dataesr/scientific-paragraphs-categorization huggingface dataset:
We then discuss the implications for the dominant quenching processes and morpholog-ical transformations. The data on the mass functions are made public so that they can be directly compared with the predictions of different models. The paper is organized as follows. In § 2, we describe the dataset used as well as the main physical parameters we measure (morphologies, structural parameters, stellar masses etc. ). In § 3 we describe the methodology used to derive the stellar mass functions. § 4 discusses their evolution. Finally in § 5, we discuss the implications for the star formation histories of the different morphologies and the evolution of the quenching mechanisms at different cosmic epochs. Throughout the paper, we assume a flat cosmology with ΩM = 0. 3, ΩΛ = 0. 7 and H0 = 70 km. s -.
e. the stellar mass for which 90% of the galaxies have lower limiting stellar masses. By adopting this threshold, we make sure that at most 10% of the low mass galaxies are lost in each redshift bin. Figure  1  shows the distribution of galaxies in our sample in the mass-redshift plane and the adopted stellar mass completeness as a function of redshift for passive and all galaxies. The sample is roughly complete for galaxies above 10 10 solar masses at z ∼ 3 and goes down to 10 9 at z ∼ 0. 5 (see also table 1). As a sanity check, we use an alternative estimate of the stellar mass completeness by taking advantage of the fact that the CANDELS data are significantly deeper than the H-band selected sample used here (H < 24. 5). We therefore compute in bins of redshift, the stellar mass at which 90% of the galaxies in the full CANDELS catalog are also included in our bright selection.
Additionally, although the photometric extractions by the 3D-HST and CANDELS teams were done independently, the actual data on which Figure  1. Stellar mass as a function of redshift for all galaxies (left panel) and quiescent galaxies (right panel) for our H < 24. 5 selected sample. Blue points show the minimum stellar mass which can be observed for a given galaxy computed as explained in the main text. The red line shows the 90th percentile of the distribution of M lim which is the adopted mass completeness in this work (see text for details). The orange line shows the mass completeness estimated using the full depth CANDELS catalog (see text for details) the photometry is based are nearly identical. This is clearly not ideal. Nevertheless, we lump these together and add in quadrature to the other two terms.
(2013) , especially at z < 2, reducing the previous tensions. We repeat those efforts here, but add morphological information. This enables the first estimates of the formation of stars in different morphologies. We first assume that the SFRD evolution can be parametrized with a Lilly-Madau law as done by  Behroozi et al. (2013) : SF RD(z) = C 10 A(z-z 0 ) + 10 B(z-z 0 ) (2) Then we fit, for each morphological type, an SFRD following the parametrization of equation 2 using equation 1 and the measured of stellar mass densities reported in figure  5. The results are shown in figure  8. The global inferred SFRD evolution agrees reasonably well with the one derived by  Ilbert et al. (2013)  using the same methodology but on a completely different dataset and with different assumptions on stellar masses.
The matrices geometrical arrangement fixes the telescope acceptance function T m [cm  2. sr] which controls the flux captured by the instrument on each line of sight r m. As shown below, the acceptance has a direct influence on the number of muons counted in a given period of time and, consequently, controls the statistical uncertainty of eventual flux variations caused by density changes in the volcano  22;29. The present dataset runs from July 7 th to August 5 th and from August 15 th to October 10 th 2014. The telescope was placed South-South-West at the lava dome basis (Fig. 1 ), the axial line of sight was oriented β 0 = 25 • Eastward and the zenith angle γ 0 = 75 • (the telescope is horizontal when γ 0 = 90 • ). The raw data processing has been standardized. Data reduction and filtering include : events time coincidence (on the 3 matrices), particle time-of-flight, alignment of the fired pixels, number of pixels activated in the rear matrix.
Acknowledgements We thank  R. Avohou ,  D. Benedetti ,  J. Ben Geloun ,  V. Bonzom ,  S. Carrozza ,  S. Dartois ,  T. Delepouve ,  O. Samary Dine ,  R. Gurau ,  T. Krajewski ,  V. Lahoche ,  L. Lionni ,  D. Oriti ,  A. Tanasa ,.
