dataesr/scientific-paragraphs-categorization huggingface dataset:
Combinatorial likelihood b-jet calibration In this section, a new method to measure b-jet tagging efficiency using a combinatorial likelihood approach with dileptonic t t production events in the 8 TeV dataset is presented. In order to extract the efficiency corresponding to a certain b-tagging requirement, the following equations are used , for the case with exactly two jets in the event: f 2tags = f bb 2 b + f b j j b + (1 -f bb -f b j ) 2 j (1) f 1tag =2 f bb b (1 -b ) + f b j [ j (1 -b ) + (1 -j ) b ] + (1 -f bb -f b j )2 j (1 -j ) (2) where b ( j ) is the b-jet (non b-jet) efficiency, f 1tag ( f 2tags ) is the fraction of events with 1 (2) tagged jets, and f bb ( f b j ) is the fraction of events with a true bb (b j) jet pair. The b-tagging efficiency can be obtained by maximizing a likelihood function, taking both f 1tag and f 2tags from data, with f bb , f b j , and j from simulation.
W + c calibration This section describes a novel method to measure the tagging efficiencies of c-jets in the 7 TeV dataset using W + c production where selecting electrons from W decay (W → eν) and c-jets with muon semi-leptonic decay (c → μX)  [4]. Since the soft muon carries a charge with the same sign as the c-quark, requiring Q μ * Q e = -1 selects W + c events with very high efficiency, where Q μ and Q e are the electric charges of the W's electron and the charm's muon, respectively. Most of the background processes are evenly populated with events where the charges of the decay leptons have opposite sign (OS) or same sign (SS). Therefore, the number of c-jets is obtained by extracting the number of W + c signal events as the difference between the number of events with opposite and with same charges (OS-SS).
D * meson calibration The c-jet efficiency is also measured using jets containing D * (D * ± ) mesons in the 8 TeV dataset, by comparing the yield of D * before and after the b-tagging requirement. The D * ± → D 0 π ± (D 0 → K ∓ π ± ) decay mode offers distinctive kinematic features, resulting in a modest combinatorial background. The contamination of D * mesons originating from b-hadron decays is measured with a fit to the D 0 pseudo-proper time distribution  [2]. Figure  4  shows the measured tagging efficiency of c-jets for data and simulation for MV1 at the 70% b-tagging efficiency WP. The data-to-simulation SF varies in the range 0. 86-0. 97 depending on p jet T , with a 8-15% uncertainty.
Light-jet mistag rate calibration The mistag rate is measured in the 8 TeV dataset. The mistag rate, defined as the fraction of light-jets which are tagged by the MV1 algorithm, is measured in an inclusive jet sample using the negative tag method  [2]. Light-flavor jets are tagged as b-jets mainly because of the finite resolution of the inner detector and the presence of tracks from displaced vertices of long-lived particles or material interactions. Fake SVs and IPs of tracks in light jets are expected to distribute approximately symmetrically around the primary vertex while those from true SVs are not. The mistag rate is estimated by using the tracks' IP and/or SV decay lengths which are negatively signed. A correction has been applied for jet-direction mis-measurement for heavy flavor jets.
Conclusion and outlook The tagging efficiency of b-, c-, and light-flavor jets for the MV1 algorithm has been measured with LHC run 1 data using the 7 or 8 TeV dataset. The results are expressed in terms of data-to-simulation SFs, correcting the efficiencies in simulated events to those measured in data. The best algorithms like MV1 provide for physics an unprecedented WP in which the b-jet tagging efficiency is at 70% while the average mistag rate is around 0. 7%. A combinatorial likelihood approach is applied to dileptonic t t events in order to measure the b-jet efficiency. The SFs have been measured and have a total uncertainty ∼2% for p jet T ∼ 100 GeV. The W boson production in association with a c-quark is exploited to measure the tagging efficiency of c-jets. The total uncertainties ranging from 5% to 13%, increasing as the background rejection of the b-tagging algorithm increases.
Thermogravimetric analysis (TGA) was carried out with a Netzsch STA 449 F3 instrument. Samples weighing ~ 100 mg were loaded into platinum crucibles and heated/cooled at a rate of 5 °C min -1 in 1 atm. of dry air (flow rate 40 mL min -1 ) or 5% H2/N2 (flow rate 20 mL min -1 ). The crystal structure of the samples was further probed by means of transmission electron microscopy (TEM) using a JEOL2100 LaB6 instrument operating at 200 kV. The samples were crushed in dry ethanol and a drop of the suspension was deposited on a carbon-coated film (copper grid). Diffraction patterns were collected with a GATAN Orius 200D Charge Coupled Device (CCD) camera. In situ neutron powder diffraction data were collected on the high-flux medium resolution POLARIS diffractometer at ISIS, the UK spallation source at the Rutherford Appleton Laboratory.
