dataesr/scientific-paragraphs-categorization huggingface dataset:
Similarly, the H → eτ signal is mostly present in the eτ µ dataset. Thus, in a search for H → µτ decays, the SM background can be estimated using the eτ µ dataset, and vice versa. Detector-related effects cause a distortion of the original e/µ-symmetry and need to be accounted for. The first effect is due to events containing misidentified and non-prompt light leptons, collectively referred to as misidentified background in the following. These particles originate from misidentified jets, misclassified light leptons, hadronic decays within a jet, and electrons from photon conversion. They contribute differently to the µτ e and eτ µ datasets. The second effect is due to the muons and electrons having different trigger, reconstruction, identifications and isolation efficiencies which depend differently on kinematic properties such as p T , |η|, and φ.
The same procedure is applied with the inverted ratio when scaling µτ e to eτ µ events. The procedure is tested in simulated events, as shown in figure  6 , by applying the efficiency correction to eτ µ events and comparing them with µτ e events. By construction, the efficiency correction is only valid for events containing prompt leptons. In the simulation, events containing misidentified leptons are vetoed. In the data, contributions from both F MC and F FF are subtracted before the efficiency correction is applied. As shown in figure  6 , this procedure restores the symmetry between the two channels in simulation. -19 -JHEP07(2023)166 Background estimation. The number of background events in each dataset (eτ µ and µτ e ) is estimated as a sum of three contributions: a symmetric component estimated by applying the efficiency correction procedure to the events in the other dataset, F MC and F FF.
Since the efficiency correction is only valid for true leptons, the contribution from events containing misidentified leptons is subtracted from the other dataset before applying the efficiency correction. Figure  7  compares the data and the background prediction for a selected set of kinematic variables with strong signal-vs-background discrimination power for the eτ µ and µτ e datasets in the non-VBF and VBF categories. In the figures, the F FF contribution is labelled as "Misidentified", while F MC contribution is labelled as "Other". The normalisation of signal and background yields is obtained by performing the independent fits of the H → eτ and H → µτ signals (discussed in sections 8. 1 and 8. 3), based only on data in the τ final state. The distributions show either H → eτ or H → µτ signal, with clear shape differences to the backgrounds, particularly for mass-based variables, and good agreement of the prediction with data within statistical and systematic uncertainties.
The three BDTs were utilised because they provided better expected signal significance than a single BDT. To increase the number of events in the BDT training, the eτ µ and the µτ e datasets are combined. The resulting BDT scores are combined into a single score using a linear weighted sum, with the weights optimised using the expected signal significance as a figure-of-merit. In the τ had channel of the MC-template method, two BDTs are used in all cases except for the non-VBF category in the eτ had channel, where three BDTs are used. Two/three BDTs were chosen instead of a single BDT because they provide better expected signal significance. In the case where three BDTs are trained, BDT τ had 1 discriminates the signal from the Z → τ τ events; BDT τ had 2 separates the signal from misidentified background; and BDT τ had 3 separates the signal from the rest of the backgrounds.
This clearly indicates the strength of direct searches at the LHC. In the case of H → µτ , the constraints are tighter than the naturalness limit preventing a non-hierarchical mass spectrum from large off-diagonal terms in the Yukawa coupling matrix: |Y τ Y τ | m τ m /v 2 , where v is the vacuum expectation value of the Higgs field  [33] ; in the case of H → eτ , the naturalness limit has not been reached yet. This is in line with the previous ATLAS search  [29]  based on a partial dataset at √ s = 13 TeV, but with tighter limits.
Measurement of the branching ratio difference in the τ final state The Symmetry method uses the eτ µ dataset to estimate the background in the H → µτ search, and the µτ e dataset to estimate the background in the H → eτ search. Therefore, this method measures the difference between the two branching ratios, B(H → eτ ) and B(H → µτ ). In the search for H → µτ , the difference B(H → µτ )-B(H → eτ ) is measured and in the search for H → eτ , the difference B(H → eτ ) -B(H → µτ ) is measured. The two measurements of the branching ratio difference with the Symmetry method are found to be compatible, taking into account the anti-correlation between the two measurements. A more detailed description can be found in appendix D. The B(H → µτ ) -B(H → eτ ) measurement has the smaller expected uncertainty and is chosen as main result for the branching ratio difference from the Symmetry method.
