dataesr/scientific-paragraphs-categorization huggingface dataset:
Using a non-linear interpolatory scheme with five anchor points (temperature profiles), allows us to have frugal learning with a sparse training set, and the neural network is able to uncover the lower dimensional non-linear manifold of data by way of mapping between latent space and real space. The resulting Interpolatory Auto-Encoder (IAE) model is trained and evaluated in the radial range of [0. 02-2] R 500 using a simulated dataset of 315 temperature profiles from the Three Hundred Project. We then implement a new deconvolution scheme using efficient and cost-effective learning-based regularisation to achieve a stable and accurate reconstruction of the 3D temperature profiles by optimising the latent parameters (barycentric weights) of the anchor points using MCMC. Moreover, the deconvolution algorithm can be easily extended to include the instrumental PSF effect.
Moreover, as in the previous case, the distribution of the fractional residuals over all radii for the MR20 (MS20) sub-sample is narrowly peaked compared to the MD20 (MI20) sub-sample. -We next considered observation-like fitting cases, with typical temperature profile data quality such as would be obtained from the XMM-Newton or Chandra satellites. We first considered a case where we fit 2D temperature profiles defined at twelve radial points and up to R 500 only, mimicking the profile expected from the moderately deep X-ray exposures. We find that in the 2D fitting range i. e. [0. 02-1] R 500 , with the relatively low resolution input 2D temperature profiles, the performance of the IAE model is negligibly degraded. However, beyond R 500 , where we do not consider any 2D data in the fit, the 1-σ dispersion in the 3D reconstruction increases with radius and becomes about ±20% in the last bin.
The augmentation step was achieved by extrapolating the simulated profiles to lower radii (below ≈0. 02 R 500 ) by fitting them to the  Vikhlinin et al. (2006)  parametric model in the range ≈ [0. 02-0. 5] R 500. We then used this updated IAE model to reconstruct the 3D temperature profiles and logarithmic derivative of the representative (DR1) sample galaxy clusters drawn from the CHEX-MATE project. The resulting non-parametric IAE profiles were compared to those derived from parametric deprojection and deconvolution. We find that, in such observational cases where the typical number of annular data points is much fewer compared to the simulations, the difference between the IAE and parametric model is less than 10% over most of the observed region. However, in the inner and outer regions, the difference between them can be as high as 25%.
Acknowledgements. The work of A. I. was supported by  CNES , the French space agency. S. E. , L. L. and F. G. acknowledge the financial contribution from the contracts  ASI-INAF Athena 2019-27-HH. 0 , " Attività di Studio per la comunità scientifica di Astrofisica delle Alte Energie e Fisica Astroparticellare " ( Accordo Attuativo ASI-INAF  n. 2017-14-H. 0 ), and from the  European Union 's  Horizon 2020 Programme  under the  AHEAD2020 project  (grant agreement n. 871158 ). This research was supported by the  International Space Science Institute (ISSI) in Bern , through  ISSI International Team  project # 565  ( Multi-Wavelength Studies of the Culmination of Structure Formation in the Universe ). M. S. acknowledges the financial contribution from contract  ASI-INAF  n. 2017-14-H. 0. and from contract  INAF  mainstream project  1.
05. 01. 86. 10. E. P. acknowledges the financial support of  CNRS/INSU  and of  CNES , the  French Space Agency. M. E. D. acknowledges partial financial support from a  NASA   ADAP  award/SAO subaward  SV9-89010. M. D. P. and A. F. acknowledge financial contribution from  Sapienza Universitá di Roma , thanks to  Progetti di Ricerca Medi  2020,  RM120172B32D5BE2. A. F. thanks financial support from  Universidad de La Laguna (ULL) ,  NextGenerationEU/PRTR , and  Ministerio de Universidades (MIU)  ( UNI/511/2021 ) through grant " Margarita Salas ". H. B. , D. dL. , and P. M. acknowledge support from the  Spoke 3 Astrophysics and Cosmos Observations. National Recovery and   Resilience Plan (Piano Nazionale di Ripresa e Resilienza ,  PNRR ) Project  ID CN_00000013  " Italian Research Center on High-Performance Computing, Big Data and Quantum Computing " funded by  MUR A51 , page 23 of 33 Iqbal, A.
