dataesr/scientific-paragraphs-categorization huggingface dataset:
Navigation occurs from sensor to sensor, then from layer to layer, and finally from volume to volume. Volume navigation works by testing which boundary surface of the current volume is intersected by the trajectory, and looking up what the corresponding volume to enter on the other side is. Construction and modeling of the silicon detectors are very similar to the implementation in the ATLAS tracking software. For the TRT it differs in that the Acts version models every straw individually, rather than grouping them, since it is conceptually simpler. Another difference is the modeling of the calorimeter. While the calorimeter is not explicitly used for tracking, it is still required to be able to propagate trajectories through it. This propagation occurs through a simplified version of the calorimeter geometry (see figure  3 ), which does not correspond directly to the physical geometry of the hardware, but rather a representation of where the readout's location fits best in the laboratory frame, in order to assess which cells are hit.
Concurrent conditions handling and alignment To achieve the most precise parameter estimations, careful handling of potential misalignments of the sensors is required. This requires determining the location of sensors for each   3 ) PreSamplerE (  4 ) EME1 (  5 ) EME2 (  6 ) EME3 (  7 ) HEC0 (  8 ) HEC1 (  9 ) HEC2 (  10 ) HEC3 (  11 ) TileBar0 (  12 ) TileBar1 (  13 ) TileBar2 (  14    interval of validity (IOV), a time period while data taking during which the detector positions are assumed not to change. The multi-threaded execution model presents the challenge that the varying parameters need to be loaded and applied in a concurrency-safe manner. This is complicated by the fact that the call chains and object hierarchies in the ATLAS tracking software are relatively deep, which causes propagation of information to require more effort.
In ATLAS, the strategy is to copy the nominal collection of detector elements in memory, which is always loaded, for every IOV. After copying, the internal caches of the detector elements are updated with the alignment data for the IOV. Finally, the tracking geometry is constructed from scratch with the correct alignment. In the Acts implementation of the ATLAS geometry, a different approach is chosen. All Acts classes and functions that need to be aware of context dependent changes to the geometry accept an explicit context object as their first argument. They also pass the object to any function that they call which also requires this information. Acts is unaware of the concrete type of this object, an std::any object from the C++ standard library is used. At the end of many of the call chains, the Acts::Surface object receives the context object.
This class represents all sensitive modules, and is written in an experiment agnostic way. In ATLAS, the surface object holds a reference to an underlying detector element class, which is aware of the experiment. This class can therefore unpack the context object into a known type (by construction), and use its information to calculate the correct alignment for the IOV. A tracking geometry tool, which makes sure that the geometry context is available for every IOV, and provides a method for clients to retrieve it, was implemented. Clients using the Acts extrapolation request the current geometry context object, while providing the general event context. The geometry context is used to call the extrapolation routine. Detector elements receiving requests for aligned transforms unpack the context, and unpack the required transform.
Acknowledgements AD and GC acknowledge partial support from the  Labex-LIO (Lyon Institute of Origins)  under grant  ANR-10-LABX-66  ( Agence Nationale pour la Recherche) ,  Knut och Alice Wallenbergs Stiftelse  ( KAW 2017. 0100 ) and FRAMA ( FR3127 ,  Fédération de Recherche "André Marie Ampère "). DBF acknowledges partial support from the  Knut och Alice Wallenberg foundation.
Data Proton-proton (pp) collision data recorded by the ATLAS detector at the LHC during 2015 and 2016 at a center-of-mass energy of √ s = 13 TeV were used for the analysis described in this paper. For this data, it is required that the LHC operate with stable beam conditions and that all relevant detector systems be fully functional. The data, corresponding to integrated luminosities of 3. 2 ± 0. 1 fb -1 and 24. 5 ± 0. 5 fb -1 for 2015 and 2016, respectively, were collected using a combination of HLT triggers, which employ algorithms  [19]  to identify jets containing b-hadrons (resulting in 'b-tagged jets'). Maximum-likelihood algorithms were utilized in 2015, while the offline multivariate classifier MV2c20  [20, 21]  was used in 2016. Events were recorded if they passed the L1 single-jet trigger with a transverse energy (E T ) threshold of E T = 100 GeV, and if the HLT identifies either one b-tagged jet with E T > 225 GeV or two b-tagged jets with different thresholds of E T = 150 GeV and E T = 50 GeV.