Acknowledgments. This study was supported by research grants from the  Spanish Ministry of Economy and Competitiveness (MINECO)  and  European Regional Development Fund (ERDF)   AYA2009-14027-C05-05 ,  AYA2011-29936-C05-04 ,  ESP2013-48032-C5-3-R  and  ESP2015-69909-C5-5-R. We also thanks  MonNatura and "Parc Natural del Delta de l'Ebre"  for hosting LMA antennas and high energy detector. Data available upon request: ferran. fabro@upc. edu.
These data were also used to constrain the optical model parameters for the DWBA analysis. The angular distributions were obtained by gating on the excitation energy (Fig. 1 ) and correcting for the geometrical acceptance of the T-REX array  [29]. Fig. 5  shows the angular distribution of protons from the twoneutron transfer reaction to the ground state and excited states of 46 Ar at 1554 and 3695 keV. To avoid systematic uncertainties data from the annular detector at backward angles has been excluded due to an unresolved problem with the time dependent efficiency of its multiplexed readout  [29]. The comparison with the DWBA calculations (Section IV B) show that protons from the transfer reaction to the ground state of 46 Ar follow the calculated differential cross section with the characteristic L = 0 minimum at a scattering angle ϑ cm ∼ 20 •.
A. Shell model calculations In order to get insights in the underlying structure causing the large cross section to the first excited 0 + state shell model calculations have been performed using the code NuShellX  [32]. The model space comprises of the sd shell for the protons and the f p shell for the neutrons. Three state-of-the-art effective interactions have been compared, SDPF-U  [11] , SDPF-MU  [12] , and EPQQM  [21]. The SDPF-MU and SDPF-U interactions are constructed from three ingredients. Both use the USD  [33]  effective interaction for the sd proton-proton matrix elements. The neutron-neutron interaction in the f p shell is based on the KB3  [34]  matrix elements for the SDPF-U interactions and the GXPF1B  [35]  interaction for SDPF-MU, respectively. The sd -f p crossshell proton-neutron matrix elements are taken from G-Matrix  [36]  for SDPF-U interaction and from V MU  [3]  in the case of SDPF-MU.
Acknowledgments This work has been supported by the  European Commission  within the  Seventh Framework Programme  ( FP7)  through  ENSAR  (contract number  262010 ), by the  German BMBF  (contract numbers  05P12WOFNF ,  05P12PKFNE ,  05P15PKCIA ,  05P12RDCIA , and  05P15RDCIA ), by the  DFG Cluster of Excellence Origin and Structure of the Universe , by  HIC for FAIR , by  FWO-Vlaanderen (Belgium) , by  BOF KU Leuven  (grant  GOA/2010/010 ), by the  Interuniversity Attraction Poles Programme  initiated by the  Belgian Science Policy Office (BriX network   P7/12 ), by the  UK Science and Technologies Facilities Council (STFC) , and in part by the  Spanish MEC  Consolider -Ingenio 2010, Project No. CDS2007-00042  (CPAN).
Terrestrial triggers are the result of either instrumental or environmental effects in the detector, and their distribution is calculated from the search background estimated by the analyses (as shown in Fig. 3 ). The distribution of astrophysical events is determined by performing large-scale simulations of signals drawn from astrophysical populations and added to the data set. We then use our observations to fit for the number of triggers of terrestrial and astrophysical origin, as discussed in detail in Appendix C. The details of the astrophysical population have a minimal impact on the fit, as in all cases we assume a population distributed uniformly in comoving volume. Figure  9  shows the inferred distributions of signal and noise triggers, as well as the combined distribution. The observations are in good agreement with the model.
The choice is different in the two analyses and reduces the available data of 48. 6 days to 46. 1 days for the PyCBC analysis and 48. 3 days for the GstLAL analysis. To suppress large SNR values caused by non-Gaussian detector noise, the analyses perform additional tests to quantify the agreement between the data and the template. These tests are different in the two analyses and are discussed in their respective subsections below. Both analyses enforce coincidence between detectors by selecting trigger pairs that occur within a 15-ms window and come from the same template. The 15-ms window is determined by the 10-ms intersite propagation time plus 5 ms for uncertainty in accurately determining the measured arrival time of weak signals. A detection statistic for each coincident event is derived as a function of the SNR observed in each detector, the value of the signal consistency tests, and details of the template.
