depth) MUSE data appear as numbered green boxes, nearly surrounding the cluster centre. The footprint of previously obtained deep MUSE data (2-8 h depth) is shown as a white contour. We subsequently combine these two data sets to create a contiguous region of spectroscopic co v erage spanning 14 arcmin 2. The coloured image shows the combined Hubble Space Telescope co v erage of A370, using F606W/F814W/F160W filters. Like the spectroscopic footprint, the imaging region also shows a deep core (the Hubble Frontier Field region) and shallower outskirts (the BUFFALO region).
ACKNOWLEDGEMENTS We thank  S. Fortier  for his assistance with the experiments. L. T. acknowledges support from the  Canadian Institute for Advanced Research (CIFAR)  as a CIFAR Fellow and funding from the  Institut Quantique , the  Natural Sciences and Engineering Research Council of Canada  (PIN: 123817 ), the  Fonds de Recherche du Québec -Nature et Technologies (FRQNT) , the  Canada Foundation for Innovation (CFI) , and a  Canada Research Chair. J. M. is supported by the  Slovenian Research Agency (ARRS)  under Program No. P1-0044  and Project No. J1-2458 ,  N1-0088 , and  J1-2455-1. J. S. Z. was supported by  NSF MR-SEC  under Cooperative Agreement No. DMR-1720595. This research was undertaken thanks in part to funding from the  Canada First Research Excellence Fund  and the  Gordon and Betty Moore Foundation's EPiQS Initiative  (Grant  GBMF5306  to L.
DEOS single-shot recorder: experimental strategy and novel theoretical framework Our experimental strategy is displayed in Figure  1a. The DEOS optical setup is extremely close to classical chirped pulse electro-optic sampling systems  [9, 10, [24] [25] [26] [27] [28] [29] , except few -though crucial -modifications. The electric field under interest E(t) is imprinted in singleshot onto a chirped laser pulse. Then we record the optical spectra in single-shot at the two polarizer outputs. If we assume a linear chirp, the optical frequency ω opt is related to the input time t by: t = - ω opt C ( 1 ) where C is the chirp rate of the laser pulse. The optical spectra provide the input data of our retrieval algorithm. The spectra are first preprocessed in a classical way (background subtraction, amplitude scaling and wavelength to time conversion (see Fig.
Data anaysis in the table-top experiment Main data consist of single-shot camera images taken in presence of the electric fied. This image contains the three spectra S 1n (λ), S 2n (λ) and S 0n (λ). In addition we also record beforehand the same data in absence of electric field, which we will write S ref Y 1n = S 1n σ 1 S 0n -1 (17) Y 2n = S 2n σ 2 S 0n -1 (18) where σ 1,2 = S ref 1,2 S ref 0. This preprocessing strategy also allows us to suppress the effect of shot-to-shot fluctuations of the laser spectrum shape. In other words, Y 1,2n do not depend on the shape of S 0n. This can be shown easily, if we assume a fixed relation between the three spectra: S 0,1,2n (λ) = f 0,1,2 (λ)S Ln (λ), with S Ln (λ) the laser spectrum at shot n, and assume that f 0,1,2 (λ) are functions that do not change with time (i.
Data analysis in the EuXFEL experiment With each burst, we record single-shot spectra on one of the two polarization directions S 1n (λ), S 2n (λ) and additionally unmodulated spectra with laser and no electron bunch S no bunch 1,2 , and the spectra without laser S dark 1,2. However as we do not record the unmodulated laser spectra S 0n (λ) of the same laser pulses in the XFEL experiment, we cannot compensate for the shot-to-shot fluctuations of the laser faster than about 2kHz. The data analysis is then similar to the 800 nm experiment case. We thus define the EO signals (before applying the MRC reconstruction) as: Y 1n = S 1n -S dark 1 S no bunch 1 -S dark 1 -1 (19) Then we apply the MRC algorithm in exactly the same way than for the previously described table-top (800 nm) experiment. analysis has been performed by ER and SB (table-top experiments), and ER, BS, CS, and SB (FEL experiments).
ACKNOWLEDGEMENTS This work has been supported by the following funds:  Ministry of Higher Education and Research, Nord-Pas de Calais Regional Council and European Regional Development Fund (ERDF)  through the  Contrat de Plan État-Région  (CPER photonics for society), LABEX  CEMPI  project ( ANR-11-LABX-0 007 ),  ME-TEOR CNRS MOMENTUM  grant,  ANR-DFG   ULTRA-SYNC  project ( ANR-19-CE30-0031 ). This work also used the PhLAM femtosecond laser facility in 2016 and 2017. The a uthors would like to thank  Marc Le Parquier  and  Nunzia Savoia (PhLAM)  for their work on the operation of the  Titanium-Sapphire laser.
3% and 0. 2% at Belle II and FCC-ee, respectively. The statistical significance of the T 0 signal is derived from the likelihood ratio of two fits: background-only imposing NðT 0 Þ ¼ 0, and the default signal-plus-background. Statistical significances of about 3 and 5 standard deviations (std. dev. ) are obtained at Belle-II and FCC-ee, respectively. The bottom panel of Fig. 3  (right) shows the residual distribution of the pseudodata minus the nullhypothesis fit. More sophisticated statistical analyses could be considered-e. g. , by carrying out the likelihood study in different categories of diphoton angular ranges, where the relative contributions of scalar T 0 and tensor χ c2 decays vary within a few tens of percent-that would slightly increase the signal significance. Nonetheless, this first exploratory study indicates that the observation (evidence) of the production of para-ditauonium is feasible in γγ collisions at FCC-ee (Belle II), respectively.
