dataesr/scientific-paragraphs-categorization huggingface dataset:
It continuously reads out voltage and current values, writing them into the above-mentioned database. Furthermore, it is capable of notifying operators of HV trips via email/SMS and attempts to automatically ramp up tripped channels if desired. In addition, it monitors the cryostat pressure via the database. If the pressure drops below a customizable threshold, all HV channels are ramped down to avoid damage due to discharges. Schedules to ramp up or down HV can also be set making automatic voltage scans possible. Figure  7  shows the pressure and temperature evolution inside the cryostat during the 222 Rn measurement described in sections 6 and 7. A typical measurement starts by filling Fig. 7  Evolution of temperature (in blue) and pressure (in red) during the 222 Rn measurement. After filling the cryostat, the cool-down phase is started  (1).
Data acquisition system The data acquisition system (DAQ) takes care of digitizing PMT waveforms. The signals are taken directly from the HeXe signal feedthroughs to a custom-made amplifier with an amplification factor of ∼2 per channel. They are then fed into discriminators with a threshold at about -10 mV. The coincidence of two discriminators above their threshold gives a trigger. Triggered data is digitized with a CAEN V1724 module which has a 100 MHz sampling rate, 14 bit resolution and a dynamic range of 2. 25 V. A custom-made software is employed to configure the module, to control data acquisition and to store the recorded waveforms on disk. Figure  8  shows an example waveform of a 83m Kr event in HeXe. The inset shows a zoom into the S1 signal region with the signals corresponding to the two characteristic decay lines.
These values are then fit with an exponential function which is used to correct the data to the value at the top of the TPC where no losses are expected. For the datasets employed for the charge and light yield measurements, an average electron lifetime is used for the correction of each field. This is enough as each field is measured for just 4 hours. Note that while the S1 z-correction can be determined once for the complete run, the S2 z-dependence is time dependent due to the continuous cleaning of the liquid xenon which results in a constantly improving S2 collection. Alpha events are selected by looking at the highest energies in the recorded spectrum and by considering the known position in the S2 versus S1 space. Therefore, only a coarse energy cut on S1 and S2 is used to select those events.
The 222 Rn data is shown in an inset. Note that the detected S1 and S2 signals are not comparable between the two datasets due to the presence of attenuators in the 222 Rn case which modifies the light collection. For the 222 Rn data, we exclude the data points below 70 V/cm from figure  15 , as the data quality is significantly better for drift fields above that. Overall data points are very clustered (due to the small quenching of alpha particles) and the fit has a significant uncertainty. This uncertainty is propagated as systematic error and it is represented in figure  17  by gray error bars. The data points for 222 Rn and 218 Po are shifted from each other (approx. 1%), likely due to the different dE/dx of the two alpha lines. Figure  16  shows the field dependence of the normalized charge and light signals for the 83m Kr decay lines.
For the Fig. 16  Light and charge yields of the 83m Kr transitions as function of applied electric field E, measured using the HeXe setup (circles). The data points from  [25]  (crosses) are also shown. The dotted lines show a fit with the modified Thomas-Imel box model given in equation 1 (data points are provided online  [29] ). combined 41. 5 keV S2 line (red), the normalization Q 0 was determined from figure  15 , following the approach described in  [25]. The normalization factors L 0 of the light signals from the 9. 4 keV and 32. 1 keV emission lines (purple and orange) are determined via extrapolation to the light signal at zero field. To avoid the variation of the 9. 4 keV signal yield with the time between the 32. 1 keV and the 9. 4 keV S1 signals as seen in  [30] , we select events in which the two corresponding S1 signals are more than 300 ns apart.
For the x-axis, the vertical component of the electric field is taken as this is what determines the electron drift. For most of the data, this vertical component corresponds to the absolute value of the electric field, except for very low fields for which the absolute field is higher due to field distortions. Most data points were measured twice, however, they are not separately visible in the figure because they lie exactly on top of each other. The only exception is the point at 13 V/cm, where in one of the two datasets the data quality was significantly poorer for unknown reasons, likely due to charge up following a HV trip. Data from the EXO-200  [36] , XENON100  [37]  and XENON1T  [38]  experiments are displayed together with the measurements of dedicated setups  [27, 35, [39] [40] [41].
The electron mobility depends on temperature roughly as µ e ∝ T -3/2  [39]. Using the dependence of the drift velocity on the electric field, v d = µ • E, the data of figure 19 could in principle be fit with a ∝ T -3/2 dependence. However, this functional dependence is unable to describe the steep increase of drift velocity at cold temperatures for high fields (E > 0. 6 kV/cm). Therefore and just to guide the eye, we fit a linear function to the points of each electric field. Our data points are acquired at a temperature close to the expected operating temperature of XENONnT and therefore are of interest for the experiment.
