dataesr/scientific-paragraphs-categorization huggingface dataset:
Model of Jupiter To illustrate the capabilities of RUBIS in deforming planetary models, we considered the centrifugal deformation of a model of Jupiter. This specific case is very interesting in practice, whether to determine Jupiter's structure by fitting its gravitational moments  (Hubbard 2012 (Hubbard , 2013;; Debras & Chabrier 2018)  or to interpret oscillation modes obtained through projects following the Jovian Oscillations through Velocity Images At several Longitudes (JOVIAL) project  (Gonçalves et al. 2019). In order to illustrate the capabilities of RUBIS in deforming planetary models, we considered a Jupiter model provided by the Code d'Evolution Planetaire Adaptatif et Modulaire (CEPAM;  Guillot & Morel 1995). This model presents a strong density discontinuity due to the presence of a solid core (causing a change of ∼75% in density), making it an ideal application to test the program's stability.
In Fig. 12  we represent the mass distribution in the Jovian model when imposing a solid rotation rate of Ω 0. 298656Ω K , which corresponds to the rotation rate used in  Debras & Chabrier (2018). The most central disconti-nuity, which corresponds to the solid core, is clearly visible in the colour change. A more discrete discontinuity caused by the metallic to molecular phase change in the envelope is highlighted with a white contour. Finally, the code continues to converge even for Jovian models exceeding 0. 9Ω K , although their concrete applications become somewhat uncertain. Another advantage of using RUBIS for deforming planetary models lies in the excellent accuracy it provides at a very low numerical cost. This is illustrated here through a classic problem of Jovian science: the calculation of gravitational moments able to satisfy the observational constraints of Juno.
Conclusion We presented RUBIS, a fully Python-based centrifugal deformation program that can be accessed from this GitHub repository 9. The program takes in an input 1D (spherically symmetric) model and returns its deformed counterpart by applying a conservative rotation profile specified by the user. More specifically, the code only needs the density profile as a function of radial distance, ρ(r), from the reference model in addition to the surface pressure, P 0 , in order to perform the deformation. The program is particularly lightweight because of the central assumption, which consists in preserving the relation between density and pressure when going from the 1D to the 2D structure. The latter makes it possible, in particular, to avoid the standard complications arising from energy conservation in the resulting model  (Jackson 1970; Roxburgh 2004; Jackson et al.
of a percent when scaling effects have been taking into account. This is useful for providing a first estimate of stellar parameters when interpreting observed pulsation spectra, which can then guide more costly searches using more realistic models. Finally, we also demonstrated the ability of the program to deform discontinuous structures such as planetary models and illustrated its accuracy when calculating gravitational moments. The results are promising compared to the existing alternatives and highlight the viability of RUBIS as a model adjustment tool for fitting the measurements coming from the Juno probe.
Acknowledgements. The authors thank the referee for their comprehensive reading and very detailed comments which significantly contributed to making the article clearer. PSH and DRR warmly thank  Pr. Tristan Guillot  for providing them with a model of Jupiter which is deformed for illustrative purposes in Fig. 12. PSH and DRR acknowledge the support of the  French Agence Nationale de la Recherche (ANR)  to the  ESRR project  under grant  ANR-16-CE31-0007  as well as financial support from the  Programme National de Physique Stellaire (PNPS)  of the  CNRS/INSU  co-funded by the  CEA  and the  CNES. DRR also acknowledges the support of the  ANR  to the  MASSIF project  under grant  ANR-21-CE31-0018-03.
high-precision tracking chambers for coverage up to |𝜂| = 2. 7, fast detectors for triggering over |𝜂| < 2. 4, and three large superconducting toroid magnets with eight coils each. The ATLAS detector has a two-level trigger system to select events for offline analysis  [17]. An extensive software suite  [18]  is used in data simulation, in the reconstruction and analysis of real and simulated data, in detector operations, and in the trigger and data acquisition systems of the experiment.