In this context, we discuss the interpretation of the GRAVITY Collaboration (2018) flare data and pay particular attention to the following diagnostics: (i) the marginally detected shift between the astrometric data and the location of the center of mass, (ii) the difference between the data and the hot-spot model used by GRAVITY Collaboration (2018), which assumes a Keplerian orbit and (iii) the physical origin of the rising and decaying phases of the flare light curve in the context of magnetic reconnection. The first point can be discussed in the context of a very simple hot-spot model and is the main topic of Sect. 2. Section 3 is the core of our study and focuses on the second and third points above. It presents a semi-analytical large plasmoid model that is the result of magnetic reconnection.
The EMBLEM code does not only solve for the evolution of the electron distribution, it also provides the associated synchrotron emission and absorption coefficients of the plasmoid particles. We can thus compute an image of our plasmoid scenario by backward-integrating null geodesics in the Schwarzschild space time from a distant observer screen, and integrate the radiative transfer equation through the plasmoid by reading the tabulated emission and absorption provided by EMBLEM. This step was performed by means of the GYOTO 2 ray-tracing code (see Appendix B for details;  Vincent et al. 2011; Paumard et al. 2019). The input parameters that we used for the code are listed in Table  3. With these values of density and magnetic field strength, we obtain a magnetization inside the plasmoid of σ p ∼ 10 -2 from the end of the growth phase because neither the density nor the magnetic field evolve.
Section 3. 3 showed that the temperature, density, and κ parameters of the plasmoid are degenerate. This degeneracy might be removed by simultaneous observations of NIR and X-ray flares. Moreover, synchrotron cooling leads to a translation of the electrons from the NIR-emitting band into the millimeter-emitting band, which could explain the submillimeter flare and its time lag with respect to the NIR. We therefore intend to consider the multiwavelength properties of our plasmoid model in future work, in order to better assess whether it can account for the complete flare data set of Sgr A*. A crucial recent observable of Sgr A* flares are the polarization QU loops  (GRAVITY Collaboration 2018 , 2020d; Wielgus et al. 2022b). We also intend to study the polarized properties of our plasmoid model and compare it to these recent constraints.
We note that Eq. C. 3 is already averaged over the pitch angle. For standard distributions such as thermal, power-law, and κ distributions, these formulae are equivalent to the fits of  Pandya et al. (2016)  (see Appendix D) that we used to compute the quiescent synchrotron flux. Because electrons start to cool as soon as they are injected into the plasmoid, the full distribution no longer is a κ distribution. However, turning the cooling off during the growth phase allows us to compare the results of EMBLEM to the fitting formulae of  Pandya et al. (2016). As we inject electrons following their definition of the κ distribution with a linear increase A62, page 13 of 19   2016 ) (black curve) with the EMBLEM code (red dots) and with the high-frequency limit analytical expression (dashed blue curve).
Acknowledgements. NA and FHV are very grateful to  B. Cerutti ,  B. Crinquand ,  S. von Fellenberg ,  S. Gillessen ,  S. Masson ,  B. Ripperda ,  N. Scepi , and  M. Wielgus  for fruitful discussions. This work was granted access to the HPC resources of MesoPSL financed by the  Region Ile de France  and the project  Equip@Meso  (reference  ANR-10-EQPX-29-01 ) of the programme Investissements d'Avenir supervised by the  Agence Nationale pour la Recherche. A62 , page 11 of 19.
Writing-original draft, H. N. ; Project administration, P. G. All authors have read and agreed to the published version of the manuscript. Funding: The APC was funded by Japan Science and Technology Agency CREST Project JPMJCR2015 (manager: Kenji Fukumizu). The first author was supported by Grant-in-Aid for JSPS fellows (2018J00379). Both authors are supported by the French grant GDR MEGA Matrices et Graphes Aléatoires 2017-2023. This work was partly supported by MEXT Promotion of Distinctive Joint Research Center Program JPMXP0619217849. The authors are grateful to the Centre Henri Lebesgue ANR-11-LABX-0020-01 for the support of this research.
Author Contributions: Writing-original draft, H. N. ; Project administration, P. G. All authors have read and agreed to the published version of the manuscript. Funding: The APC was funded by Japan Science and Technology Agency CREST Project JPMJCR2015 (manager: Kenji Fukumizu). The first author was supported by Grant-in-Aid for JSPS fellows (2018J00379). Both authors are supported by the French grant GDR MEGA Matrices et Graphes Aléatoires 2017-2023. This work was partly supported by MEXT Promotion of Distinctive Joint Research Center Program JPMXP0619217849. The authors are grateful to the Centre Henri Lebesgue ANR-11-LABX-0020-01 for the support of this research.