After a short description of the CMS detector in Section 2, the concept of track-based alignment is illustrated in Section 3, followed by a description of the data sets used to perform and validate the alignment in Section 4. We present studies that identify and minimize systematic distortions, which can be left uncorrected by the track-based alignment, in Section 5. Two algorithms, MILLEPEDE-II and HIPPY, are used for the tracker alignment. The recent software developments of these algorithms are summarized in Section 6, and the derivation of the alignment constants during data taking is described in Section 7, taking as representative examples the start-up of Run 2 and the first alignments of the pixel detector in 2017 after the Phase-1 upgrade  [3, 4]. Section 8 focuses on the strategies developed to provide the best possible alignment calibration for reprocessing the data before using them in physics analyses.
For the Phase-0 (Phase-1) detector, the half barrels in the BPIX consist of three (four) layers and the half cylinders in the FPIX consist of two (three) disks. In the barrel, groups of eight pixel modules are mounted on rods arranged in cylindrical layers. The rods are mounted such that the modules of two adjacent ladders are rotated by 180 degrees around z with respect to each other, thus having the silicon surface pointing inwards or outwards. In the FPIX, modules are supported by blades arranged in a turbine-like geometry, each hosting two modules mounted back-to-back, pointing in opposite directions. A more detailed description of the CMS detector, together with a definition of the coordinate system used and the relevant kinematic variables, can be found in Ref. [9].
Hits reconstructed with incorrect Lorentz angles are displaced in a direction defined by the orientation of the module, increasing the residual distance between the hits and the track. and p T > 1 GeV. The vast majority of the final-state particles have low p T , and their tracks are concentrated in the high-η region, as shown in Fig. 3  (top row). This figure shows the p T and η distributions for tracks from the inclusive L1 trigger data set collected by the CMS detector in 2018, after applying the track selection described above. The data are compared with Monte Carlo (MC) simulation for both low-pT and high-pT interactions, where pT is the scale in the 2 → 2 matrix element calculation of the hard process. The events are simulated using PYTHIA 8. 240  [11, 12]  with the CP5 tune  [13]. The low-and high-pT samples, which correspond to events with pT in the range of 15-30 GeV and 1000-1400 GeV, respectively, are used as two opposite reference points and the data naturally fall in between.
1, where ∆R = √ (∆η) 2 + (∆φ) 2 and ∆η and ∆φ are differences in the pseudorapidity and the azimuthal angle, respectively. Isolated muons cover a different phase space with respect to collision tracks from the inclusive L1 trigger data set, because they are Z → µµ (middle), and interfill cosmic ray muon (bottom) data sets in arbitrary units. Data collected with the CMS detector in 2018 and used for the final alignment in that year (solid black circles) are compared with the simulation (solid coloured lines). Distributions in data are obtained from a sample of 11 × 10 6 , 55 × 10 6 , and 3. 4 × 10 6 tracks for the inclusive L1 trigger, Z → µµ, and cosmic ray muon data sets, respectively. For the inclusive L1 trigger data set, data are compared with two sets of simulated QCD events with different ranges of transverse momentum transfers pT.
The green line corresponds to pT between 15 and 30 GeV, whereas the magenta line corresponds to pT between 1000 and 1400 GeV. The inset in the p T distribution of the inclusive L1 trigger data set (top left) shows the same distribution with a logarithmic scale for the y axis. No correction for the limited modelling of the trigger efficiency in the simulation has been applied for the Z → µµ data set. The statistical uncertainty is smaller than the symbol size and therefore imperceptible. characterized by a harder p T spectrum and hence a more central η distribution.
Cosmic ray muons Cosmic ray muons, referred to as cosmics or cosmic events, recorded by the CMS detector are used for detector commissioning and calibration. Before turning on the magnetic field, events are recorded during the Cosmic RUns at ZEro Tesla (CRUZET). Cosmic ray muon tracks are also recorded in the 3. 8 T magnetic field provided by the CMS solenoid, during the Cosmic Runs At Four Tesla (CRAFT). Tracks from cosmic ray muons are crucial for the derivation of the alignment constants for two main reasons. First, they can be recorded before the start of LHC collisions, and are therefore employed to derive the first alignment corrections after a shutdown period, as described in Section 7. 1. Second, they have a very different topology compared with collision tracks. Unlike collision tracks, tracks from cosmic ray muons cross the whole detector and connect modules located in the top and bottom halves of the tracker.