The FeynHiggs program  [24]  was used to calculate the branching ratios and the cross sections shown in Table  2 , where the branching ratio B(φ → b b) > 85% for all m φ values up to 1400 GeV. Given the large values for B(φ → b b) in Table  2 , the total widths derived from this set of MSSM parameters also represent the typical total widths in the flipped scenario  where tan β = 20, µ = -800 GeV, M SUSY = 1000 GeV, A t = 2000 GeV, M 2 = 800 GeV, and M 3 = 1600 GeV. The pp → b bφ cross section at √ s = 13 TeV is also shown. Full simulation samples of 300,000 events were produced for each of the mass points. The FeynHiggs program  [24]  was used to calculate the branching ratios and the cross sections for pp → b bφ at The ATLAS detector response to the generated signal events was modeled using the ATLAS full simulation software  [25]  based on G 4  [26].
428  [33] , and an LO M  [34]  simulation of the subprocess j j → Z j j, Z → b b interfaced to P 8. 205, where j represents a gluon or a u, d, s, c quark/antiquark. The full ATLAS detector simulation software was used for t t production. A fast ATLAS detector simulation in which the calorimeter response is parametrized  [25, 35]  was used for the multiple b-jets and j j → Z j j samples. The dominant background comes from the production of multiple b-jets. In the S 2. 1. 1 simulation of multiple b-jets all 2 → 2, 3, 4 hard subprocesses with at least one b-quark in the final state were generated at LO. The cand b-quarks masses are set to their running Yukawa values to properly simulate gluon splitting into heavy quarks.
Systematic uncertainties of the background model The background model is validated through the χ 2 probability and F-test analyses described in Sec. 5. As a cross-check, the fit procedure is applied to a small multijet MC sample with an equivalent integrated luminosity of 6. 8 fb -1 for eight m φ hypotheses. Events in MC samples are weighted to reflect data-based corrections for pileup, flavor tagging and trigger efficiency. In order to use the data fit procedure without modification, a special unweighted version of the MC sample is produced using acceptance-rejection sampling and the total weight of each event. The results are summarized in Table  4. The eight separate fits should be uncorrelated given the 10%-15% mass resolution for a heavy Higgs boson. With the assumption of no correlation, the χ 2 per degree of freedom is 1.
Conclusions A search for heavy neutral Higgs bosons produced in association with at least one b-quark and decaying into a pair of b-quarks was performed using 27. 8 fb -1 of 13 TeV pp collision data recorded by the ATLAS detector at the LHC in 2015 and 2016. The data are compatible with SM expectations, yielding no significant excess of events in the mass range 450-1400 GeV. Upper limits on the cross section times branching ratio were derived as a function of the mass of the heavy Higgs boson. The 95% C. L. upper limits are in the range 0. 6-4. 0 pb. Compared to heavy neutral Higgs boson searches utilizing φ → τ + τ -or A → Z h decays, these limits expand the excluded Type Y (flipped) 2HDM parameter space into regions with | cos(β -α)| ≈ 0 and tan β 20. --------------- The ATLAS Collaboration.
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; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC, China ;  COLCIENCIAS, Colombia; MSMT CR, MPO CR  and  VSC CR, Czech Republic ;  DNRF  and  DNSRC, Denmark ;  IN2P3-CNRS  and  CEA-DRF/IRFU , France; SRNSFG, Georgia; BMBF,  HGF  and  MPG , Germany; GSRT, Greece; RGC and  Hong Kong SAR, China ;  ISF and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; NWO, Netherlands; RCN, Norway;  MNiSW  and  NCN , Poland; FCT, Portugal; MNE/IFA, Romania;  MES of Russia  and  NRC KI , Russia Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MICINN, Spain;  SRC and Wallenberg Foundation, Sweden ;  SERI ,  SNSF  and  Cantons of Bern and Geneva, Switzerland; MOST, Taiwan ;  TAEK, Turkey; STFC, United Kingdom ;  DOE  and  NSF ,  United States of America.
Acknowledgements Financial support of  MCIU , through the grant  PGC2018-101355-B-100 ( MCIU/AEI/FEDER,UE)  (XGdC, MP, DS), of  Spanish MINECO , project  FIS2016-80681-P  (MP), and of the  Basque Government  Grant No  IT986-16  (MP, DS) is gratefully acknowledged.