Photometry 2. 1. 1. TESS TOI-3235 was observed by the TESS primary and extended missions, in sectors 11 (2019 April 23rd to May 20th) and 38 (2021 April 29th to May 26th), respectively. In both cases, it was observed with camera 2 and CCD 4. The long-cadence data (30 minute cadence for sector 11; 10 minute cadence for sector 38) were initially processed by the Quick-Look Pipeline (QLP;  Huang et al. 2020a Huang et al. , 2020b)) , which uses full-frame images calibrated by the tica package  (Fausnaugh et al. 2020). The TESS faint-star search  (Kunimoto et al. 2022)  detected a planet in the QLP data, and it was promoted to a TOI following  Guerrero et al. (2021) , as noted in the ExoFOP archive. 30  For our analysis, we downloaded the TESS PDCSAP light curves  (Smith et al. 2012; Stumpe et al.
MEarth-South MEarth-South is an array of eight 0. 4 m telescopes at the Cerro Tololo Inter-American Observatory (CTIO) in Chile  (Nutzman & Charbonneau 2008; Irwin et al. 2015). MEarth observed TOI-3235 with six telescopes on 2021 June 21st in the RG715 filter with 60 s exposure time, obtaining a full transit of TOI-3235. 01. The light curves are shown in Figure  2  (right panel), where the data from all six telescopes have been plotted together, and the data are listed in Table  1.
TRAPPIST-South TRAPPIST-South  (Gillon et al. 2011; Jehin et al. 2011 ) is a 0. 6 m Ritchey-Chretien robotic telescope at La Silla Observatory in Chile, equipped with a 2K × 2K back-illuminated CCD camera with a pixel scale of 0 65 pixel -1 , resulting in a field of view of ¢ ´¢ 22 22. A full transit of TOI-3235. 01 was observed by TRAPPIST-South on 2022 May 10th in the Sloan-¢ z  filter with an exposure time of 100 s. We used the TESS Transit Finder tool, which is a customized version of the Tapir software package  (Jensen 2013) , to schedule the observations. Data reduction and photometric measurement were performed using the PROSE 31  pipeline  (Garcia et al. 2022). The light curve is shown in Figure  2  (right panel), and the data are listed in Table  1.
LCOGT The LCOGT network  (Brown et al. 2013 ) is a globally distributed network of 1 m telescopes. The telescopes are equipped with 4096 × 4096 SINISTRO cameras having an image scale of 0 389 per pixel, resulting in a ¢ ´¢ 26 26 field of view. TOI-3235 was observed by LCOGT with the SINISTRO instrument at the South Africa Astronomical Observatory site in the Sloan-¢ i  band on 2021 June 10th and at the CTIO site in the Sloan-¢ g  band on 2022 July 1st, full transits of TOI-3235. 01 being obtained in both observations. We used the TESS Transit Finder, which is a customized version of the Tapir software package  (Jensen 2013) , to schedule our transit observations. The images were calibrated by the standard LCOGT BANZAI pipeline  (McCully et al. 2018). The differential photometric data were extracted using AstroImageJ  (Collins et al.
2006 ) and T eff  (Gaia Collaboration et al. 2018 ) similar to TOI-3235. The resulting ExTrA data were analyzed using custom data reduction software. The light curves are shown in Figure  2  (right panel), and the data listed in Table  1. For each sector, we show the full unphased light curve as a function of time (top row); the full phase-folded light curve (second row); the phase-folded light curve zoomed in on the planetary transit (third row); the residuals from the best-fit model, the phase-folded residuals from the best-fit model,zoomed in on the planetary transit (fourth row); and the phasefolded light curve zoomed in on the secondary eclipse (bottom row). The solid red line in each panel shows the model fit to the light curve. The blue filled circles show the light curve binned in phase with a bin size of 0.
The stellar parameters are constrained using isochrones from version 1. 2 of the MIST theoretical stellar evolution models  (Paxton et al. 2011 (Paxton et al. , 2013 (Paxton et al. , 2015;; Choi et al. 2016; Dotter 2016). We allow the line-of-sight extinction A V to vary in the fit, imposing a maximum of 0. 527 mag and a Gaussian prior of 0. 055 ± 0. 2 mag based on the MWDUST 3D Galactic extinction model  (Bovy et al. 2016). We used the ODUSSEAS software (Antoniadis-Karnavas et al. 2020), developed specifically for M dwarfs, to measure the [Fe/H] and T effå from the ESPRESSO spectra. Although ODUSSEAS was developed for spectra with resolutions from 48,000 to 115,000, it has been successfully used with ESPRESSO spectra at their original 140,000 resolution (Lillo-Box et al. 2020). We obtained preliminary values of [Fe/H] = -0.