030 atm). Two thermocouples were attached on opposite sides of the wall of the (inner) quartz tube approx. 5mm above the sample to control the furnace temperature and monitor the sample temperature. Data normalization and file output was undertaken using the Mantid software package  13. The patterns from two banks of detectors; the backscattering bank (average 2θ = 146. 7°) which covers a range in 2θ from 135 to 167° with a d-spacing range of 0. 2-2. 65 Å and the 90° bank (average 2θ = 92. 6°) which covers a 2θ range of 75 to 113° with a maximum d-spacing of 4. 1 Å were used together for structure refinement by the Rietveld method, using the FullProf Suite program  14. Peak shapes were modeled by a convolution of two back-to-back exponentials with a pseudo-Voigt function. The background arising from the amorphous quartz glass cell was fitted by the Fourier filtering technique.
Acknowledgments The authors thank  Chris Goodway ,  Paul McIntyre  and  Adam Sears  of the  ISIS User Support Group  for technical assistance with the furnace and gas handling equipment during the neutron diffraction experiment and  STFC  for provision of neutron beam time. GG and MB thank  Mario A. Macias  ( Universidad Industrial de Santander ) and  Tamara Mollon  ( UR1 ) for their participation in the synthesis of the compound. MB acknowledges  T. Roisnel  ( UR1 ) for FullProf improvements relating to TOF data analysis. FT thanks the  KAUST Academic Excellence Alliance  for funding.
Observations and Analysis H. E. S. S. The H. E. S. S. observations have been carried out in four different epoches between 2004 and 2014. The dataset comprises 34 runs 1 taken in wobble mode with the HESS I array in 2004 (4), 2006 (5) and 2012 (4) and with the full HESS II array in 2014  (21). The 2004 and 2006 runs were part of MWL campaigns and were taken on the nights of June  [15] [16] 2004  and July  [18] [19] 2006. Due to the low state of the source, only upper limits could be derived in the original analysis and the results were published separately  [12, 13]. The 2012 and 2014 observations were triggered by flaring states in the TeV domain detected by MAGIC and FACT, respectively. In 2012 two runs in two consecutive nights were taken (June 11 and 12), while in 2014 Mrk 501 was observed on several nights between June 19-25 and on July 29-30.
In order to have an homogeneous dataset, only events detected with the CT1-4 telescopes were extracted from the 2014 observations, discarding the information coming from CT5. All but four runs pass the standard quality cuts, for a total livetime of 13. 1 h (12. 1 h when corrected for acceptance). The mean zenith angle is 63. 7 • and the mean offset from the pointing position 0. 50 •. Data reduction has been performed using the Model analysis  [14]  with Loose cuts, chosen to lower the energy threshold as much as possible. The Reflected Region Background method  [15]  was used for the background determination of the spectral analysis. An excess of more than 2600 photons is detected with high significance. Thanks to the higher sensitivity of the Model analysis, Mrk 501 is detected also in the years 2004 and 2006.
Spectral analyses have been carried out on different data subsets using the forward-folding technique  [16]  and three different spectral shapes. RXTE and Swift-XRT. X-ray coverage has been ensured by RXTE and Swift-XRT. The lightcurve of the former has been retrieved from  [17] , while the data of the latter collected in WT PoS(ICRC2015)761 mode between May 12 and July 30, 2012 (obsIDs: 00030793177-00030793201) and between May 31 and June 28, 2014 (obsIDs: 00035023035-00035023047) have been analysed for this work. The HEASoft software package v. 6. 16 2  with CALDB v. 20140120 has been used. All the events were cleaned and calibrated using the xrtpipeline task and the data in the energy range 0. 3-10 keV with grades 0-2 were analysed. The flux values for the lightcurve (Fig. 1 ) were calculated from the spectra of single snapshots integrating between 2 and 10 keV.
