dataesr/scientific-paragraphs-categorization huggingface dataset:
We collect data in one-day-long runs, which are interrupted for 2-3 hours every 48 hours to refill the liquid He bath and perform other maintenance on the cryogenic system. Roughly once per month, we calibrate the energy response of the detector by inserting thoriated tungsten wires inside the external lead shielding and using the characteristic γ lines from the 232 Th decay chain. These calibration runs typically last for three days. The data are combined into datasets that contain roughly three weeks of 0νββ decay physics runs flanked at the beginning and the end by a set of calibration runs. Each crystal has a typical event rate of ∼1 mHz in the physics runs and ∼60 mHz in the calibration. During the tower assembly, one NTD and one heater could not be bonded, and another heater was lost during the first cool down.
III. FIRST-LEVEL DATA PROCESSING The low-level data processing takes the CUORE-0 data from a series of triggered waveforms to a calibrated energy spectrum that will be the input into the higher-level analysis. The data processing procedure for CUORE-0 closely follows that of Cuoricino, outlined in CINO2011, but with several additions newly developed for CUORE-0. In order to estimate the energy of each event, we model the time-waveform v i (t) of each bolometer, i, as the sum of a known detector response function s i (t) and an unknown additive noise term n i (t) v i (t) = B i s i (t) + n i (t), (2) where B i is the amplitude of the signal response. To a very good approximation, this amplitude can be decomposed as B i = G i (T ) • A i (E), (3) where A i (E) depends only on the energy deposited into the bolometer E, and G i (T ) is a bolometric gain which depends on the operating temperature of the bolometer T.
The expected detector response s i (t) is computed for each bolometer over each dataset by averaging many events in the 2615 keV calibration line. The exact number of events depends on the counting rate and the amount of calibration data in a given dataset, but is typically several hundred events. The noise power spectral density N i (ω) is similarly estimated for each bolometer on each dataset by averaging the noise power spectral densities of noise samples collected throughout each run. The DOF generalizes Eqn. (4) by accounting for noise correlations between neighboring bolometers  [29, 30]. The DOF pulse for an event on bolometer i is given by V DOF i (ω) ∝ e iωtmax j S * i (ω)C -1 ij (ω)V j (ω), (5) where C -1 ij (ω) is the i, j component of the inverted noise covariance matrix at frequency ω and the sum runs over a list of correlated bolometers.
This effect is only prominent in the calibration runs where the event rate is about 60 times higher than in the physics runs and thus, in theory, does not worsen the DOF performance on the physics data and in our 0νββ analysis. However, as we show in Section VI, the calibra-tion runs are essential to determining the energy resolution input to our 0νββ decay analysis, so this makes the DOF problematic. Despite this, for some bolometers, the benefit of the decorrelation outweighs the degradation due to the higher event rate in the calibration data. Thus the final CUORE-0 dataset utilizes both the OF and the DOF, depending on which performed better on the 2615 keV 208 Tl line in the calibration data. In order to use the DOF over the OF the improvement in energy resolution at 2615 keV must be statistically significant at the 90% level.
With this requirement, 20% of the final CUORE-0 data production utilizes the DOF. Once filtered, the amplitude of each pulse is determined by interpolating the three data points around the peak of the filtered pulse and evaluating the maximum of that parabola. 2. Thermal Gain Stabilization: The thermal gain stabilization (TGS) compensates slow variation in the gain of the bolometers G i (T ) due to drifts of the operating temperature of the detector. As with the amplitude evaluation, we use two techniques in parallel: a heaterbased TGS and a calibration-based TGS. The heater-TGS is identical to the technique described in CINO2011 and described further in  [31]. This approach uses the heater attached to each bolometer to inject fixed-energy reference pulses every 300 s during each run. Since the energy of the reference pulse is constant, any variation in its measured amplitude B ref i is due to a change in the bolometric gain G i (T ).
e. , differences in the pulse shapes resulting from particle interactions and heater pulses). A failure of the heater-TGS manifests as a shift in the location of the calibration peaks between the initial and final calibration runs, visible as two distinct peaks in the calibration spectrum. In this case, we consider the entire dataset invalid for that particular bolometer. These shifted calibration datasets correspond to about 7% of our total exposure. In order to address these issues, we developed a TGS algorithm based on calibration data and independent from the heater. This approach uses the 2615 keV γline in the calibration runs in lieu of the monoenergetic pulser to map the temperature-dependent gain G i (T ). We regress the gain dependence measured in the calibration runs (see Fig. 3 ) and use this to correct the amplitudes of events in both the calibration and physics runs.
