dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison of MIP Calibrations During the construction and commissioning phase as well as during the testbeam periods, the MIP calibration factors were determined with several methods: after the tile assembly the individual HBUs were calibrated with cosmic rays; after the module assembly several modules (without absorber) were calibrated with cosmic rays and with non-showering electrons in the DESY testbeam facility; and during the testbeam at CERN beam muons were recorded. The MIP calibration factors from the DESY and the CERN beam tests are directly comparable, since they were recorded with consistent detector settings. The comparison is shown in figure  13. On average, the calibration factors determined from the electron beam at DESY are about 3% higher, with a spread of about 8%. This demonstrates that the extraction procedure of the MIP calibration factors is robust and can also be applied for non-showering electron data, and that the values determined during the detector commissioning from electron data provide a good initial calibration for the prototype.
Event Displays Figures 16 and 17 show three-dimensional views of events: figure  16  shows muons, one in a cosmic ray event recorded during the commissioning, causing a shower, and a typical beam muon. Figure  17  shows an electron shower and a pion showering in layer ≈ 10 after leaving a track-like signature in the layers before. These displays illustrate the excellent imaging capabilities of the AHCAL. The detailed information provided by the AHCAL can be used to monitor not only the detector stability, but also beam parameters like the purity, already during data taking. An example of a distribution used regularly for this purpose is shown in figure  18  for an electron and a pion run with low contamination: for a given beam energy, electron showers show a narrow distribution in the number of hits and the center-of-gravity along the beam axis at a small detector depth, while hadron showers show a wider distribution in both quantities, with (on average) more hits and deeper centre-of-gravity than electrons.
Acknowledgements We would like to thank the technicians and the engineers who contributed to the design and construction of the prototypes. We express our gratitude to the  DESY  and  CERN laboratories  for hosting our test beam experiments, and to their staff for the efficient accelerator operation and excellent support. Some of the measurements leading to these results have been performed at the  Test Beam Facility at DESY Hamburg (Germany) , a member of the  Helmholtz Association (HGF). This work was supported by the  FWO, Belgium ; by the  Natural Sciences and Engineering Research Council of Canada ; by the  Ministry of Education, Youth and Sports of the Czech Republic ; by the  AIDA-2020 Advanced European Infrastructures for Detectors at Accelerators project , which has received funding from the  European Union 's  Horizon 2020 Research and Innovation programme  under Grant Agreement no.
654168 ; by the  P2IO LabEx  in the framework ' Investissements d'Avenir ' managed by the  French National Research Agency (ANR)  under Grant Agreements  ANR-10-LABX-0038  and  ANR-11-IDEX-0003-01 ; by the  Master Project CALICE of CNRS/IN2P3 France ; by the  Alexander von Humboldt Stiftung (AvH), Germany ; by the  Bundesministerium für Bildung und Forschung (BMBF), Germany , within the Verbundprojekt  05H2018   -R&D DETEKTOREN  ( Szintillatoren ):  R&D für künftige Szintillatorbasierte Detektoren (AHCAL ); by the  Deutsche Forschungsgemeinschaft (DFG), Germany ; by the  Helmholtz-Gemeinschaft (HGF), Germany ; by the  I-CORE Program  of the  Planning and Budgeting Committee, Israel ; by the  Nella and Leon Benoziyo Center for High Energy Physics, Israel ; by the  Israeli Science Foundation, Israel ; by the  JSPS   KAKENHI Grant-in-Aid for Scientific Research  (B) No.
The goal of this study is to determine whether what was observed in M 33 is a general feature of spiral discs. Our goal is to systematically search for asymmetries in velocity dispersion maps, identify their origin, and evaluate the consequences for galactic dynamics. In this paper, we present the first census of asymmetries in the velocity dispersion of H i gas in local, massive discs. The samples used for this analysis are described in Sect. 2. The asymmetries are measured through fast Fourier transforms (FFTs) of velocity dispersion maps from interferometric data with a careful treatment of the effect of beam smearing (BS hereafter), as detailed in Sect. 3. The properties of the asymmetries are presented in Sect. 4, and the discussion of their origin is given in Sect. 5. Finally, we provide a synthesis and conclusions in Sect.