These were derived in the following way: the data were grouped using the grappha tool to have a minimum of 30 counts/bin and then fit using XSPEC v. 12. 8. 2 with a single power-law model and Galactic hydrogen absorption fixed to n H = 1. 58 • 10 20 cm -2  [18]. A clear harder-when-brighter behaviour is detected, as also expected from the literature (e. g. [2] ). Contemporaneous X-rays and H. E. S. S. observations exist for some nights, and are typically offset by ∼ 90 minutes. On June 12, 2012, Swift-XRT observations start immediatly after the H. E. S. S. ones have finished. Abastunami Observatory. Optical data are obtained with the 70 cm telescope of the Abastunami Observatory (Georgia), equipped with an Apogee 6E camera (between 1997-2006 a SBIG ST-6 camera). Observations taken with a R Cousins filter cover the 1997-2014 period and have been analyzed with the Daophot II reduction software using an aperture diameter of 10.
g. ,  [3, 10] ), although it has been determined mostly at energies below 2 TeV. Analyses of several datasets have been performed (total dataset, single nights and single runs during the flares) and the 2012 and 2014 spectra are shown in Fig. 2 (left). Power law (PL) spectral fits provide in general poor results and are acceptable only for low-flux nights and for some run-wise analysis. This is expected from the curved spectra described in the literature (e. g. [3, 8, 10] ) and from the consideration that the intrinsic emitted spectrum must have been absorbed through the interaction with the extragalactic background light (EBL). Mrk 501 spectrum extends up to ∼20 TeV. Despite being a close source, a significant optical depth for EBL absorption τ 1 is expected. Hence, although the curved power law (CPL) and exponential cut-off power law (ECPL) spectral shapes fit the data satisfactorily, they are not a good description of the intrinsic spectra.
PoS(ICRC2015)761 Spectral characteristics of Mrk 501 Gabriele Cologna 1. 27 +0. 18 -0. 15 ). This can be explained by the fact that, altough several sources have been used in  [21] , the major contribution is given by PKS 2155-304 alone. Differences could therefore be explained as dependencies on the redshift (z Mrk 501 0. 034 vs. z PKS 2155-304 = 0. 116) and on the significantly different energy range covered (∼ 2 -20 TeV vs. ∼ 0. 15 -7 TeV). A further comparison with the lowredshift subset composed of Mrk 421 and PKS 2005-489 only (<z> = 0. 051) is not very meaningful because of the large errors on the normalization (α low-z = 1. 6 +0. 5  -1. 1 ,  [21] ) and because the various subsets yield results with LLRT values between -2 and 2, hence not significant. On the other hand, the present result is perfectly compatible with the one derived in  [21]  for the high energy dataset α high-energy = 1.
Spectral characteristics of Mrk 501 Gabriele Cologna 20 TeV during the 2014 flare peak indicates the absence of Klein-Nishina suppression at these high energies. Studies on the EBL intensity exclude the hypothesis of a non-existent EBL at the 9σ level. The normalization factors derived with all datasets (α = 0. 93 +0. 15 -0. 14 ) and with the flare datasets only (α = 0. 89 +0. 16  -0. 14 ) are consistent with the model of  [20]. The quadratic limits on the QM scale (1. 15×10 11 GeV) are the best ones derived from GRB and AGN observations.
Acknowledgments The support of the  Namibian authorities and of the University of Namibia  in facilitating the construction and operation of H. E. S. S. is gratefully acknowledged, as is the support by the  German Ministry for Education and Research (BMBF) , the  Max Planck Society , the  German Research Foundation (DFG) , the  French Ministry for Research , the  CNRS-IN2P3  and the  Astroparticle Interdisciplinary Programme of the CNRS , the  U. K. Science and Technology Facilities Council (STFC) , the  IPNP of the Charles University , the  Czech Science Foundation , the  Polish Ministry of Science and Higher Education , the  South African Department of Science and Technology and National Research Foundation , and by the  University of Namibia. We appreciate the excellent work of the technical support staff in  Berlin, Durham, Hamburg, Heidelberg, Palaiseau, Paris, Saclay , and in Namibia in the construction and operation of the equipment.
The fundamental building blocks of all (underwater) neutrino telescopes are similar: highly sensitive optical detectors called optical modules (OMs) are attached to a backbone cable providing the electrical supply and the readout of the data. These strings are anchored at the sea bottom and hold in an upright positions by buoys. The neutrino detection relies on the emission of Cherenkov light by high energy muons originating from charged current neutrino interactions or from electromagnetic cascades induced by interactions of electron and tau neutrinos inside or near the instrumented volume. All detected signals are transmitted via an optical cable to a shore station, where a farm of CPUs filters the data for coincident signals or hits in several adjacent OMs. The muon direction is then determined by maximising a likelihood which compares the timing of the hits with the expectation from the Cherenkov signal of a muon track or an electromagnetic cascade.