Since calibration-TGS is interpolated across an entire dataset, it requires carefully measuring and accounting for the applied and stray voltage offsets. This calibration-TGS allowed us to recover about 80% of the lost exposure on the two bolometers with broken heaters. Additionally, in cases of large temperature drifts, the calibration-TGS routinely outperformed the heater-TGS and resulted in little or no shift between the peak positions in the initial and final calibration runs, as measured on the 2615 keV line. This allowed us to recover much of the 7% of exposure that would have been rejected with the heater-TGS; and further it improved the resolution of other bolometers that showed a marginal peak shift between initial and final calibration runs, but one not large enough to be considered invalid.
All told, we used the calibration-TGS on 12% of the total CUORE-0 exposure. For the majority of the CUORE-0 data, applying the TGS caused temperature-dependent gain drifts to become a subleading cause of degradation in the energy resolution of our detector. However, in 2. 7% of the final exposure both TGS algorithms failed significantly, usually due to an abnormally large or sudden drift in temperature or a change in electronic operating conditions. These data were discarded for the rest of the analysis. 3. Energy Calibration: For each dataset, we calibrate the energy response of each bolometer A i (E) using the reconstructed positions of at least four of the seven strongest γ peaks from the 232 Th decay chain. This consists of fitting each peak position using a Gaussian lineshape plus a first degree polynomial background and performing a linear regression on the expected energies of the calibration peaks against their reconstructed positions using a second-order polynomial with zero intercept.
In Section VI, we show that a Gaussian line shape does not provide a good fit to the reconstructed peak shapes. This discrepancy leads to a small bias in the reconstructed event energies, but rather than correcting for this bias at the calibration stage, we adjust the position at which we search for a 0νββ decay signal (this is detailed in Section VI). For CUORE, we plan to improve our energy reconstruction by accounting for these non-Gaussian peak shapes during the data processing. 4. Data Blinding: The final step of the first level data processing is the blinding of the ROI. Our blinding procedure is designed to mask any possible signal or statistical fluctuation at Q ββ , while maintaining the spectral integrity so that we can use the blinded energy spectrum for testing our later analyses.
All pulse shape parameters have an energy dependence, which we normalize by interpolating across events that lie within peaks in the calibration spectrum over the range 146 keV to 2615 keV. As a result, the efficiency of the cuts on these variables is independent of energy to within statistical uncertainty over this range. We tune these pulse shape cuts by maximizing the signal efficiency over the square root of the background in the physics spectrum, where the signal efficiency is measured as the fraction of selected events in the γ peaks over the range FIG. 4. Plot of the two absorbed energies in double-crystal coincidences during physics data collection. The diagonal lines correspond to events where a γ scatters in one crystal and is then fully absorbed in another. The vertical and horizontal lines are cascade events where one γ is fully absorbed and the other is scattered.
This can be seen for the two 60 Co γ-rays, 208 Tl 2615 keV + 583 keV γ-rays, and 208 Tl pair production events where one annihilation photon escapes and is absorbed in another bolometer. 146 -2615 keV, and the background is measured in the energy regions around the peaks. To avoid biasing our selection, we use a randomly selected half of the data for tuning the selection and the remaining events for determining the selection efficiency (reported in Table  I ). We exclude the 0νββ decay ROI from both calculations.
Thus any event in coincidence with a fully absorbed 1460 keV γ-ray constitutes an accidental coincidence. Combining these, we determine the total signal efficiency of the CUORE-0 detector to be 81. 3 ± 0. 6%. The unblinding procedure was decided upon before any data were unblinded. After fixing the data selection cuts and the 0νββ decay analysis procedure (described in the next section), we unblinded the data in two stages: first Reconstructed Energy (keV) we unblinded 17 of 20 datasets (or 8 kg•yr of 130 Te exposure) and began the 0νββ decay analysis while we continued to collect the final three datasets (or 1. 8 kg•yr of 130 Te exposure). The last three datasets were blinded during collection, were subjected to the same production procedure and cuts as the rest of the data, and were to be included regardless of their effect on the final result.