ACKNOWLEDGMENTS We thank  Marco Cattaneo ,  Francesco Ciccarello ,  Adam Hewgill  and  Gabriel Landi  for useful discussions. GDC thanks the  CNRS and the group Theory of Light-Matter and Quantum Phenomena of the Laboratoire Charles Coulomb  for hospitality during his stay in Montpellier. We acknowledge support from the  UK EPSRC   EP/S02994X/1. All the numerical data presented in this work can be found in Ref. [68].
ACKNOWLEDGMENTS This work was supported by the  Swedish Research Council  (Contract No. VR 2015-03956 ), the  Carl Tryggers Foundation  (Contract No. CTS18 : 285 ), and the  Helmholtz Virtual Institute   VI419  " Dynamic Pathways in Multidimensional Landscapes. "The computations were performed on resources provided by the  Swedish National Infrastructure for Computing (SNIC)  under allocation SNIC2019-1-9 at the  Swedish National Supercomputer Center (NSC) , the  High Performance Computer Center North  ( HPC2N ), and the  Chalmers Centre for Computational Science and Engineering  ( C3SE ). We thank  Markus Kowalewski  for helpful discussions and  Sebastian Mai  for assistance in using SHARC.
All galaxies with a detectable 21cm line are expected to have strong continuum detections, and so this survey is expected to be carried out commensally with a continuum galaxy survey. In fact, characterising the continuum emission along the line of sight to HI-emitting galaxies may be a necessary step in detecting the 21cm line. In this section, we describe the properties of a HI galaxy redshift survey using the SKA1 Medium-Deep Band 2 Survey , and the main cosmological applications of the resulting dataset.
Foregrounds Intensity mapping observations suffer from contamination from Galactic and extra-Galactic foregrounds. The main components of the Galactic foregrounds in intensity mapping are synchrotron and free-free emission with amplitudes up to 4 to 5 orders of magnitudes higher than the redshifted HI signal. Current all-sky observations of the foregrounds are sparse in frequency  (Reich & Reich, 1988)  and suffer under high systematic contaminations  (Remazeilles et al. , 2015)  which limit the possibility of template-fitting. However, the spectral smoothness of the foregrounds -each component approximately following a power law in frequency -allows one to separate the spectrally varying HI signal from the foregrounds. Results of Green Bank Telescope data analysis show that blind component separation techniques like Singular-Value Decomposition (SVD,  Switzer et al.
HI intensity mapping: • The SKA1 Wide Band 1 Survey in combination with the Deep SKA1-LOW Survey will provide a legacy dataset of the large-scale matter distribution measuring the cosmic HI abundance through cosmic time (0 < z < 6) with unprecedented precision. • The excellent redshift precision of the intensity maps covering large areas allows one to constrain the expansion history and growth of structure in the Universe, providing constraints on dark energy with SKA1-MID comparable to concurrent surveys at other wavelengths. • The HI intensity mapping surveys will also allow to measure neutrino masses, test Warm Dark Matter models, and inflationary physics. • Synergies of the intensity mapping surveys with optical surveys such as LSST and Euclid are crucial for multi-wavelength cosmology and systematics mitigation (see more detailed discussion below).
ACKNOWLEDGMENTS All authors of the paper contributed to the work presented and the writing of the paper. There was an editorial board who were responsible for the planning and bringing together all the material. This comprised of the working group chairs,  Richard Battye  and  Laura Wolz  who are the corresponding authors for the paper, and members from each of the Focus Groups:  David Bacon  and  Stefano Camera  for Synergies,  Phil Bull  for HI galaxies,  Ian Harrison  for  Weak Lensing ,  David Parkinson  for  Continuum Science ,  Alkistis Pourtsidou  and  Mário Santos  for HI intensity mapping ( Mario Santos  was previously working group chair and started the  Red Book  project), as well as  Pedro Ferreira  in editorial capacity. This Editorial Board is listed as the first few authors alphabetically and then all other contributors as another alphabetical list.
75415  and  84156 ). LW is supported by an  ARC   Discovery Early Career Researcher Award  ( DE170100356 ). YA acknowledges support from the  Netherlands Organization for Scientific Research (NWO)  and the  Dutch Ministry of Education, Culture and Science (OCW) , and also from the  D-ITP consortium , a program of the NWO that is funded by the  OCW. YA is also supported by  LabEx ENS-ICFP :  ANR-10-LABX-0010/ANR-10-IDEX-0001-02 PSL*. HP 's research is supported by the  Tomalla Foundation. AR has received funding from the  People Programme  ( Marie Curie Actions ) of the  European Union   H2020 Programme  under  REA  grant agreement number  706896  ( COS-MOFLAGS ). TS and DJS gratefully acknowledge support from the  Deutsche Forschungsgemeinschaft (DFG)  within the  Research Training Group 1620 'Models of Gravity '.