For quantities subject to change because of precession, we quote values at a reference gravitation-wave frequency of f ref ¼ 20 Hz. There are small differences in the source's parameters compared to the runs on the older calibration, but these are well within the total uncertainty; the greatest difference is in the sky area, where the reduced calibration uncertainty improves the localization area by a factor of about 2-3. There are two differences in the configuration of the analysis of LVT151012 from that for GW150914: The prior on the component masses was set to be uniform over the range m 1;2 ∈ ½5; 80M ⊙ , and the length of data analyzed was T ¼ 22 s. We find that LVT151012 is consistent with a lower-mass source, which necessitates a lower prior bound on the component masses and requires us to analyze a longer stretch since the signal could be in band for longer.
The distribution of terrestrial triggers is calculated from the search background estimated by the analyses (as shown in Fig. 3 ). The distribution of astrophysical events is determined by performing large-scale simulations of signals drawn from the various astrophysical populations added to the O1 data set and using the distribution of triggers recovered by our detection analyses applied to this data set. This method correctly accounts for various thresholds applied in the analyses. Note that the observed distribution of astrophysical triggers over the detection statistic will be essentially independent of the astrophysical population used: All populations are assumed to be distributed uniformly in comoving volume; thus, to a good approximation, the measured SNRs and other detection statistics follow the flat-space, volumetric density p 1 ðρÞ ∝ ρ -4  [129].
ACKNOWLEDGMENTS The authors gratefully acknowledge the support of the  United States National Science Foundation  ( NSF ) for the construction and operation of the  LIGO Laboratory and Advanced LIGO , as well as the  Science and Technology Facilities Council (STFC)  of the United Kingdom, the  Max-Planck-Society (MPS) , and the  State of Niedersachsen/ Germany  for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the  Australian Research Council. The authors gratefully acknowledge the  Italian Istituto Nazionale di Fisica Nucleare (INFN) , the  French Centre National de la Recherche Scientifique (CNRS)  and the  Foundation for Fundamental Research on Matter  supported by the  Netherlands Organisation for Scientific Research , for the construction and operation of the Virgo detector and the creation and support of the EGO consortium.
Acknowledgments: The work has been supported by the  ERC  project  MOMB. Authors acknowledge discussions with  D. M. Basko ,  S. A. Studenikin ,  T. Brauner ,  C. Michel ,  E. M. Hankiewicz  and  M. O. Goerbig. The work in ITME-Poland has been supported by the research project  NCN UMO ,  2011/03/B/ST3/03362  Polska. M. V. Y acknowledges the support from  RFBR  though projects  14-02-00080 ,  14-03-00121  and  UB RAS 15-20-3-11.
The darker intervals represent error estimates for (from left to right) the 5%, 50% and 95% quantiles, estimated by Bayesian bootstrapping. M/M M source /M M/M M source /M m1/M m source 1 /M m2/M m source 2 /M M f /M M source f /M q χ eff χp a1 a2 a f z DL/Mpc θLS 1 θLS 2 φ12 cessing IMRPhenom posterior does not (with KS p-value ∈ [0. 30, 0. 60]). This is as it should be, since in precessing IMRPhenom binaries with identical projection of the total spin on the orbital plane have identical waveforms. Although the KS p-values suggest that the data provide information about θ 12 and φ 12 beyond the prior, we note that the 90% confidence intervals for both of these parameters cover approximately 90% of their valid ranges, and are indistinguishable for each waveform model. Spin evolution All the source parameters discussed above are measured at a reference frequency of 20 Hz.
ACKNOWLEDGMENTS The authors gratefully acknowledge the support of the  United States National Science Foundation  ( NSF ) for the construction and operation of the  LIGO Laboratory  and  Advanced LIGO  as well as the  Science and Technology Facilities Council (STFC)  of the United Kingdom, the  Max-Planck-Society (MPS) , and the  State of Niedersachsen/Germany  for support the construction of Advanced LIGO and and operation of the GEO 600 detector. Additional support for Advanced LIGO was provided by the  Australian Research Council. The authors gratefully acknowledge the  Italian Istituto Nazionale di Fisica Nucleare (INFN) , the  French Centre National de la Recherche (CNRS)  and the  Foundation for Fundamental Research on Matter  supported by the  Netherlands Organisation for Scientific Research , for the construction and operation of the Virgo detector and the creation and support of EGO consortium.