Samples of simulated events with a Higgs boson produced in association with a leptonically-decaying 𝑍 boson are generated using Powheg Box v2  [41]  at next-to-leading order (NLO) with up to one additional jet. The interface with the GoSam package  [42]  provides one-loop amplitude corrections. The PDF4LHC15nlo PDF set  [43]  is used. The decay of the Higgs boson into pseudoscalars and their subsequent decay into 𝑏-quarks are simulated with Pythia 8. 212. The latter is also used to simulate the parton showering and hadronisation, as well as underlying event, with the AZNLO CTEQ6L1 set  [44]  of tuned parameters. The last signal considered is a displaced heavy neutral lepton (HNL)  [45] [46] [47] [48] , since it can isolate the behaviour of LRT for displaced muon tracks. This signal is motivated by theories in which SM left-chiral neutrinos mix with a theoretical massive right-chiral counterpart providing an explanation to the origin of the neutrino mass.
All truth quantities use the simulated beam spot as reference. A data sample recorded during 2018 is used to compare the tracking performance between simulation and data. These are selected from the "zero bias" data stream collected using an unbiased random trigger which fires on the bunch crossing occurring one LHC revolution after a low-threshold calorimeter-based trigger  [49]. This ensures that data are collected with equivalent beam conditions to the rest of the standard physics data. The total number of recorded events is  25, 485, 193. Selected data are compared to a simulation sample of inelastic 𝑝 𝑝 collisions generated with EPOS  [50]  to describe processes with leading jet 𝑝 T < 35 GeV and with Pythia 8. 244  [34]  for events with a leading jet 𝑝 T > 35 GeV, based on the NNPDF2.
3LO PDF  [36] , using the A3  [51]  tune. Additional samples are simulated using modified detector geometries to assess the impact of the modeling of the inner detector material on the track reconstruction efficiency. These samples include one with an overall 5% scaling of the passive material in ID, one with a 10% scaling of the IBL, and one with a 25% scaling to the pixel services. A fourth variation is simulated using the QGSP_BIC physics list instead of the nominal FTFP_BERT physics list  [52]  to quantify the impact of the choice of parameters in the GEANT4 simulation. All data and simulation samples were reprocessed using the Run 3 reconstruction software  [14]. Simulation samples are generated with a flat ⟨𝜇⟩ profile between 0 and 80. In the case of the comparison with data events, they are reweighted to match the distribution of ⟨𝜇⟩ in data with a mean value of 34.
This will translate directly to an improved signal to background ratio for displaced vertex searches in Run 3. To benefit from the improved performance, Run 2 data events have been reconstructed using these updated configurations. Additionally, comparisons between data and simulation show good modelling of both low-level track quantities as well as high-level properties of reconstructed 𝐾 0 𝑆 vertices using LRT tracks. These developments are expected to increase the sensitivity of LLP searches in Run 3 and allow for a considerable expansion of the ATLAS LLP search program. (Taiwan), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in Ref. [61].
Acknowledgments We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT, Argentina ;  YerPhI, Armenia ;  ARC, Australia ;  BMWFW  and  FWF ,  Austria ;  ANAS ,  Azerbaĳan ;  CNPq  and  FAPESP ,  Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ;  CERN ;  ANID ,  Chile ;  CAS ,  MOST  and  NSFC, China ;  Minciencias, Colombia ;  MEYS CR, Czech Republic ;  DNRF  and  DNSRC ,  Denmark ;  IN2P3-CNRS  and  CEA-DRF/IRFU, France ;  SRNSFG, Georgia ;  BMBF ,  HGF  and  MPG, Germany ;  GSRI, Greece ;  RGC  and  Hong Kong SAR, China ;  ISF  and  Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ;  CNRST, Morocco ;  NWO, Netherlands ;  RCN ,  Norway ;  MEiN, Poland ;  FCT, Portugal ;  MNE/IFA, Romania ;  MESTD, Serbia ;  MSSR ,  Slovakia ;  ARRS  and  MIZŠ, Slovenia ;  DSI/NRF, South Africa ;  MICINN, Spain ;  SRC  and  Wallenberg Foundation, Sweden ;  SERI ,  SNSF  and  Cantons of Bern and Geneva, Switzerland ;  MOST, Taiwan ;  TENMAK ,  Türkiye ;  STFC ,  United Kingdom ;  DOE  and  NSF ,  United States of America.