Shower-like events Recently the IceCube collaboration reported the observations of a high-energy excess over the atmospheric backgrounds at the level of 5. 6 and with a flux at the level of E 2 = 0. 95 • 10 -8 GeVcm -2 s -1 sr -1  [7]. The majority of the detected events are induced by electromagnetic cascades. Similarly 5 years (1247 effective days) of data recorded with the ANTARES telescope have been recently used to search for similar events. A dedicated reconstruction algorithm has been developed and extensive Monte Carlo simulations where used to optimize the event selection criteria. The number of background events has been estimated to 4. 9 +2. 9 -3. After un-blinding the data set, 8 events have been found and an upper limit of E 2 90% = 4. 9 • 10 -8 GeVcm -2 s -1 sr -1 could be derived.
Searches for clusters In addition to exploiting the different energy spectra, one can use the reconstructed arrival directions of the recorded events to search for an excess over the uniform atmospheric backgrounds. Exploiting a combination of both features, an enhanced two-point correlation method has been used recently to search for clusters in the arrival directions of neutrinos observed by ANTARES. The introduced novel method exploits the energy of the individual events and searches for the cumulative signal from clusters over the full accessible sky. No apriori assumption on the underlying source morphologies are required. The method has been applied to 4 years of ANTARES data (813 days of effective lifetime; 3058 events) without the detection of a significant excess  [12]  (cf. Fig. 5 ).
Searches for point-like sources The main goal of neutrino telescopes is the localization of astrophysical sources of highenergy neutrinos. Typically the data quality selection criteria are optimized on simulated data sets injecting point-like sources in addition to the atmospheric backgrounds and assuming an E -2 energy spectrum. Within the ANTARES collaboration these Monte Carlo simulations reproduce the actual, time-dependent data taking conditions and allow to describe the real data very accurately. Extensive comparison between data and Monte Carlo simulations are performed using blinded events where the relevant information for the final analysis (e. g. the arrival directions of the events) is hidden. Only once the data selection procedure is finalized, the data is unblinded and a skymap of the neutrino arrival directions is build.
Acknowledgements We would like to sincerely thank our colleagues  A. Alloul ,  C. Collard ,  E. Conte  and  G. Hammad  for the help in generating the background samples used in this work and for useful comments. The work of LB is supported by the  Theorie-LHC France initiative of the CNRS/IN2P3  and by the  French ANR   12 JS05 002 01 BATS@LHC.
ACKNOWLEDGEMENTS We thank  T. Pfau ,  R. Löw ,  D. Jaksch ,  M. Kiffner ,  A. Browaeys ,  T. Lahaye ,  V. Vuletić ,  I. Lesanovsky ,  R. J. Levis ,  R. J. D. Miller ,  I. Ohmine ,  S. Saito ,  T. Yanai ,  S. Takeda ,  A. Tanaka  and  D. Vodola  for fruitful discussions. We also acknowldge  K. R. A. Hazzard  and  R. Mukherjee  for showing us their unpublished numerical simulations of the contrast decays and phase-shifts based on the exact expression equation (3). This work was partly supported by  CREST-JST ,  JSPS   Grant-in-Aid for Specially Promoted Research Grant Number   16H06289  and  Photon Frontier Network Program  by  MEXT. C. G. was supported from the  Austrian Science Fund (FWF)  via project  P24968-N27. G. P. was supported by  ERC-St  Grant ColdSIM (Number  307688 ),  EOARD, RySQ, UdS via IdEX  and  ANR  via BLUESHIELD.
V. PHENOMENOLOGY AT THE LHC AND FUTURE 100 TEV COLLIDERS A. Model implementation To study the phenomenology of this model, we implemented the Lagrangian in Mad-Graph  [89] , using the Mathematica package FeynRules  [90]. The implementation of the FeynRules model file is sketched in this section, while the model files are publicly available on the HEPMDB website  7. The neutral resonances A, Z, A 0 , V 0 , S 0 , S0 and Ṽ 0 are introduced as one particle class of VN, with two additional neutral states X 0 , X0 into another particle class of VX. The charged resonances W + , A + , V + , S + and S+ are put into one particle class of VC. The effective Lagrangian for the strong sector is written in terms of physical pions, thus in the Unitary gauge, and vector bosons in gauge basis, with the latter rotated to their mass eigenstates via mixing matrices.
f 1 = √ 2M A / g, f K = √ 2M V / g and f 0 = v 2 / sin 2 θ + f 2 1 r 2 defined V ± V 0 S 0 A ± A 0 0. 5 1. 0 1. 5 1 e 2 = 1 g 2 + 1 g 2 + 2 g 2 , det M 2 N -M 2 Z I 5 = 0 (77) The model file is loaded using FeynRules package which exports the Lagrangian into UFO format  [91]. We implement one python code as the parameter card calculator, to conduct the numerical rotation and write all block information into a param card. dat.