JHEP07(2023)166 The observed limits based on the full dataset recorded by ATLAS at √ s = 13 TeV are more stringent by factors of up to 2. 5  (1. 6)  than the corresponding previous limits for the H → eτ (H → µτ ) decay based on a partial dataset at √ s = 13 TeV, while the expected sensitivity for the H → eτ (H → µτ ) signal improves by a factor of about 3. 1 (4. 1). In addition to the approximately four times larger dataset, the main sources of improvement are significantly more sophisticated features of the analysis method. These include lepton assignment in the approximate Higgs boson rest frame for the τ channel, use of the symmetry method for the τ VBF category in the fit with one parameter of interest, introduction of a simultaneous fit of H → eτ and H → µτ signals (two parameters of interest), more advanced multivariate classifiers for signal extraction, and improved object reconstruction, in particular the τ identification algorithm.
C MVA optimisation The MVAs are trained using the four-momenta of the selected reconstructed particles as well as other derived observables, such as the invariant masses, angular separations and the E miss T. In the VBF category, additional jet-related variables are used to exploit the characteristic VBF topology. The complete list of the variables used is shown in table  10. For the MC-template method, the list of input variables is optimised by maximising the expected signal significance based on fits to Asimov datasets  [112]  considering statistical uncertainties only. For the Symmetry method, the hyperparameters and the input variables are optimised with respect to the expected signal significance by performing the statistical analysis taking into account all systematic uncertainties and using only τ data (as described in section 8).
D Compatibility of branching ratio differences The compatibility of the two measured branching ratio differences, ∆ eτ = B(H → eτ ) -B(H → µτ ) and ∆ µτ = B(H → µτ ) -B(H → eτ ), in the Symmetry method is tested. Measuring ∆ eτ (∆ µτ ) is equivalent to measuring µ eτ (µ µτ ) when assuming µ µτ (µ eτ ) = 0. This implies that the sum of the two fitted values for µ eτ and µ µτ should be consistent with zero. This was quantified using a χ 2 test, accounting for the error of the mean value of the upward and downward systematic variations of µ eτ and µ µτ , and the correlation coefficient ρ between the eτ µ and µτ e datasets. The latter is found to be ρ = -0. 80 using the bootstrap methodology  [120]  when considering the full systematic uncertainties in the fit. The results obtained in the non-VBF and VBF categories, as well as in the combined fit, indicate that the ∆ eτ and ∆ µτ measurements are compatible within 1.
5, ∼32. 9, and ∼49. 4 m s -1 for the 13 CO (3-2), CO (3-2), and CO (4-3) lines, respectively. For each target, 120″ × 120″ on-the-fly maps were obtained at 6″ s -1 using a relative reference off position 1000″ away in R. A. We removed a first-order baseline from the spectra and calibrated the data using a main beam efficiency of 0. 73 and 0. 60 at 352 and 464 GHz, respectively, and the values were converted to janskys using 41 and 48 Jy K -1 at 352 and 464 GHz, respectively. We calculated the noise levels of each CO line by first selecting the first and last 100 channels of each cube (individually confirmed to be free of line emission), calculating the noise levels for each FUor using these channels, and then calculating the median noise level of all FUors to obtain representative values. The rms noise levels at the native spectral resolution mentioned earlier are 3.
Distribution of Gas We constructed velocity-integrated emission maps (moment zero) for 12 CO (3-2) using all channels in our data cubes. The resulting maps are presented in Figure  1. Some young eruptive stars are still deeply embedded; therefore, it is possible that the observed CO emission originates from the remaining material in their surrounding envelope. In order to verify that our CO detections come from the FUors, we compared our moment zero maps with the dust continuum emission. We searched for 250 μm continuum maps taken with Herschel/SPIRE maps in the Herschel Science Archive 9 and found data at an angular resolution of 17 6 for 18 sources. For the two remaining sources (V900 Mon and Z CMa), we searched the Canadian Astronomy Data Centre 10 for archival 850 μm observations taken with the James Clerk Maxwell Telescope 11 (JCMT) with an angular resolution of 14 5.