The unblinded spectrum is shown in Fig. 6 , and the blinded and unblinded spectra in the ROI are shown in Fig. 7(b ). As a cross-check, we also reproduced all of the CUORE-0 data without the blinding/unblinding steps and compared them to the data that had been blinded and unblinded to confirm it had no effect on the final spectrum. Indeed, the blinding/unblinding procedure had no effect on our final spectrum. This confirmation of our blinding/unblinding procedure validates this technique moving forward to CUORE.
VI. 0νββ ANALYSIS The CUORE-0 physics spectrum over the range 300-2700 keV is shown in Fig. 6. The CUORE-0 data consists of 20 datasets collected on 51 active bolometers. After implementing all cuts, 1,008 bolometer-dataset (BoDs) pairs remain for a total TeO 2 exposure of 35. 2 kg•yr, or 9. 8 kg•yr of 130 Te. Our 0νββ decay analysis treats each one of these as a semi-independent experiment with some parameters unique to each BoDs, some parameters shared across datasets (i. e. , constant in time), and other parameters shared globally (i. e. , constant in time and uniform across the detector). We define the ROI for our 0νββ decay analysis as the range 2470-2570 keV; this region contains 233 events. This is the widest possible range that allows us to con-strain the background rate without introducing unnecessary peaks into the analysis.
The range is bounded by a 214 Bi line at 2448 keV and a small peak at 2585 keV from a 2615 keV 208 Tl γ-ray minus a 30 keV Te X-ray escape (see Fig. 7(a) ). The ROI contains the potential 0νββ decay signal at 2527 keV as well as a peak from the single-crystal coincidence of the two γ-rays from 60 Co decay which lies only 21 keV below. We attribute this 60 Co contamination to the activation of the copper frames and internal shielding  [24]. We have measured the production rate of 60 Co inside the TeO 2 crystals to be small  [33] , so we expect a negligible background from the β + γ + γ coincidence. Our 0νββ decay analysis proceeds through three steps. We first construct a detector response function ρ i for each BoDs, which characterizes the expected spectral shape of a particular bolometer's response to a mono-energetic energy deposition during a particular dataset.
A visual inspection of pulses selected from the primary and secondary peaks reveals no obvious difference in the pulse shape. The Cuoricino data shows a hint of this asymmetric line shape; however, it is the improved resolution of the CUORE-0 detector that has made this effect clear. We tested multiple models to reproduce the line shape, including a Gaussian distribution with an asymmetric tail and a triple Gaussian lineshape which modeled escapes of 4 keV characteristic X-rays from Te. Ultimately, we settled on the double-Gaussian shape which reproduced the data well across a broad range of energies. Each BoDs has its own peak position, µ i , and a single resolution parameter, σ i , for both the primary and secondary Gaussian peaks. The data suggest that the position and amplitude of the secondary Gaussian peak may vary between bolometers and in time, thus indicating that this is possibly a detector related effect.
The position of the X-ray escape peak is described as a fraction of the primary peak energy, δ Escape , and is also left unconstrained in the fit. The final parameter b Cal is a global flat background rate in counts/(keV•kg•yr), also unconstrained. We perform a simultaneous unbinned extended maximum likelihood (UEML) fit to all BoDs pairs using the RooFit fitting package  [34]. For each BoDs, this yields a set of parameters which describe the detector response function, (μ i , σi , δi , ηi ). We fix these parameters for use later in the ROI fit. Figure  8(b)  shows the resulting bestfit model to the summed calibration data over all BoDs pairs.