The ATLAS trigger system During Run 1 the ATLAS trigger system consisted of three different levels: Level 1 (L1) : It is a hardware trigger implemented in custom-built electronics. It uses the calorimeters and muon spectrometer with coarse granularity to select Regions of Interest (RoI), which are analysed by the following subsequent levels. Level 2 (L2) : It processes data from all sub-detectors at full granularity but only in limited detector RoIs seeded by L1. Access to ID allows track reconstruction. Event Filter (EF) : Guided by L2 result it has the ability to access all sub-detectors data at full granularity. Jets are reconstructed at L1, L2 and EF. Tracking information for b-tagging is available at L2 and EF. The upgrade for Run 2 provides ATLAS with a robust configuration for higher luminosity running, enabling the experiment to take full advantage of the accelerator upgrades.
Prospects for Run 2 The possibility of efficiently triggering on b-jets can be crucial for many analyses present in the ATLAS Run 2 physics program. ATLAS used ever-improving btagging algorithms at the trigger level in the data taking in 2011 and 2012 and calibrated these for data analysis. For the LHC Run 2 data, more advanced b-jet triggers will be used thanks to the merged HLT and the ability to use complex offline b-tagging algorithms directly to the HLT level. In Run 2 the Fast TracKer (FTK), an electronics system to reconstruct tracks for every event selected at L1, will also be incorporated inside the AT-LAS trigger system. Part of FTK will be installed in late 2015, it will give full η coverage by the end of 2016. FTK tracks, freed from the CPU constraints of L2 tracking, will be an important tool box for the ATLAS HLT, allowing it to have improved event selection.
The ATLAS EventIndex: Full chain deployment and first operation Álvaro Fernández Casaní -Increasing rate to 1 kHz. Event size 0. 8/1MB. -RAW DATA 800/1000 MB/s -Translates globally in CPU +20%, DISK +15%, TAPE +15% each year. Event Cataloguing • All this information needs cataloguing according to different points of view to meet multiple use cases and search criteria. • A system that contains the reference to the file that includes every event at every stage of processing is necessary to recall selected events from data storage systems. • ATLAS already has a database called EventTag, with some issues: -Using traditional DBMS Oracle, expensive to maintain -Scaling up in hardware is expensive. -Not all use cases implemented. -Not completely automatized: asynchronous, error prone -Consistency issues -Performance not satisfactory • Appropriate to scale for increased data rates (Commodity HW, Fixed cost/unit), scalable.
-Command line interface, -Web Services with graphical and text/plain output suitable for the Panda Event Service. • Simple data scan operations on the test dataset (1 TB of 2011 data) using the available Hadoop cluster of 20 nodes take about 15 minutes. worst case, reads all data • Queries that give the run number and event number (event picking use case) and use the so-far unoptimised "accessor" method return their result in 3. 7 seconds A Typical query from the Panda Event Service for all events from a GUID takes ~3-10 secs.
The computation of marginalized PDFs and the model evidence require the evaluation of multidimensional integrals. This is addressed by using a suite of Bayesian parameter-estimation and model-selection algorithms tailored to this problem  [42]. We verify the results by using two independent stochastic sampling engines based on Markov-chain Monte Carlo  [43, 44]  and nested sampling  [5, 45]  techniques. (The marginalized PDFs and model evidence are computed using the LALInference package of the LIGO Algorithm Library (LAL) software suite  [46] ). At the detector output we record the data d k ðtÞ ¼ n k ðtÞ þ h M k ðt; θÞ, where n k is the noise, and h M k is the measured strain, which differs from the physical strain h k from Eq. (1) as a result of the detectors' calibration  [47]. In the frequency domain, we model the effect of calibration uncertainty by considering hM k ðf; θÞ ¼ hk ðf; θÞ½1 þ δA k ðf; θÞ × exp½iδϕ k ðf; θÞ; ð4Þ where hM k and hk are the Fourier representation of the timedomain functions h M k and h k , respectively.
V. SUMMARY We have reported the properties of GW150914 derived from a coherent analysis of data from the two LIGO detectors, based on accurate modeling of the coalescence signal as predicted by general relativity. We have shown that GW150914 originates from a BBH system with component masses 36 þ5 -4 M ⊙ and 29 þ4 -4 M ⊙ at redshift 0. 09 þ0. 03 -0. 04 that merges to form a BH of mass 62 þ4 -4 M ⊙ and spin 0. 67 þ0. 05 -0. 07. The final BH is more massive than any other found in the stellar-mass range. We bound the spin magnitude of the binary's primary BH to <0. 7 (at 90% probability), and measure the effective spin parameter along the direction of the orbital angular momentum to be -0. 07 þ0. 16  -0. 17 , but we cannot place meaningful limits on the precession effects. Further implications stemming from our findings for tests of general relativity, the rate of BBH mergers, and the astrophysics of the BBHs are reported in Refs.