Distances The estimation of gas masses is dependent on the distance to the target. For FU Ori, V899 Mon, AR 6A, V900 Mon, V960 Mon, and BBW 76, we used photogeometric distances from the Gaia Early Data Release 3 (Bailer-Jones et al. 2021). In the case of the more embedded objects (i. e. , undetected by Gaia), L1551 IRS 5, V883 Ori, V1647 Ori, and V2775 Ori, we used the distances estimated from the distance to their molecular clouds and the positions of the FUors within them  (Connelley & Reipurth 2018 , and references therein). For V582 Aur, we used the distance estimated by  Kun et al. (2017)  under the assumption that the FUor is related to the Aur OB1 association. We followed  Tapia et al. (2015)  and used the mean distance to the Great Carina Nebula (NGC 3372) for V723 Car. The distance to iPTF 15afq was estimated by  Park et al.
The inclination of V582 Aur is unknown because continuum observations have not resolved the disk  (Ábrahám et al. 2018) , and the latter two FUors have comparable inclination angles  (Cieza et al. 2018; Kóspál et al. 2021). We do not have 10 μm data for two sources: AR 6A and iPTF 15afq. Based on its high envelope mass, we could expect the silicate feature around AR 6A to be in absorption. However, the peak of its CO emission is off-center (Figure  1 ), so the direct line of sight to our target could have less material and show the feature in emission. In the case of iPTF 15afq, the peak of the CO is also slightly off-center; however, its mass envelope falls in the intermediate range of masses, so we expect this to depend on the geometry of the system. This suggests that HBC 687 is the most evolved FUor in our sample.
Tobin et al. (2020)  and  Kóspál et al. (2021)  presented continuum observations at millimeter wavelengths with data from ALMA and the Very Large Array, and both reported that this FUor is also a proto-binary star. Reipurth 50-This Class I protostar is also referred to as HBC 494. Ruíz-Rodríguez et al. (2017b)  presented high angular resolution observations with ALMA in which they traced the emission from the outflow in 12 CO (J = 2-1) and the envelope emission with the same transition of 13 CO and C 18 O. The extension of the J = 2-1 outflow obtained with ALMA is smaller than the size of our beam. This can be explained by the maximum recoverable scale of their ALMA configuration (11″), which is comparable to the extended emission seen in their channel maps (e. g. , the 6 km s -1 channel in their Figure  3 ); thus, it is likely that they have resolved out most of the extended emission of the outflow.
We estimated the inclination of the outflows by assuming that the inclination of the outflow was perpendicular to the inclination of the disk. For most sources, we used the inclination of the FUor disks obtained from high angular resolution observations with ALMA  (Cieza et al. 2018; Hales et al. 2020; Kóspál et al. 2021) , while for Z CMa, we used the estimate by  Antoniucci et al. (2016)  from an analysis with data from optical interferometry, and in the case of iPTF 15afq, we assumed an inclination of 45°. However, the works studying the quiescent sample used a Figure  9. Cumulative histograms of the outflow properties and outflow-to-envelope mass ratio for the Class 0 and I objects from the literature  (Dunham et al. 2014; Yıldız et al. 2015; Mottram et al. 2017 ) and the FUors from this work.
This project has received funding from the  European Research Council (ERC)  under the  European Unionʼs Horizon 2020 research and innovation program  under grant agreement No. 716155  (SACCRED). T. Cs. has received financial support from the  French State  in the framework of the  IdEx Université de Bordeaux   Investments for the Future Program. This publication is based on data acquired with the  Atacama Pathfinder Experiment  (APEX) under program  IDs 094. F-9508  and  098. F-9505. APEX is a collaboration between the  Max-Planck-Institut fur Radioastronomie , the  European Southern Observatory , and the  Onsala Space Observatory. Facilities: APEX, Herschel, JCMT. Software: NumPy (Harris et al. 2020), Astropy (Astropy Collaboration et al. 2013Collaboration et al. , 2018)), Matplotlib (Hunter 2007), SciPy (Virtanen et al.
Data taking began on 2004 January 1 with 154 WCDs and two fluorescence stations partly operational. Observations with the instrumentation of Figure  1  started in 2008 June and have been in progress ever since. The SD is operated almost continuously, while observations with the FD are restricted to clear dark nights. Phase I of the project was completed on 2020 December 31. Instrumentation used in other Phase I studies is described in  Aab et al. (2015a). It is thus timely to release a catalog giving details of the extensive air showers produced by the highest-energy cosmic rays observed thus far. In addition to the detailed information on the 100 events of the highest energy recorded between 2004 January 1 and 2020 December 31, which are part of the set of events discussed by  Abreu et al.