We present a major release of the Tomofast-x open-source gravity and magnetic inversion code that incorporates several functionalities enhancing its performance and applicability for both industrial and academic studies. The code has been re-designed with a focus on real-world mineral exploration scenarios, while offering flexibility for applications at regional scale or for crustal studies. This new version includes several major improvements: magnetization vector inversion, inversion of multi-component magnetic data, wavelet compression, improved handling of topography with support for non-uniform grids, a new, efficient parallelization scheme, a flexible parameter file, and optimized input-output operations. Extensive testing has been conducted on a large synthetic dataset as well as field data from a prospective area of the Eastern Goldfields (Western Australia) to explore new functionalities with a focus on inversion for magnetisation vectors and magnetic susceptibility, respectively.
Introduction In spite of the long-recognised ambiguities in the geological interpretation and inversion of gravity and magnetic datasets  (Nettleton, 1942)  these two methods remain popular approaches to understanding the continental subsurface. This is at least in part because, unlike theoretically more informative methods such as the array of seismic techniques, there are already widespread gravity and magnetic databases available for most continental areas. In addition, unlike geophysical methods such as gamma ray spectroscopy or Synthetic Aperture Radar, there is significant 3D information content in the data. One of the challenges faced by geophysical inversion platforms is to make them scalable, so that they not only meet the expectations of research geophysicists trialling new algorithms but can actually be applied to large-scale industrial and governmental applications.
As part of their exploration program mineral explorers will often undertake an unconstrained 3D inversion of the available magnetic or gravity data in order to better understand the 3D architecture and to generate prospects. As more data are acquired these unconstrained inversions may be re-run with geological constraints and new data sets. Similarly, government agencies also need to undertake 3D inversions, at a relatively large scale, in order to help their geological mapping teams understand the 3D architecture. While they may be less pressed by time constrains than explorers, they will typically be working with much larger data sets. Tools that can scale from prospect to regional scale are required. There has been a long history of forward modelling  (Jessell, 2001)  and inversion of gravity and magnetic datasets  (Pears et al.
, 2017). For much of the past 20 years the tools of choice for most explorers and Government agencies have been the inversion codes developed by the Geophysical Inversion Facility (GIF) at The University of British Columbia (UBC)  (Li and Oldenburg, 1996a, 1998). Over the past decade many explorers have switched from the GIF based tools to the Seequent's Voxi product, the interface for which is built into the commercial Windows based, software tools they often use. Voxi is provided as a commercial cloud based SaS running on clusters. There are several other less commonly used commercial programs for 3D potential field regularised inversion, each with unique specialisations (e. g. Geomodeller, VPmg and MGinv3D). More recently, there has been an increasing focus on developing Open-Source solutions to solve the gravity and/or magnetic inversion problem, which provide access to inversion solutions to a wider audience (Table  1 ).
When the inversion facility at UBC wound down several of the researchers moved to make the algorithms open source and available in python in the hope they would be adopted as a teaching and research tool. This project is known as SimPEG and now has a sizeable user group of academic, government and industry users, particularly in Northern America  (Cockett et al. , 2015). The Python code has been built from scratch using many similar problem formulations to the original Fortran code used in the GIF packages. It uses public libraries (e. g. , NumPy) for I/O and for the solver and leverages new advances in high performance computing (dask and zarr libraries) to improve performance. It is however significantly slower than the older Fortran codes and does not effectively support very large computations.
Name Language Parallel Physics Joint URL Tomofast-x Fortran X Grav/Mag X installation, ease of generation of input meshes, reliance on commercial platforms, etc. even before we think of specific functionality and the additional constraints that can be added to the inversion to try and reduce the aforementioned ambiguity. This paper does not attempt to benchmark the different codes, interesting as that would be and much as that would be a worthy collaboration between the different research groups currently maintaining these codes. What has been missing from this mix is an open source, cross platform, package able to scale to continental sized problems while offering rapid answers at prospect scale. Tomofast-x is such a package. When Tomofast-x was publicly released in 2021 it strongly reflected its research origins.