We produced moment maps for them by means of CAMEL, a Python tool 2  described in  Epinat et al. (2012). We then performed tiltedring models of WHISP data cubes using the package 3D-Based Analysis of Rotating Object via Line Observations (3D-  Barolo, Di Teodoro & Fraternali 2015). This software creates a model data cube from input centre of the rings, inclination and position angle, systemic velocity, scale height of the disc, and velocity dispersion, which is fitted to the observations. To determine the kinematic centre, we first kept the inclination and position angle fixed, with the centre of radial rings and the systemic velocity as free parameters. Then, we fixed the centre and let the inclination and position angles vary to get the warp parameters. The final sample of galaxies is presented in Table  1  which lists the coordinates, distance, systemic velocity, the mean inclination and position angle of the discs, and the properties of the synthesised beam.
With mean source distances of 8 and 9 Mpc for THINGS and WHISP respectively, the mean beam B maj of 11 and 18 correspond to average linear resolutions of 470 pc and 80 pc, respectively. The THINGS sample is the main reference and the WHISP sample is used to check the observed trends are not linked to a particular telescope. Section 4 shows that the WHISP data fully support the trends found with THINGS data only.
Modelling the effect of BS in a velocity dispersion map Beam smearing impacts the flux, velocity field, and velocity dispersion maps extracted from datacubes in a different way. Its main effect on velocity fields is to weaken the inner gradient of the rotation curve by mixing data from adjacent regions in the disc. de Blok et al. (2008)  showed that BS does not have a significant impact on THINGS galaxies rotation velocities. They built mock datacubes, smoothed the cubes to the THINGS resolution, and derived the velocity fields and rotation curves from the mock datacubes. Compared to the input used to make the datacube, deviations were less than ∼1 km s -1. For velocity dispersion maps, one expects BS to be more prominent where large variations of radial velocities are observed locally, for instance due to rotation curve inner gradients and to variations of cos(θ), θ being the azimuth angle (see Eq.
(C. 1)). We need to model the impact of BS on velocity dispersion maps by taking into account the variations of velocities in the two spatial dimensions. We use the tool MocKinG 3 based on an analytical formula presented in  Epinat et al. (2010)  who derived the observed velocity dispersion in a galaxy following: σ 2 = σ 2 corr + σ 2 bs , (2) 3 MocKinG stands for modelling Kinematics of Galaxies, see https: //gitlab. lam. fr/bepinat/MocKinG with σ 2 corr = pix σ 2 loc M ⊗ PSF ds pix M ⊗ PSF ds , (3) and σ 2 bs = pix V 2 los M ⊗ PSF ds pix M ⊗ PSF ds -         pix [V los M] ⊗ PSF ds pix M ⊗ PSF ds         2 , (4) where σ loc is the local velocity dispersion map, V los is the line-ofsight velocity field, M is the line flux map, ⊗PSF represents the two-dimension convolution by the PSF, and pix ds integrates over the surface of the pixel.
Fourier series modelling of velocity dispersion maps In order to quantify asymmetries, we performed FFTs of the maps of H i random motions. Discrete FFTs of the corrected σ 2 los maps were performed using the FFTPACK package of the SciPy libraries in Python (see Appendix A). In cylindrical coordinates, the discrete FFT of the velocity dispersion is: σ 2 asym = N-1 k=0 σ 2 k cos(k(θ -φ k )), (5) where σ k and φ k are the amplitude and phase angle of an asymmetry of order k, N is the number of orders in the decomposition, equivalent to the number of elements along the considered ring, and θ is the azimuthal angle in the galaxy plane measured from the semi-major axis of the receding disc half. With this FFT formalism, we make the choice of studying the azimuthal asymmetry, that is we cannot study the asymmetry with respect to the disc plane in the vertical direction.
In our sample, all galaxies were observed at OMM except NGC 3621 and NGC 7793, observed at ESO. In this section, we benefit from the higher angular resolution of the Hα velocity fields to estimate, and correct from, the BS effects on H i THINGS velocity fields. The angular resolution of the Hα data is seeing limited, ∼1 at ESO and 3 at OMM, which represents a gain of ∼2 to 13 with respect to THINGS data. The underlying hypothesis is that the Hα kinematics have a similar behaviour as the H i one. Nevertheless, the flux distribution of the ionised gas is more peaked due to the star forming regions. To model the Hα BS map, we used the Hα velocity field weighted by the flux distribution of the neutral gas. Since the kinematical maps available from  Daigle et al. (2006)  and  Dicaire et al. (2008)  were obtained using adaptative smoothing, we compute new maps from the sky-removed data cubes.