ACKNOWLEDGMENTS The authors gratefully acknowledge the support of the  United States National Science Foundation  ( NSF ) for the construction and operation of the  LIGO Laboratory  and  Advanced LIGO  as well as the  Science and Technology Facilities Council (STFC)  of the United Kingdom, the  Max-Planck-Society (MPS) , and the  State of 241102-11 Niedersachsen, Germany  for support of the construction of Advanced LIGO and construction and operation of the GEO 600 detector. Additional support for Advanced LIGO was provided by the  Australian Research Council. The authors gratefully acknowledge the  Italian Istituto Nazionale di Fisica Nucleare (INFN) , the  French Centre National de la Recherche Scientifique (CNRS)  and the  Foundation for Fundamental Research on Matter  supported by the  Netherlands Organisation for Scientific Research , for the construction and operation of the Virgo detector and the creation and support of the EGO consortium.
The visibility value of 16% suggests a good performance of the instrument, also considering the relatively high x-ray energy which reduces the absorption in the analyzer grating. For the tomographic measurement, the specimen was immersed in water to reduce artifacts arising from the strong refraction at the sample-air interface  [19]. Furthermore, simulations showed that the influence of phase-wrapping artifacts on the delta values are negligible at the employed energy of 53 keV (data is not explicitly shown here). A series of 800 phase-stepping scans were collected over a sample rotation of 180 degrees at equidistant angles. The phase stepping was performed over one grating period in four evenly-spaced steps. The acquisition time per frame was 2 s.
Results & Discussion The three-dimensional distributions of the mass density (from the phase volume) and of the linear attenuation coefficient (from the absorption volume) of all samples are obtained simultaneously with the procedure described above. The measured mass density describes the whole tissue including mineral and organic components inside the biocomposites. An example of the same sagittal slice extracted from the phase and absorption data of sample G is shown in Fig  3A    3 for mass density and of about 0. 8 cm -1 for the linear attenuation coefficient are in agreement with previous reports (see  [12, 38] ). The width of the water peak in the histograms is related to the sensitivity of the measurement. If the data were noise free, all the voxels displaying water would yield the same refractive index value and thus the peak would be infinitely narrow.
Acknowledgments We acknowledge  Hans Deyhle  ( BMC, University of Basel ) for helpful insight in interpreting the tooth images. We acknowledge financial support through the  DFG Cluster of Excellence Munich-Centre for Advanced Photonics (MAP) , the  DFG   Gottfried Wilhelm Leibniz program , and the DFG funding PZ through SPP1420. RG gratefully acknowledges the  ESRF  for a 6 month hosting, and the E17 group of  TU Munich  for fruitful discussions and generous support.
This software, EPIgray, by DOSIsoft S. A. (Cachan, France), has since been commercialized and installed in over 60 centers worldwide. From June 2012 to July 2013, transit dosimetry has been successively introduced in routine at our center for all treatment sites and techniques. In addition to its use in patient quality assurance, EPIgray can be easily employed to perform more detailed studies of certain patient cases or techniques over extended periods of time. Since recording MV images during the treatment requires neither extra time nor radiological adjustments, a dosimetric study can even be conducted over the whole course of the treatment if necessary. Such studies can either be for the validation of a technique, the confirmation of the stability of the treatment plan, or the study of anatomical or other changes that lead to dosimetric changes (e.
B. 1 Analysis per linac and technique The configuration of our treatment delivery system is presented in Table  1. As explained above, "integrated dose" images are acquired in the case of static techniques (3D CRT or FiF) and IMRT, beam by beam. Images in "cine" mode are acquired in the case of VMAT treatments, where the irradiation is continuous over the whole arc. The cine mode images of the linac 1, a TrueBeam, are in a new format. This format is incompatible with the treatment delivery system and EPIgray version available to our center. The VMAT plans of linac 1 could thus not been monitored with EPIgray at the time of this study. To ensure a correct follow-up, adapt the tolerance limits to the reality of the clinic, and timely detect any systematic error, series of statistical studies are periodically conducted, using the statistical tools embedded in EPIgray, as well as additional in-house software (Microsoft Excel 2010 and MATLAB v.