Description of code features Tomofast-x inversion code is written in Fortran 2008 using classes, which allows flexibility in extending the code such as adding new types of constraints, algorithms, and data I/O. It is fully parallelised using the MPI library which provides both shared and distributed memory parallelism. The code has parallel unit tests covering important parts, which are implemented using "ftnunit" framework  (Markus, 2012)  extended by us to the parallel case to work with the MPI library. The previous code version Tomofast-x 1. 0 included geological and petrophysical constraints implemented, specifically clustering constraints  (Giraud et al. , 2019b) , cross-gradient constraints  (Martin et al. , 2021) , local gradient regularization  (Giraud et al. , 2019a (Giraud et al.
, , 2020)) , Lp-norm constraints  (Martin et al. , 2018) , and disjoint interval bound constraints  (Ogarko et al. , 2021; Giraud et al. , 2023b) , which were summarized in  (Giraud et al. , 2021). The Figure  1  shows a summary of the Tomofast-x 2. 0 inversion workflow, new components added to the version 2. 0 are highlighted in orange. In this section we describe new code components added to Tomofast-x 2. 0 code version and highlight their importance. Figure  1. Tomofast-x 2. 0 inversion workflow, new components are highlighted in orange.
Multiple data components Historically only a single component of the magnetic field was measured. Initially the local declination or inclination were measured using optical dip meters, subsequently the field strength in the vertical direction, Hz, was measured using a fluxgate. Fluxgates were replaced by proton precession and then optically pumped magnetometers which measured the field strength in the direction of the Earth's field, the Total Magnetic Intensity (TMI). However, fluxgates can be used to also measure the horizontal components, as is routinely done within magnetic down hole survey tools. SQUID magnetometers are now being developed which will enable a tensor measurement. Mutli-component data provide a more diagnostic data set with regard to the body location, particularly when remanence is involved.
This can be significant as the wavelet transform calculations are non-local and span over the whole model space. 2) The inverse problem is parallelised by model: In the compressed sparse row (CSR) matrix storage the model vector is accessed in the inner loop in both matrix-vector and transposedmatrix-vector products. This limits the performance due to memory traffic (Chapter 4. 3. 2 in  (Barrett et al. , 1994) ). In order to minimise the memory traffic in these operations it is better to partition the model vector. Both versions with the inversion problem parallelised by data and by model were implemented and compared, confirming that parallelising by model is faster and leads to better scalability. This was also confirmed by profiling the code using Intel VTune Microarchitecture Exploration tool, which showed an increase in L3 cache misses relative to partitioning by model, resulting in an overall slowdown of the code.
1 SI. However, as we are looking at susceptibility contrasts the use of a low background value in the synthetic model does not detract from its results. A parametric potential field modelling package (Potent - https://geoss. com. au/potent. html) was then used to compute the magnetic response of several, geologically plausible, bodies buried in this topography. In order to provide a challenge for the inversion algorithm, the bodies were placed where their magnetic response might best be masked by topography. Parametric programs do not model the effect of 3D topography working instead on contrasts so although the topographic data was used as the ground surface and basis for the sensor drape in Potent, it had no effect on the response. Likewise, the susceptibility of the half-space is only used by the parametric modelling to compute a contrast to use when computing the response of the model body.
This is confirmed by high parallel efficiency of greater than ~90% for all numbers of processors. The nearly linear speed up with number of processors indicates an efficient use of memory and no sign of bandwidth bottlenecking or I/O limiting performance barriers. It is expected that this would scale to an even higher number of threads, particularly on optically linked distributed memory systems. To analyse performance, we also measured the MPI time using the Integrated Performance Monitoring tool (IPM). The IPM has an extremely low overhead and is easy to use, requiring no source code modification. Various configurations of MPI settings were tested and it was found that the strongest impact on the performance has the parameter I_MPI_ADJUST_ALLREDUCE. This controls the algorithm used for the collective MPI communications in the inversion solver.