Summary and concluding remarks For this work, we have performed the first systematic search and characterisation of asymmetries in velocity dispersion maps of H i gas in nearby galaxies. We used the best H i data available in the archives, namely 32 galaxies from THINGS and WHISP samples with a spatial resolution better than 1. 5 kpc and a neutral gas distribution extended by at least seven times the resolution, to carry out this pioneering work. We performed a Fourier analysis of the observed second moment maps previously corrected from both the line spread function and the two-dimensional BS contribution tracing the velocity gradients occurring at a large angular scale. This allowed us to measure the strength and phase angle for each of the first four Fourier harmonics, which are the most important perturbations in the random motions of H i gas.
We did not explore the possibility σ θ > σ R , as this situation only occasionally occurs in the stellar populations of the Milky Way or the Large Magellanic Cloud (Gaia Collaboration 2021b, 2023). Then, the mock cubes of data were built, giving each particle a V los following Eq. C. 1. Each data cube contains 200 spectral channels of 3 km s -1 width, and 400 × 400 squared pixels, with a pixel scale of 1. The assigned channel map of a particle corresponds to the one closest to V los , and the line profile in a spatial pixel was obtained by summing the counts of all individual particles from this pixel. Constant position angle of the disc major axis and disc inclination were assumed as a function radius. The position angle was fixed at 45 • in all models, and inclinations of 45 • , 60 • , and 75 • were used.
Acknowledgements. We are grateful to  F. Walter  for sharing the maps of the dirty beams of THINGS observations. P. Adamczyk  and  L. Chemin  warmly acknowledge the  Comité Mixto European Southern Observatory-Gobierno de Chile. The research of L. Chemin is funded by the  Chilean Agencia Nacional de Investigación y Desarrollo (ANID)  through the  Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT) Regular  Project  1210992.
, 2020) , edge-effects  (Pfeifer et al. , 2017) , habitat fragmentation  (Kaszta et al. , 2020) , and forest clearance  (Barber et al. , 2014). However, similarly to other infrastructure types, roads are also accompanied by secondary disturbances such as mining, agriculture, and associated deforestation and hunting that radiate further than the linear clearing itself due to increased accessibility into previously remote areas  (Asner et al. , 2013; Gaveau et al. , 2021). Here, we provide the first large-scale appraisal of the potential biodiversity impacts of infrastructure development in Kalimantan using empirical data. We compiled camera trap data from 11 sites across Kalimantan to predict the spatial patterns of mammal occurrence and diversity, and quantify the effects of infrastructure development to critical habitats of threatened species.
Camera trap sampling of medium-large mammals We collated camera trap data from 11 sites across West (n = 4), Central (n = 3) and East Kalimantan (n = 4) (Fig. 1 ) spanning a gradient of habitat (primarily lowland and hill dipterocarp forests, and peat swamp) and management types (Table  1 ). Surveys were implemented in forests allocated specifically for conservation (n = 7), watershed protection (n = 3) and logging (n = 2) (S1 and Table  S1 ). Sites were sampled between 2012 and 2019, comprising an average of 44 camera stations per site (range: 19-79) separated by a mean inter-trap distance of 0. 78 km. Cameras were positioned approximately 0. 5 m from the ground and positioned on or near animal trails or key environmental resources (i. e. , salt licks, water sources) to maximise detections.
Length of camera deployment was variable between years, stations, and sites, therefore, to standardise sampling effort and meet assumptions of demographic closure for occupancy analysis we limited sampling periods to 90 consecutive nights per camera station per site. Camera data were pooled to make inferences at the landscapelevel whilst accounting for site-level heterogeneity. We define the mammal community as medium-large bodied (>1 kg), terrestrial species (Table  S2 ). Taxonomically ambiguous species (e. g. , lesser mouse deer Tragulus kanchil, and greater mouse deer T. napu; Bornean red muntjac Muntiacus muntjac, and Bornean yellow muntjac M. atherodes) were pooled into genus-level categories due to inconsistency in species identification among datasets. We excluded primarily arboreal species (e.
Covariates We explored 10 anthropogenic and environmental covariates derived from remotely-sensed data based on their reported influence on mammalian habitat-use at either the community or species levels (e. g. Deere et al. , 2020; Tilker et al. , 2019b ) (Table  S3 ). Human disturbance and hunting patterns are difficult to quantify directly due to the complex nature of human behaviour but can be inferred over large spatial scales using established proxy variables from remote sensing  (Tilker et al. , 2019a). Therefore, we explored five such proxies to reflect anthropogenic disturbance ranging from traditional metrics (distance to primary roads and density of logging roads) to more bespoke measures: landscape accessibility, human population pressure, and a village-level multidimensional poverty index.