ACKNOWLEDGMENTS D. d'E. thanks useful discussions with  R. Perez-Ramos  and L. Gouskos on different aspects of this work. Support from the  European Union 's  Horizon 2020 research and innovation program  (Grant agreement No. 824093 ,  STRONG-2020 ,  EU Virtual Access "NLOAccess") , the  French ANR  (Grant No. ANR-20-CE31-0015 , " PrecisOnium "), and the  CNRS IEA  (Grant No. 205210 , "GlueGraph"), are acknowledged.
Having much in common with the Alder-ene reaction and some similarities with the oxy-Cope rearrangement, the oxyene is an overlooked pericyclic reaction. Just like the Alder-ene, the oxy-ene could play a significant role in biological processes and become a useful tool in synthetic organic chemistry. Until now, information about the oxy-ene reaction has remained scattered in the literature. This study puts together the pieces of the puzzle and provides the theoretical background of this underexplored subfield of pericyclic group transfer reactions. S. Y. thanks the CSC for PhD grant. We thank the support of UPSaclay, Ecole Polytechnique, ANR-18-CE07-0033-01 (HICAT) and GENCI (2020-A0070810977).
For a given gas species and a known pressure and temperature, the scaled cross section is known and can be computed using the high-resolution transmission (HITRAN) database. Using the fact that all the frequencies in the probe EOFC are equally-spaced (by ω M,P ) and absorbed simultaneously by the gas sample, it is possible to solve Eq. (  5 ) using an inversion method, for instance, a least squares optimization. The solution yields an estimation of the concentration parameter, as well as the additional parameters: the scaling factor L, the residual transmission offset O and the central frequency of the probe EOFC ω 0,P. The mean gas concentration along the optical path can be computed provided that its length is known. The estimation of L and O allows to compensate power fluctuations common to all the comb lines, without degrading the concentration measurement.
ACKNOWLEDGEMENTS We thank an anonymous referee for helpful comments on the manuscript. TK was supported by the  National Research Foundation of Korea  ( NRF-2019K2A9A1A06091377  and  2020R1C1C1007079 ). SG acknowledges support from a  NOVA  grant for the theory of massive star formation. TG is supported by the  ERC Starting  grant  757258  'TRIPLE'. This work was supported by the  Programme National Cosmology et Galaxies (PNCG)  of  CNRS/INSU  with  INP  and  IN2P3 , co-funded by  CEA  and  CNES. We additionally acknowledge support and computational resources from the  Common Computing Facility (CCF)  of the  LABEX Lyon Institute of Origins  ( ANR-10-LABX-66 ). The supercomputing time for numerical simulations was kindly provided by  KISTI  ( KSC-2019-CRE-0196 ), and large data transfer was supported by  KREONET , which is managed and operated by  KISTI.
Physical ingredients As in  Martins & Palacios (2021) , we computed non-rotating evolutionary models with the code STAREVOL  (Siess et al. 2000; Lagarde et al. 2012; Amard et al. 2019). We modelled stars with initial masses of  150, 200, 250, 300 , and 400 M. We recall here the main physical ingredients relevant for this study. We assumed an Eddington grey atmosphere as outer boundary condition to the stellar structure equations. We used the  Asplund et al. (2009)  solar chemical composition as a reference, with Z = 0. 0134. A calibration of the solar model with these input physics leads to an initial helium mass fraction Y = 0. 2687 at solar metallicity. We used the corresponding constant slope ∆Y/∆Z = 1. 60 (with the primordial abundance Y 0 = 0. 2463 based on WMAP-SBBN by  Coc et al. 2004 ) to compute the initial helium mass fraction at Z = 5.
Comparison to published grids and surface chemical evolution In Fig. 1 , we present the evolutionary paths of the non-rotating STAREVOL models as described in the sections above. We also show three models, for 200 M , 300 M , and 400 M computed with a classical mass-loss recipe 2  combining  Vink et al. (2001)  and  Nugis & Lamers (2000)  when the models enter a Wolf-Rayet-like phase that we used in  Martins & Palacios (2017). In these latter models, we did not include wind clumping in order to be consistent with the models computed by  Köhler et al. (2015)  with the BEC code  (Heger et al. 2000; Brott et al. 2011). We show the latter models in Fig. 1  for comparison. Finally, we overplot the 200 M , 300 M , and 400 M non-rotating models computed by  Gräfener (2021)  with the MESA code  (Paxton et al.
Atmosphere models and synthetic spectra Along each evolutionary sequence, we selected points for which we computed an atmosphere model and the associated synthetic spectrum with the code CMFGEN  (Hillier & Miller 1998). With applications to population synthesis in mind, we chose points at 0, 0. 5, 1. 0, 1. 5, and 2. 0 Myr along each track (when possible, the most massive stars living less than 2. 0 Myr). A final point   was taken at the redmost extension of the track. Fig. 2  shows the positions of the selected points. We took the surface properties of the stars predicted by STAREVOL as input for the atmosphere and synthetic spectra calculations. These parameters are gathered in Table  1. The terminal velocity ∞ was set to 2. 51 × esc as assumed in Sect. 2. 2. Because the mass-loss recipe of  Gräfener (2021)  takes wind clumping into account, we also added inhomogeneities in the computation of the atmosphere models.
Spectral morphology We illustrate the evolution of the spectroscopic appearance of our VMS models with the 200 M star in Fig. 4. The figures for the other masses are gathered in Appendix A. To better identify spectral lines, we show in Fig. 5  the contribution of individual ions to the total UV spectrum of model 200d and to the optical spectrum of model 200f. These two models were chosen because they allow the identification of the maximum number of individual lines in the UV and optical range, respectively. We distribute our synthetic spectra through the POLLUX database 4  (Palacios et al. 2010 ). In the next sections, we describe the morphology of our VMS spectra in the optical and UV range. We highlight the features that appear as typical of VMSs and are not observed in stars with M 100 M.