A. Detector Energy Resolution To characterize the energy resolution of our detector, we quote the FWHM at the 208 Tl energy in calibration runs. Each BoDs has its own best fit probability density function (PDF), for which we numerically evaluate the FWHM. We measure the FWHM of the summed primary  and secondary peaks without the background continuum. This yields a distribution of FWHM values, one for each BoDs, which is shown in Fig. 11. In order to quote a single FWHM value characteristic of the entire detector performance, we calculate an effective FWHM which is obtained by averaging detector sensitivities (i. e. , we quote the resolution of a single bolometer with equivalent sensitivity). Numerically, this is a weighted harmonic mean of resolutions: Effective FWHM = i T i i T i ∆E i , (17) where T i is the physics exposure of bolometer-dataset i.
Finally, we have implemented a data blinding technique that is both robust and effective. This analysis has also highlighted several open issues that will be addressed for CUORE. As a result of our improved energy resolution, we saw a more complicated lineshape than previously seen. We seek to understand the source of this substructure and its effect on our expected 0νββ decay signal. We found a small energydependent bias in our energy reconstruction at the level of 0. 1 keV that we seek to address in CUORE. Finally, we have seen a significant shift in the reconstructed energy of the single-crystal coincidence peaks, 60 Co and the 208 Tl single escape. Moving forward, we plan to investigate the sources of these effects and their impacts on our 0νββ analysis as we push to better understand our detectors.
ACKNOWLEDGEMENTS The CUORE Collaboration thanks the directors and staff of the  Laboratori Nazionali del Gran Sasso  and the technical staff of our laboratories. The authors wish to thank  J. Feintzeig  for carefully reviewing the manuscript,  M. Nastasi  for preparing the 60 Co calibration sources, and  F. Iachello  for helpful discussions concerning the NME literature. This work was supported by the  Istituto Nazionale di Fisica Nucleare (INFN) ; the  National Science Foundation  under Grant Nos. NSF-PHY-0605119 ,  NSF-PHY-0500337 ,  NSF-PHY-0855314 ,  NSF-PHY-0902171 ,  NSF-PHY-0969852 ,  NSF-PHY-1307204 ,  NSF-PHY-1314881 ,  NSF-PHY-1401832 , and  NSF-PHY-1404205 ; the  Alfred P. Sloan Foundation ; the  University of Wisconsin Foundation ; and  Yale University. This material is also based upon work supported by the  US Department of Energy (DOE) Office of Science  under Contract Nos.
Acknowledgments B. Ducloué  acknowledges support from the  Academy of Finland , Project No. 273464. This work was done using computing resources from  CSC -IT Center for Science in Espoo, Finland. L. Szymanowski  was partially supported by a  French Government Scholarship  and by a grant of  National EPJ Web of Conferences Science Center, Poland , No. 2015/17/B/ST2/01838. This work is partially supported by the  French Grant ANR   PARTONS  No. ANR-12-MONU-0008-01.
To investigate the performance of the muon detector at the future BC rate of 40 MHz and at higher luminosity, the knowledge of the dead-time of the CARIOCA is crucial. The present paper is divided in two parts. In the first part (Section 2) we describe the method adopted to deduce the dead-time of the CARIOCA (δ c ) from a measure of background rates (R * ) at different luminosities. These measurements were performed in dedicated runs with a non-standard data aquisition (DAQ). In the second part of this paper (Section 3) the values of δ c obtained in Section 2 are used to evaluate the muon detection inefficiency due to the CARIOCA dead-time when the experiment runs in the standard data taking conditions and at a BC rate of 20 MHz and 40 MHz.
Time distribution of the background hits The time distribution of the signals depends essentially on the time of flight of the detected particles and on the fluctuations of the response of the chamber and its readout electronics. This distribution was extracted from measurements performed in special low luminosity runs where events were acquired in a time gate of 125 ns around the triggered BC (instead of 25 ns adopted in the standard data taking)  [2, 3]. Different time distributions were measured in different detector zones. In the MC, for each BC, the hits were distributed in time according to the spectrum of.
Experimental results The counting rates of the pads belonging to the regions of station M1 and M2 equipped with MWPCs were measured in 1 s by the scalers of the CARIOCA chip. To take into account the duty cycle of LHC, the values of R * were obtained by attributing the scaler counts to a time interval of 0. 7 s. The measurements were performed at five different luminosities. Only the couples of measurements performed in the same day were chosen for the analysis  4. The data belonging to the five selected pairs of luminosities (L i , L j ) were represented on a scatter plot ρ i j versus R * j. As an example we show (Fig. 3a ) the results of the measurements performed on all the pads of station M2, and using the pair of luminosities (L 1 , L 3 ). Each point corresponds to a pad. Cathode and anode pads are shown separately.