Based on observations collected at the European Organisation for Astronomical Research in the Southern Hemisphere under ESO program 0108. C -0123 (A). A. J. , R. B. , and M. H. acknowledge support from  ANID-Millennium Science Initiative -ICN12_009. A. J. acknowledges additional support from  FONDECYT  project  1210718. R. B. acknowledges support from  FONDECYT  Project  1120075  and from project  IC120009  " Millennium Institute of Astrophysics (MAS) " of the  Millennium Science Initiative. This work was funded by the  Data Observatory Foundation. The MEarth Team gratefully acknowledges funding from the  David and Lucile Packard Fellowship for Science and Engineering  (awarded to D. C. ). This material is based upon work supported by the  National Science Foundation  under grants  AST-0807690 ,  AST-1109468 ,  AST-1004488  ( Alan T.
Waterman   Award ), and  AST-1616624 , and upon work supported by the  National Aeronautics and Space Administration  under grant No. 80NSSC18K0476  issued through the  XRP Program. This work is made possible by a grant from the  John Templeton Foundation. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the  John Templeton Foundation. The research leading to these results has received funding from the  ARC  grant for  Concerted Research Actions , financed by the  Wallonia-Brussels Federation. TRAPPIST  is funded by the  Belgian Fund for Scientific Research (Fond National de la Recherche Scientifique, FNRS)  under the grant  PDR T. 0120. 21. M. G. is F. R. S. -FNRS Research  Director and E. J. is F. R. S. -FNRS  Senior Research Associate.
Observations were carried out from  ESO La Silla Observatory. The postdoctoral fellowship of K. B. is funded by  F. R. S. -FNRS  grant  T. 0109. 20  and by the  Francqui Foundation. This work makes use of observations from the LCOGT network. Part of the LCOGT telescope time was granted by NOIRLab through the  Mid-Scale Innovations Program   (MSIP). MSIP  is funded by  NSF. Based on data collected under the ExTrA project at the  ESO La Silla Paranal Observatory. ExTrA is a project of  Institut de Planétologie et d'Astrophysique de Grenoble (IPAG/CNRS/ UGA) , funded by the  European Research Council  under the  ERC  Grant Agreement No. 337591-ExTrA. This work has been supported by a grant from  Labex OSUG@2020  ( Investissements d'avenir -ANR10 LABX56 ). This work has been carried out within the framework of the NCCR PlanetS supported by the  Swiss National Science Foundation.
This work has been carried out within the framework of the  National Centre of Competence in Research  PlanetS supported by the  Swiss National Science Foundation  under grants  51NF40_ 182901  and  51NF40_205606. The authors acknowledge the financial support of the  SNSF. This publication benefits from the support of the  French Community of Belgium  in the context of the  FRIA Doctoral grant  awarded to M. T. The contributions at the  Mullard Space Science Laboratory  by E. M. B. have been supported by  STFC  through the consolidated grant  ST/W001136/1. Facilities: TESS, HATSouth, MEarth-South,  TRAPPIST-South , LCOGT,  ExTrA ,  ESPRESSO , Gaia, Exoplanet Archive. Software: FITSH (Pál 2012), BLS (Kovács et al. 2002), VARTOOLS (Hartman & Bakos 2016), CERES (Brahm et al. 2017a), ZASPE (Brahm et al.
Weaker events are more frequent, roughly one per year, but their occurrence rate is dominated by solar activity: GLEs have a greater chance to occur around the maximum and declining phase of the solar cycle. The weakest relativistic SEP events (which are still very strong compared to 'normal' SEP events) form a sub-class of socalled sub-GLE events which can be only observed by high-altitude polar NMs in Antarctica  (Poluianov et al. 2017). The full dataset of original data of the available NMs for the known GLEs is collected in the International GLE database (IGLED -https://gle. oulu. fi) hosted and maintained by the University of Oulu  (Usoskin et al. 2015). Energy/rigidity spectra and anisotropy of GLE-producing particles are typically reconstructed by inversion techniques from a dataset of records of ground-based NMs (e.
20 ). Rapid 14 C increases were confirmed in 7177/7176 BCE and 5260/5259 BCE in these datasets of three or four tree samples (note that only 14 C data of the Bristlecone pine for the 5259 BCE event depicts the increase one year earlier, i. e. , in 5261/5260 BCE). This offset may be due to a dendrochronological miscount or inaccurate separation of annual rings, but  Panyushkina et al. (2022)  have also emphasized the importance of cross-correlating different sequences and correcting for timing differences. For these two events, overall 14 C variations are similar to those of the 774 CE event, with the maximum 14 C increase reaching 19-20‰ in 14 C (deviation of 14 C concentration from a standard of modern value after corrections of age and isotopic fractionation), which is greater than that of the 774 CE event (≈18‰).