He brought to my knowledge the history of the Piancaldoli meteorite that fell near Florence in northern Italy on 10 August 1968. A fireball was observed over Yugoslavia and central and northern Italy, followed by explosions accompanying the break-up of the meteorite. Three meteorite fragments totaling 13. 1 g were found on the roof of a house in Piancaldoli. The Piancaldoli meteorite was first classified as a L3 chondrite  (Carapezza and Nuccio 1971)  before being reclassified as LL3  (Carapezza et al. 1975 (Carapezza et al. , 1976)). Although no Raman or TL data were available for Piancaldoli,  Rubin et al. (1982)  proposed its reclassification as petrologic type 3. 4 ± 0. 2 based on silicate compositions. Consequently, Piancaldoli is listed as a LL3. 4 ordinary chondrite in the Meteoritical Bulletin Database.
The chemical compositions of olivine grains were quantified using a Cameca SXFive electron microprobe at the Université Pierre et Marie Curie (UPMC, Camparis, Paris, France) using a 150 nA focused beam accelerated at 15 kV. We analyzed Na, Mg, Si, Al, K, Ca, Fe, Ti, Cr, and Mn in olivine grains. The high beam current allowed detection limits for silicates to be 100 ppm for Al, Ca, and Ti, 150 ppm for Mn and Si, and 200 ppm for Na, K, Cr, Fe, and Mg. The PAP software was used for matrix corrections.
Our results thus demonstrate that (i) Piancaldoli is the second least-altered unequilibrated ordinary chondrite fall after Semarkona, and (ii) primitive meteorites are most likely sitting, unstudied, in meteorite collections worldwide. Grossman and Brearley, 2005). Piancaldoli lies between Semarkona and Bishunpur, suggesting it is of petrologic subtype 3. 10. Bonal et al. , 2016). Data for Vicencia (LL), Axtell (CV3) and ALHA 77307 (CO3) are also reported (see section 3. 3). A vertical offset was applied to ease comparison. Notably, the water band is very weak and olivine is the dominant silicate. et al. 1979; McNaughton et al. 1982; Yang and Epstein 1983; Alexander et al. 2012; Vacher et al. 2020).
Acknowledgments We thank  Colette Guilbaud ,  Michel Fialin ,  Nicolas Rividi ,  Maxime Piralla , Lionel G. Vacher, and  Thomas Rigaudier  for their assistance during sample preparation and analysis. We thank  Ashley King  and  Tim Gregory  for helpful reviews and  Timothy Jull  for editorial handling. This is CRPG-CNRS contribution # 2732.
Previous searches have been performed by both the ATLAS  [22] [23] [24] [25] [26] [27] [28]  and CMS  [29] [30] [31] [32] [33] [34] [35] [36] [37]  collaborations. In this search, enhanced sensitivity to two-body top squark decays, where m tm χ0 1 is greater than the top quark mass, m t , is achieved by the analysis of the full LHC Run 2 dataset and the exploitation of techniques designed to efficiently reconstruct top quarks that are Lorentz-boosted in the laboratory frame. Sensitivity to compressed scenarios, where m tm χ0 1 ∼ m t , is extended compared with previous searches through the analysis of events in which high-transverse-momentum jets from initial-state radiation (ISR) boost the top squark system in the transverse plane. Finally, sensitivity to the four-body decay scenario where m tm χ0 1 is less than the sum of the W boson mass, m W , and the b-quark mass, m b , is achieved by extending the identification efficiency for low-transverse-momentum b-hadron decays through the use of charged-particle tracking information, adding sensitivity to the all-hadronic channel in comparison with previous searches.