From the FADC information, the amplitude and time structure of each signal are obtained. Data from the FDs are recorded in a different manner  (Abraham et al. 2010b). The telescopes at each of the four fluorescence stations are operated remotely from the Malargüe Campus or, since 2017, additionally from various locations around the world. The camera of each telescope contains 440 photomultipliers (pixels). The recording of signals and time stamps is completely independent of that used for the SDs. A very loose criterion of a localized pattern of four pulses in consecutive time order is adopted as the trigger at each fluorescence telescope. Those triggers where a shower track can be found are transmitted to the central computer, together with information on the geometry of the shower candidate.
From this information, the time of impact of the shower at a ground position in the region of the SDs is computed, so that all FADC traces in the region, arriving within 20 μs, are also centrally recorded. After each night of operation, data from the fluorescence triggers are then merged with those data collected with the SDs: these form the hybrid data set. For high-level analyses, several quality cuts are applied to the fluorescence events, including those relating to cloud cover and atmospheric aerosols. Further cuts are made to ensure that the selection of events is unbiased with respect to the primary particle mass  (Aab et al. 2014a ). The overall efficiency of these cuts is such that approximately 25% of SD events with energies above 10 EeV registered during FD operation have an accompanying good-quality and unbiased FD shower profile.
This usually occurs for detectors within about 500 m of the core, where the signal is greater than 1000 VEM. An algorithm is used to estimate the true magnitude of the signal from the amplitude of the undershoot, which is introduced capacitatively. Moreover, for signals larger than 2000 VEM, the photomultiplier tube (PMT) response is highly nonlinear so that only timing information is used and the signal is treated in the LDF fit only as a lower limit to the actual size of the signal. Note that the estimated true signal value is used in the LDF fit for saturated signals smaller than 2000 VEM. For 50% of the events contained in the full data set, the signal in one station is saturated; three of the events discussed below have two saturated stations. Examples of saturated signals can be found in Section 4, and in the larger database.
of the shower. This hybrid technique, implemented for the first time at the Auger Observatory, enhances the precision with which the shower geometry is determined: the direction is known to ∼0°. 6  (Bonifazi 2009). The signal from each pixel is recorded in 100 ns intervals and the time and amplitude data are used to delineate the profile of the shower development using the techniques described by  Unger et al. (2008). This method allows differentiation between the various sources of detected light, namely the fluorescence light, direct Cherenkov light, and light scattered from the Cherenkov beam into the fluorescence telescope from air molecules and aerosols. For each 100 ns interval, the energy deposited in the slant depth interval corresponding to the measured light flux is estimated. These individual estimates are fitted using the universal shower profile function described in  Andringa et al.
Determination of the Energy of the Primary Particles The methods by which data from the SDs are calibrated to obtain the energies of the primaries are detailed in  Aab et al. (2020a). Use is made of hybrid events, both for showers with θ < 60°(referred to as "vertical events") and for events of larger zenith angles ("inclined events"). For vertical events, the measure of S(  1000 ) is first adjusted to the value that a shower would have if it arrived at 38°from the vertical, S 38 , as this is the median zenith angle for the vertical sample. Using the 3338 hybrid events that are available, the calibration relationship is E A S B FD 38.
The origin points to the right, looking on to the shower from the position of the station. The vertical axis is an angular elevation of the viewing direction of the FD pixels. In Figure  14  a 3D view of the event is exhibited. PAO140131 (#101). This is the second most energetic hybrid event and belongs to the data set used to calibrate events with zenith angles above 60°. The zenith angle θ = 60°. 8. The energy reconstructed from the SD signals is (78 ± 9) EeV, consistent with the energy from the fluorescence measurement of (73 ± 8) EeV. With 30 triggered stations, the footprint is elongated and covers an area of (14 × 6) km 2. At 60°, the depth of the atmosphere is twice the atmospheric vertical depth. Thus the electromagnetic component of the shower is partially quenched (see Section 3.