Discussion & Conclusions Since its initial release in 2021 the Tomofast-x code base has undergone a significant overhaul in terms of both its versatility and performances. Solving for magnetisation rather than susceptibility alone better reflects the real world, where magnetic remanence is much more common than many would like to admit. In addition, allowing input data to have multiple components improves the inverted model relative to a single component inversion of synthetic data, where the answer is known. Meshes with expanding vertical thickness with depth and matrix wavelet compression have allowed larger problems to be solved in real time. Improvements to the parallelisation scheme have allowed for better performance on both shared and distributed memory systems with speedups of ~40 and ~400 times, respectively, for moderate size models.
Memory reduction of up to 100 times are achieved due to wavelet compression of the sensitivity kernel. Following the changes to the code documented here, Tomofast-x 2. 0 produces results very similar to those obtained from the UBC suite of potential field inversion codes. It does so in a similar run time and uses a comparable amount of system resources. However, unlike the commercial version of the UBC suite it runs on Linux platforms which, because of memory and CPU slot limitations imposed on common versions of the Windows operating system, will allow it to be used on more powerful systems than those typically running Windows. Because of its reliance on MPI, in modern Windows systems it runs on the Linux Subsystem for Windows, rather than as a Windows application. As maintenance is planned for the next foreseeable future, we argue that Tomofast-x can be used for commercial projects and demanding, large scale inversions.
Acknowledgements This research was supported by the  Mineral Exploration Cooperative Research Centre (MinEx CRC) , whose activities are funded by the  Australian Government 's  Cooperative Research Centre Programme. This is the  MinEx CRC  Document number  2023/38. VO acknowledges  Rodrigo Tobar  for technical discussions. JG acknowledges support from  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No. 101032994. https://doi. org/10. 5194/egusphere-2023-2243 Preprint. Discussion started: 26 October 2023 c Author(s) 2023. CC BY 4. 0  License.
Code and Data availability The data sets and Tomofast-x input and output files for the Calisto and Synthetic400 models have been packaged and are available for download at Zenodo (Ogarko et al. , 2023a). Readers are encouraged to use these when testing Tomofast-x as well as other codes they may be working with. The latest Tomofast-x code version with user manual and some representative tests is available for download in GitHub (https://github. com/TOMOFAST/Tomofast-x) and at Zenodo  (Ogarko et al. , 2023b). https://doi. org/10. 5194/egusphere-2023-2243 Preprint. Discussion started: 26 October 2023 c Author(s) 2023. CC BY 4. 0 License.
https://doi. org/10. 5194/egusphere-2023-2243 Preprint. Discussion started: 26 October 2023 c Author(s) 2023. CC BY 4. 0 License. al. , 2023a). Finally, it is possible to import the external kernel corresponding to a different 1 geophysics or another optimisation problem (e. g. geological modelling), and use the Tomofast-x as 2 a high-performance parallel inversion (or optimisation) solver. The Python module to import/export 3 the Tomofast-x sensitivity kernel from/to Python scipy sparse matrix has also been implemented, 4 and it is available in https://github. com/TOMOFAST/Tomofast-tools. 5 https://doi. org/10. 5194/egusphere-2023-2243 Preprint. Discussion started: 26 October 2023 c Author(s) 2023. CC BY 4. 0 License.
A more advanced approach, is to compare the SFR of AGN with that from a control galaxy, i. e. , a non-AGN sample that has been selected by applying the same criteria (e. g. , photometric coverage) as the AGN sample and for which the galaxy properties have been calculated following the same method (e. g. , SED fitting). This method has been applied by  Shimizu et al. (2015 Shimizu et al. ( , 2017) )  in hard (14-195 keV) X-ray selected AGN from the Swift/BAT and more recently by  Mountrichas et al. (2021c Mountrichas et al. ( , 2022a,c),c). The limitation of this method is the available number of reference galaxies at a given M , z bin  (Pouliasis et al. 2022). Here, we compare the SFR of X-ray AGN with that of normal galaxies. We apply the same SED fitting analysis in both datasets and we require the availability of the same photometric bands.