All signal samples were produced using the fast simulation, while SM background samples used the G 4 set-up. All MC samples were produced with a varying number of simulated minimum-bias interactions overlaid on the hard-scattering event, to account for pile-up. These interactions were produced using P 8. 2 with the A3 tune  [97]  and NNPDF2. 3 LO PDF set. The simulated events are reweighted to match the distribution of the number of pp interactions per bunch crossing in data. Corrections are applied to the simulated events to account for differences between data and simulation for the lepton trigger, reconstruction, identification and isolation efficiencies, and for the lepton and jet momentum scale and energy resolution. Corrections are also applied to the efficiency of identifying jets containing b-hadrons (b-jets), the probability of mis-tagging jets containing only charm hadrons (c-jets) and only lighter hadrons (light-flavour jets), and the probability of mis-tagging jets originating from the hard pp scattering as pile-up jets.
Background estimation The main SM background process in SRA, SRB, and SRD is Z → ν ν production in association with heavy-flavour jets. In SRC, t t production dominates, including mostly events where one W boson decays hadronically and the other W boson decays via a τ-lepton and its corresponding neutrino (W + jets). Other important background processes include leptonic W decays produced in association with heavy-flavour jets, a single top quark produced with a W boson, and the irreducible background from t t + Z, where the Z boson decays into two neutrinos. Significant background contributions are estimated primarily from comparisons between data and simulation in specially designed 'control regions' (CRs), which have a selection orthogonal to all SRs and aim to enhance a particular background process, while probing a similar event topology.
Conclusions Results from a search for a scalar partner of the top quark based on an integrated luminosity of 139 fb -1 of √ s = 13 TeV proton-proton collision data recorded by the ATLAS experiment at the LHC from 2015 to 2018 are presented. Final states with high-p T jets, large missing transverse momentum, and no electrons or muons are selected. The sensitivity of the search is enhanced by the analysis of the full LHC Run 2 dataset, improvements achieved in the detector performance by the end of the LHC Run 2, and new analysis techniques such as the use of b-tagged track-jets, which extend sensitivity to all-hadronic four-body decays. Direct top squark pair production is considered, assuming both top squarks decay via t → t ( * ) χ0 1 , and considering a large range of mass differences between the top squark and the neutralino.
CONCERTO hardware outside the C-cabin On top of the elements described above and located in the Ccabin, CONCERTO is made also by modules elsewhere in the telescope tower and beyond, in particular, the commercial pulsetube compressor (Cryomech CPA289C) and, more interestingly, the dilution cryostat Gas Handling System (GHS) and the Data AcQuisition (DAQ) and Real Time Analysis (RTA) computers. The GHS is composed of: a) a series of pumps and compressors for the circulation of the 3 He-4 He mixture and for providing compressed air to CONCERTO, and, b) an electronic cabinet hosting a National Instrument CompactRIO 7 real-time controller and multiple analogue and digital input-output modules. A dedicated Labview-based software is loaded on the CompactRIO. The software continuously monitors the state of the cryostat and controls all the pumps, compressors, valves and actuators of the dilution circuit.
The telescope has hosted since 2007, in the same place that will be occupied by CONCERTO, the Large APEX BOlometer CAmera (LABOCA) operating at 360 GHz  (Siringo et al. 2009). The CONCERTO optics box and chassis are slided separately through the C-cabin door and then fixed to the floor by a sufficient number of 12 mm metric screws. Since the beam will bounce between the floor and the top of the cabin (M4), we have measured the deformations of the C-cabin itself under typical APEX observing conditions. This measurement was achieved using two linear wire sensors with range of 3 meters and a single measurement precision of 0. 1 mm. As shown in Fig. 11 , the RMS of both sensors, over 48 hours of data taken during standard APEX observations, is smaller than 0. 2 mm and thus negligible for our purposes.
Acknowledgements. Besides the authors, the technicians and engineers more involved in the experimental setup development have been  Maurice Grollier ,  Olivier Exshaw ,  Anne Gerardin ,  Gilles Pont ,  Guillaume Donnier-Valentin ,  Philippe Jeantet ,  Mathilde Heigeas ,  Christophe Vescovi , and  Marc Marton. We acknowledge the crucial contributions of the whole  Cryogenics and Electronics groups at Institut Néel and LPSC. The arrays described in this paper have been produced at the  PTA Grenoble microfabrication facility. We warmly thank the support from the  APEX  staff for their help in CONCERTO pre-installations and design. The flexible pipes, in particular, have been routed under the competent coordination of  Jorge Santana  and  Marcelo Navarro. We acknowledge support from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (project  CONCERTO , grant agreement No  788212 ) and from the  Excellence Initiative of Aix-Marseille University-A*Midex , a  French "Investissements d'Avenir" programme.