Acknowledgements We thank  Marc Gillioz  for collaboration at an early stage of this work. GC, HC and AD acknowledge partial support from the  Labex-LIO (Lyon Institute of Origins)  under grant  ANR-10-LABX-66  and  FRAMA  ( FR3127 ,  Fédération de Recherche "André Marie Ampère"). MTF is partially funded by the  Danish National Research Foundation , grant number  DNRF90.
17. The same idea may be implemented in software over an entire image, but the computational complexity remains as the phase difference between the modulating and the reference sinusoids has to be determined. Multiplying two sinusoids of the same frequency produces an output proportional to the cosine of the phase difference between them, and thus phase matching is desirable. The technique of Quadrature Lock-in Detection (QLD) circumvents the problem of the actual phase determination very simply, by making a copy of the time series, and multiplying one by the reference sinusoid, and the other by a sinusoid 90° phase shifted to this, so as to obtain the in-phase and quadrature-shifted components at the frequency of interest, both of which are then used to determine the amplitude at the frequency of interest  18.
All these aspects are extremely important in applications where speed is of essence, as in navigation. Practical utility demands that images be produced with near zero latency, that is, with negligible delay between the acquisition of data and the rendering of the image. Images must also be produced at rates faster than 30 frames per second (fps) to mimic natural vision with continuity of motion, and at twice that rate to avoid flicker (perception of variation in intensity)  21. Here we report implementation of QLD in software, performed at the modulation frequency, which provides the advantage of versatility, low cost and upgradability, while providing processed images in real-time with latencies of the order of milliseconds, and frame rates of 100 fps, limited only by the performance of the camera.
Processing Time and Speedup As the Fourier decomposition is essentially a series of QLDs being performed at different frequencies, it is expected that QLD at a single frequency can be executed faster. However, several Fast Fourier Transform (FFT) algorithms have been developed that compute the Fourier transform not by the definition, but by methods of reducing the problem to smaller parts, so that the computation time is reduced from O(N 2 ) to O(NlnN), N being the length of the time series. Heavily optimised commercial codes are available that may be used for minimising processing time, and thus, it may well be that a FFT consumes lesser time than QLD (though at the cost of efficiency of spectral decomposition, as evidenced by the lower CNR for FFT in Fig. 1 ). We examined, as function of the size of the frame and the length of the time-series, the time taken to obtain a processed image using FFT and QLD algorithms in MATLAB (see Supplementary Information).
The time advantage of QLD over FFT was minor, implying that the FFT algorithm is indeed well optimised. Next, a comparison was made of implementation of QLD using MATLAB and C+ + ; a speedup of nearly an order of magnitude was observed in the latter. The processing time, nevertheless was still a few seconds. Clearly, such long processing times, with additional overheads of other tasks, cannot be tolerated if real-time images are sought. The phrase "real-time" allows for latencies of different durations in different contexts. For example, while imaging a static scene, a time gap of ~10 s may be tolerated. In medical applications, a latency of a few seconds qualifies as real-time. In fact, most often, "real-time" is taken to imply that acquisition is not halted to enable processing and data is being continuously updated, albiet with a latency of several seconds (e.
However, the task of obtaining a single processed image consists of the acquisition of requisite number of raw frames, transfer of data from the camera to the computer, the performing of QLD by software, and the display and storage of the processed image. In fact, it turns out that with GPU implementation of QLD, computation consumes a very small fraction of the entire time. It now becomes imperative that the time taken by the different non-computational tasks be considerably reduced. In this context, a very important fact is that the C+ + environment permits direct access to the camera control (ANDOR Neo sCMOS camera, see Supplementary Information) facilitating rapid setting of camera's operating parameters, data routing, memory management and display on the computer screen, all of which contribute to time overheads, though to varying extents, and have to be carefully optimised.