Acknowledgement Constructive reviews from  Hans-Michael Seitz ,  Andy Davis , and two anonymous reviewers, which greatly improved the presentation of this manuscript, are highly appreciated. The authors would like to thank  L. Vltava  for keeping the ims-1290 ion probe in top condition. Editorial handling by Associate Editor  Sasha Krot  was greatly appreciated. We are grateful for insightful discussions with  Kevin McKeegan  and  Ed Young. This work is supported by  NASA  grants  80NSSC18K0602  and  80NSSC20K0759  (to  M. -C. Liu ) and  ANR  grant  ANR-15-CE31-0004-1  (to M. Chaussidon). The UCLA ion microprobe facility is partially supported by a grant from the  NSF   Instrumentation and Facilities program.
2009). The NaI detectors cover the energy range from 8 keV to 1000 keV and are oriented in different directions around the spacecraft as to observe the entire unocculted sky. The two BGO detectors are on opposite sides of the spacecraft and cover an energy range from 200 keV to 40 MeV. The Fermi -LAT is a pair-conversion telescope at the zenith of the spacecraft and is sensitive to gamma-ray energies from 20 MeV to more than 300 GeV  (Atwood et al. 2009 ). The highest resolution Fermi -GBM data product is the Time-Tagged Event (TTE) data, tagging individual photons to 2 microsecond temporal precision at the full 128 spectral channel resolution of Fermi -GBM. Continuous Spectroscopy (CSPEC) data has the same full spectral resolution as TTE data but is a binned dataset with temporal resolution as fine as 1.
(2011)   • at trigger time and the evolution of the minimum variabtility timescale (MVT; ∆tmin) obtained from the Fermi-GBM non-BTI regions. The MVT ranges from ∼0. 01 s to ∼1 s. The decreasing MVT indicates more variability around regions with higher count rates. Bottom: Uncorrected Fermi-GBM CTIME data (0. 064 s resolution) for the same two NaI detectors during the first Fermi-GBM BTI region. out the prompt emission phase. We performed a standard polynomial fitting technique to the lightcurve over a known background region on either side of the emission episode while adjusting the polynomial fit to best match the orbital background estimate (see Figure  3 ). The χ 2 ν fit statistic for our polynomial fitting technique across all 128 CSPEC energy channels is shown at the bottom of Figure  3.
For the LLE data we used the standard polynomial fitting method with polynomial order 2 and background selection regions from t 0 -200 s to t 0 -2 s and from t 0 +550 s to t 0 +560 s. Due to the exceptionally long duration of this event, multiple detector response matrices DRMs for intervals spanning the burst duration were generated via the Fermi -GBM Response Generator 7. Both the polynomial background estimate and these DRMs were used throughout our spectral analyses. All spectral fits discussed in this section correspond to the lightcurve intervals and sub-intervals shown in Figure  1  and their fit parameters can be found in Table  1. The fit statistics quoted in Table 1 are higher than expected. We attribute this to our coarse time resolution of our spectral fits. The time intervals used are likely not well fit because the spectral evolution evolves on a very 7 https://fermi.
Pre-Main Pulse The bulk of the main emission episode begins at t 0 +176 s with a subdominant pulse from t 0 +176 s to t 0 +210 s (Figure  1 , interval III). Taken by itself, this would be one of the brightest GRBs in the Fermi -GBM sample in terms of peak flux. Although part of the main pulse, we analyzed this pulse separately because it is clearly distinct from the following bright main emission and is the last contiguous emission interval before the onset of Fermi -GBM data issues. This interval is best fit by a Band function, the parameters of this fit can be found in Table  1.
Primary Pulse We define the main pulse (Figure  1 , interval IV) as the region between t 0 +210 s to t 0 +324 s. The bulk of the main emission occurred during the first Fermi -GBM BTI, during which both Fermi -GBM and Fermi -LAT experienced data issues 5. Due to these data issues we divide this interval into three sub-intervals, before (IVa), during (IVb), and after (IVc) the first Fermi -GBM BTI (sub-intervals shown in Figure  1 ). Although the Fermi -LAT BTI falls into sub-intervals IVa and IVc, we do not attempt to correct or use any Fermi -LAT LLE data within the published Fermi -LAT BTIs. Region IVa begins before the onset of Fermi -GBM data issues and is best fit by a Band function that peaks in energy (E peak ) around 3. 7 MeV. Region IVc begins after the Fermi -GBM data issues have subsided and is best fit by a Band function with an additional PL component, extending the fit out to higher energies.