We also explored five environmental covariates to capture local environmental conditions around camera traps, including percentage of forest cover (an indicator of habitat availability), aboveground biomass (an indicator of habitat quality), elevation, and characteristics of regional fire regimes (frequency and radiative power). All covariates were time-calibrated to the nearest year of data collection for each site reducing biases associated with different sampling years  (Semper-Pascual et al. , 2020) , except for biomass which was only available for 2018. Further information regarding the source and processing of each covariate can be found in S2 and Table  S3. Prior to analysis all continuous covariates were centred around their mean values and scaled to one-unit standard deviation to place the covariates on a comparable scale.
Camera model was strongly linked to site and thus excluded from the detection model: logit r ij À Á ¼ β 0i Habitat j þ β 1i CTN j We conducted all analyses in JAGS version 4. 3. 0 through R version 4. 1. 0 using the package jagsUI; see Supplementary Information S4 for further information on model specification and predictive performance checks. We considered coefficients to have strong support if the 95 % Bayesian credible interval (95 % BCI, the 2. 5th and 97. 5th percentiles of the posterior distribution) did not overlap zero and moderate support if the 75 % BCI did not overlap zero. Unless stated otherwise, we summarise parameter estimates as the mean of the posterior distribution and express uncertainty using 95 % BCI.
Acknowledgements This work was supported by an ARIES PhD studentship awarded to K. L. S by the  UK Natural Environment Research Council (NERC ;  NE/S007334/ 1 ) and  Chester Zoo , and a  Leverhulme Research Leader Award  granted to M. J. S's research team. Camera datasets in Kalimantan were generated via funds from the  Arcus Foundation ,  United States Fish and Wildlife Service (USFWS) ,  Oak Foundation ,  Robertson Foundation ,  University of California, University of Michigan, Victoria University of Wellington ; the  Chinese Academy of Sciences ;  Xishuangbanna Tropical Botanical Garden ;  Hellman Foundation ;  Leakey Foundation; Orangutan Conservancy; Fulbright Foundation ;  Mohamed bin Zayed Species Conservation Fund ; the  AZA Ape TAG Initiative; Disney Conservation Fund ;  Idea Wild ;  Anne Nacey  and  Jeffery Maggioncalda.
This paper presents a generic search for top-quark pair production where one of the top quarks decays to a light scalar particle X (with mass m X < m top ), with X → b b, and an up-type quark (either u or c), while the other top quark decays to W b according to the SM with the W boson decaying leptonically, as shown in figure  1. This search uses the full Run 2 dataset of pp collisions recorded at √ s = 13 TeV. Events with one charged lepton (l = e, µ) and jets in the final state are considered, and separate regions are defined according to the overall number of jets and the number of jets tagged as containing a b-hadron. In order to distinguish signals from SM backgrounds, a neural network (NN) is employed, with basic information from the jets and the lepton as well as invariant masses and angular separation between pairs of jets.
Object definition and event selection Data were recorded from pp collisions at √ s = 13 TeV with the ATLAS detector between 2015 and 2018. Only data consistent with the beam collision region and for which all relevant detector components were functional are used  [21]. The total integrated luminosity is 139 fb -1  [22, 23]. Events were recorded with a single-electron or a single-muon trigger, with minimum thresholds on the transverse momentum (p T ) varying from 20 to 26 GeV depending on the lepton flavour and peak instantaneous luminosity during data taking. The triggers with the lowest p T thresholds include isolation requirements based on the ID or EM calorimeter measurements  [24] [25] [26] [27]. In each event, the primary vertex is defined as the reconstructed vertex consistent with originating from the beam collision region in the x -y plane having the highest scalar sum of the squared p T of associated tracks with p T ≥ 0.