However, scales of SEP events for the three events in 774 CE, 7176 BCE, and 5259 BCE are comparable after correcting for the variable geomagnetic shielding, which depends on the geomagnetic field strength  (Brehm et al. 2022). Paleari et al. (2022a)  collected new 10 Be (annual resolution) and 36 Cl (4-year resolution) data from the Northern Greenland Ice core Project (NGRIP) site and 10 Be at sub-annual resolution from the recent Eastern Greenland Ice core Project (EGRIP) for the 7176 BCE event. Significant increases in 10 Be and 36 Cl were observed in these datasets, allowing for synchronization of tree-ring and ice-core chronologies -as a result, the original Greenland Ice core Time Scale GICC05 was shifted 54 years towards the present. Also, increases in the three cosmogenic isotopes ( 14 C, 10 Be, and 36 Cl) are consistent only with the extreme SEP event origin, while contradicting other possible sources such as gamma-ray events  (Miyake et al.
2019; Paleari et al. 2022a). Using these new and previous datasets, i. e. , from Greenland Ice core Project (GRIP) and EPICA Dronning Maud Land (EDML) in Antarctica 10 Be data with ≈6 and ≈5-year resolution,  Paleari et al. (2022a)  estimated a 36 Cl/ 10 Be enhancement ratio of 1. 59 ± 0. 38 for the 7176 BCE event, which corresponds to a hard energy spectrum Fig. 20  Carbon-14 data for the 7176 BCE and 5259 BCE events  (Brehm et al. 2022)  similar to that of 774 CE, 993 CE, and 660 BCE events and similar to the 36 Cl/ 10 Be ratio for GLE#5, estimated to be 1. 57.
New Although a notable 10 Be increase around 5259 BCE has not been confirmed in the lowresolution datasets  (Brehm et al. 2022 ), a similarity of the 14 C variation between the 5259 BCE event and other extreme events suggests an extreme SEP origin for the 5259 BCE event as well. Therefore, 774 CE, 7176 BCE, and 5259 BCE events encompass the greatest SEP events that occurred during the Holocene.
In such a case, γ -rays from the explosion could produce 14 C in the atmosphere through photonuclear reactions (e. g. ,  Konstantinov and Kocharov 1965; Damon et al. 1995). However, Kaiser  Kudsk et al. (2022)  found no significant increase in 1054/1055 CE (i. e. , no association with SN1054) by analyzing the available annually-resolved 14 C datasets reported so far. On the other hand, they reported a remarkable 14 C increase from 1048 to 1055 CE. Thus, multiple publications have reported an unusual 14 C increase around that age, but its origin has not yet been resolved. Panyushkina et al. (2022)  suggested that these different excursions may all relate to the same event, which is manifested differently in different records. For the 1279 CE event, reported by  Brehm et al. (2021) ,  Miyahara et al.
Since CIs are produced as a sub-product of the cosmic-ray-induced atmospheric cascade, it is necessary to model the cascade development. Earlier, it was calculated (semi)analytically, using simplified analytical approaches (see, e. g. , a review by  Gaisser et al. 2016 ), but currently, with the availability of high computational power, it is standardly done with Monte Carlo modelling and specialized software tools such as, e. g. , CORSIKA  (Heck et al. 1998) , FLUKA  (Ferrari et al. 2005) , Geant4  (Agostinelli et al. 2003) , MCNP(X)  (Werner et al. 2017) , PHITS  (Sato et al. 2013 ). These are toolboxes, which allow a selection of different 'physics lists', i. e. , sets of models for physical effects used in computations. Unfortunately, the existing physics models and the particular choice of the used 'physics list' may affect the result (e.
Cross-section data σ j,l (E ) represent the probability of a nuclear reaction leading to the production of the CI of interest to occur and can be quantified via experimental or theoretical ways. Experimental data are thought to be more accurate, but there is a shortage of them, especially for neutron-induced reactions. Gaps between the data points are often bridged by parameterisation (e. g. , applying the measured proton cross-sections to the neutron ones), extrapolations to higher energies, the use of theoretically-calculated values, or a mixture of all the above. One of the most popular sources hosting a compilation of various experimental cross-section data is the EXFOR database (https://www-nds. iaea. org/exfor/). Once the yield function is known, it is straightforward to calculate the CI production rate.