1 ). In principle, it would be possible to combine the three SPT frequencies to reduce sensitivity to noise and tSZ signal in the CMB maps; however, the 90 and 220 GHz data are significantly noisier than the 150 GHz band so that combining them would introduce additional complexity without a substantial gain. 6 he survey was observed in 19 subpatches, or fields. Each field was observed in at least 200 individual observations, and maps from individual observations are co-added into a single map for each field. The detector time-ordered-data are filtered, then projected using the telescope pointing model into maps with the Sanson-Flamsteed projection  (Calabretta & Greisen 2002). In an update from B15, absolute calibration of the maps was derived using the 2015 release of the Planck 143 GHz data  (Planck Collaboration XI 2015).
Matched filtering and temperature estimates Since we have prior knowledge about the shape of the expected SZ signal from clusters, we can improve the S/N by filtering the CMB maps to suppress noisy modes and enhance modes with high expected signal. We use a matched filter technique (see e. g. Haehnelt & Tegmark 1996; Melin, Bartlett & Delabrouille 2006)  identical to that used in B15. The observed temperature in a direction n can be written as T ( n) = B( n) * [T clust ( n) + n astro ( n)] + n noise ( n). (13) Here, B( n) characterizes the effect of the instrumental beam and data filtering and the asterisk denotes a convolution. The SZ signal from clusters T clust ( n) is comprised of tSZ and kSZ components and n astro ( n) characterizes the noise contribution from astrophysical sources that include the lensed primary CMB, emission from dusty extragalactic sources, as well as the kinematic and thermal SZ background; all components are treated as Gaussian noise and modelled from previous SPT power spectrum measurements  (Keisler et al.
Therefore a Monte Carlo covariance is not a good choice in our case. On the other hand, one could generate the tSZ mock realizations from simulations in the same way as the one realization we use for comparison with the real data (see Section 5 below). This method becomes computationally expensive, however, for surveys that sample large volumes: when preserving the survey geometry, only four independent projections of the DES-Y1×SPT-SZ footprint can be generated from one full-sky kSZ simulation. Hence an unfeasibly large number of simulated kSZ skies (and therefore Nbody simulations) would be required to obtain a reliable covariance estimation. For these reasons, we adopt resampling techniques as our baseline choice for computing the covariance matrix; this approach also has the advantage of being model independent.
kSZ simulations We use realistic mock data in order to demonstrate the accuracy of our pairwise kSZ model and to estimate the impact of systematic effects such as redshift errors and miscentring. For these purposes, we use the simulated tSZ and kSZ maps by  (Flender et al. 2016, hereafter F16). In the following we will briefly summarize how these maps were generated, and we refer the reader to F16 for details. We will further describe the post-processing steps that lead from the full-sky maps and cluster catalogue to the realistic mock data used in this analysis. The CMB maps and cluster catalogue in F16 were generated using the output from an N-body simulation that was run using the N-body code framework HACC (Hardware/Hybrid Accelerated Cosmology Code;  Habib et al. 2016 ). This N-body simulation is part of a suite of ∼100 simulations that are being carried out under the Mira-Titan Universe project  (Heitmann et al.
We further model Poisson noise from radio galaxies and dusty star-forming galaxies, as well as the clustered component of the CIB as Gaussian random realizations, using the best-fitting model for their power spectra presented by  George et al. (2015). In order to generate realistic mock data, we apply the following post-processing steps to the full-sky maps from F16. (i) We project the full-sky CMB map (consisting of the SZ signal, primary CMB, and foregrounds) on to the SPT fields. (ii) We convolve each field with the corresponding SPT beam and filter transfer function (note that the SPT beam and filtering depends on the field and the observation year). (iii) Finally, we add to each field a random realization of the instrumental noise in that field. The noise realizations for each field are calculated directly from the data by randomly pairing all observations, subtracting one observation from the other in each pair, then co-adding the resulting difference maps.