The background due to non-prompt leptons is expected to be negligible based on studies of data using multiple lepton isolation criteria  [58]  and on the analysis of low E miss T events. The production of t t + jets events is modelled using the Powheg-Box v2 NLO generator with the NNPDF3. 0 NLO PDF set and the h damp parameter set to 1. 5 times the topquark mass. The parton shower and hadronisation are modelled by Pythia 8. 230 with the appropriate A14 tune. The b-quark production from the matrix element is modelled using the five-flavour scheme (5FS)  [59, 60]. The sample is normalised to the Top++2. 0 [61]  theoretical cross-section of 832 +46 -51 pb, calculated at next-to-next-to-leading order (NNLO) in QCD that includes resummation of next-to-next-to-leading logarithmic (NNLL) soft-gluon terms  [62] [63] [64] [65] [66].
A 5-fold training  [97]  is performed using the events in the  [4] [5] [6] j3b and  [4] [5] [6] j≥4b regions of the t → uX and t → cX signal processes separately. For each training, all background samples and all signal samples with m X ≥ 30 GeV for the corresponding process are used. The various backgrounds are normalised according to their cross-sections, while the different signals are normalised to the total background. The training also includes the value of the m X parameter, which for signal events is defined to be the true mass of the sig-  nal sample, while for background events a random value of the X mass, taken from the fraction of signal masses in the input dataset, is assigned to each event  [98]. In addition to increasing the size of the training sample, the use of a mass-parameterised NN allows the different signals to be differentiated.
Uncertainties in the t tV , tZ, t tH and tH crosssections are 60%, 60%, +9/-12% and 50%, respectively, arising from the uncertainties in their respective NLO theoretical cross-sections  [90, 108]. Finally, several normalisation and shape uncertainties are taken into account for the t → qX signal. Since no alternative signal samples are used in the analysis, the uncertainties from the choice of NLO generator, parton shower and hadronisation, and reweighting of the t t+light background are assigned to the signal. These uncertainties are chosen to be correlated with the t t+light background, and thus are included in the t t+light modelling category in tables 3 and 4, and uncorrelated across jet multiplicities. The uncertainty in the measurement of the integrated luminosity of the Run 2 dataset is 1.
In addition, individual groups and members have received support from  BCKDF ,  CANARIE ,  Compute Canada  and  CRC ,  Canada ;  PRIMUS 21/SCI/017  and  UNCE SCI/013 , Czech Republic; COST,  ERC ,  ERDF ,  Horizon 2020  and  Marie Skłodowska-Curie Actions ,  European Union ;  Investissements d'Avenir Labex, Investissements d'Avenir Idex  and  ANR, France ;  DFG and AvH Foundation ,  Germany; Herakleitos ,  Thales  and  Aristeia  programmes co-financed by  EU-ESF  and the  Greek NSRF ,.
While GR has only two tensor modes (i. e. plus and cross modes), a generalised metric theory of gravity can accommodate up to six polarisation modes: two tensor, two vector, and two scalar modes  [141, 142]. Similar effects can be seen in extradimensional setups e. g. Ref. [59] ; in such cases, however, these can often lie far beyond detectable range  [56, 57]. The situation is complicated further by the known difficulty in relating these null tests to one another  [143]. For these reasons, we suggest a new avenue of pursuit by which to probe extra dimensions within extant GW data, that exploits the connection between QNM and GW studies. Inspired by tests for deviations from GR within the postmerger phase  [68, 69] , we make use of one of the few tools dedicated to QNM analyses of GW data: the Python package PyRing  [77, 82].
The package was recently developed to perform Bayesian parameter estimation, tests of GR, and other QNM analyses through a combination of observed GW data with simulation and numerically-generated waveform templates, following the Bayesian framework detailed in Ref. [66]. Treating GR as the null hypothesis, PyRing tests for deviations from the QNF oscillation frequency (Re{ω} = ω) and decay timescale (1/Im{ω} = τ ): δω = ω GR (1 + δω), δτ = τ GR (1 + δτ ). ( 4. 57) As a first exploratory step, we run this agnostic test of GR deviation in GW data from the GW150914 black hole merger event  [70]  using the provided Kerr 220 waveform template corresponding to the = m = 2, n = 0 mode (see Fig. 6 ). The analysis through PyRing is conducted in the time domain using publicly available data from the LVK collaboration  [144].
Software. LVK data are interfaced through GWpy, with support through LALSuite. The publicly-available pyRing package can be found at: https://git. ligo. org/lscsoft/pyring. We use the cpnest: v0. 11. 4  [146]  and corner: v2. 2. 1  [147]. Other open-source python packages required by pyRing include cython  [148] , h5py  [149] , matplotlib  [150] , numpy  [151] , scipy  [152] , and seaborn  [153].