This breaks the cylindrical symmetry typical of collision tracks and helps to constrain several classes of systematic distortions. Figure  3  (bottom row) shows the p T and η distributions for cosmic ray muons in which the asymmetry in η is attributed to the location of the CMS cavern shaft. The MC events in the figure, used for comparison with data, are simulated using the cosmic muon generator CMSCGEN  [15]. Throughout Run 2, cosmic ray muon events were recorded before the start of LHC collisions in dedicated commissioning runs and in the time intervals between two LHC fills (interfill runs). Since 2018, it has been possible to record cosmic events during collision data taking. Figures in this paper that contain data from cosmic events are indicated with the label "cosmic rays" instead of "pp collisions".
Commissioning and interfill runs A total of 1. 72 × 10 7 and 1. 33 × 10 7 cosmic ray muon events were collected with the CMS detector during commissioning and interfill runs, respectively, from 2016 to 2018. These events are selected using an unprescaled single-muon trigger with no p T threshold applied. Figure  4  shows the average rate of cosmic ray muon events recorded by the CMS detector in this time period. Cosmic events are recorded using dedicated muon triggers. The tracks are reconstructed using three different algorithms, which are described in Ref. [16] : combinatorial track finder, cosmic track finder, and road search. Reconstructed tracks obtained by the combinatorial track finder algorithm are used for the alignment procedure, and are required to have at least seven hits, of which at least two must be in either the pixel detector or in stereo module pairs.
These tools match two geometries by translating and rotating certain structures before the differences between the geometries are calculated. These differences are treated as survey residuals in the alignment algorithm  [1]. The global shift and rotation of large structures are removed and the module displacements ∆z, ∆r, and ∆φ are measured with respect to the reference geometry as a function of z, r, and φ. The other coordinates (x, y, and z) and three angular rotations can be visualized in this manner as well. Geometry comparison validation is performed without any reconstructed track data and can be applied with reference to any prior geometry, such as design, survey, or previously aligned track-based geometry.
In particular, the fact that these tracks do not originate at the centre of the detector means they connect the top and bottom halves of the detector directly through a single track. Such a connection is not possible with tracks originating from the beam collision point. This effect is shown in Fig. 5. Figure  5 : Diagram demonstrating distortions of the tracker geometry that may not affect the consistency of a reconstructed collision track with the measured hits (left), but introduce a kink when reconstructing the two halves of a cosmic ray muon track (right), leading to an inconsistency. This illustrates the telescope effect from Table  1. Cosmic ray muon track validation can be performed without data from beam collisions and serves as an early validation of the detector geometry before LHC operation starts.
That is, the detector position remains fixed to the ideal geometry, and the geometry used in the reconstruction changes. When discussing data, the opposite convention is more natural: the geometry used in the reconstruction is initially fixed and the detector itself moves. Taking the radial misalignment as an example, a value of > 0 means that the geometry used for reconstruction is expanded in the r direction with respect to the geometry used during data taking. If this happens in data, we call it a radial contraction, because the detector has moved with respect to the expected position. The nine basic systematic distortions summarized in Table  1  are not necessarily WMs when considering all possible topologies of tracks. We found that cosmic ray muon track, overlap, and dimuon validation are sufficient to detect these global, coherent movements of modules.
However, it is easily detected using the overlap validation, since in the case of a radial expansion, modules that overlap in the radial direction will move apart uniformly. Therefore, the difference between the true and the predicted hit locations in two overlapping modules is a good indicator of a radial expansion or contraction. The linear relationship between the mean of the overlap validation figures and the magnitude of the radial misalignment are used to categorize the presence of radial expansion or contraction in pp collision data. Figure  9  (top row, middle) shows the distribution of overlaps in the φ direction for modules overlapping in the φ direction in the BPIX for collision events in MC simulation. The MC events are simulated with = 5 × 10 -4 , 2. 5 × 10 -4 , 0, -2. 5 × 10 -4 , and -5 × 10 -4.
MILLEPEDE-II The MILLEPEDE-II algorithm  [10, 18, 19]  has been discussed in the context of CMS in Ref. [2]. It is still being developed further to meet the growing user needs; in addition to CMS, the Belle-II experiment  [20]  is a main user driving the developments. In this section, we review the main algorithms implemented in the software and describe recent improvements. The MILLEPEDE-II algorithm allows determination of the position, the orientation, and the curvature of the tracker modules. The algorithm consists of two steps: MILLE This program has to be integrated into the track fitting software of the specific experiment. For each track the independent residuals with errors and the derivatives of the track (local) and module (global) parameters from Eq. (  2 ) have to be calculated and stored in custom binary files.
The track fitting method has to fit all hits simultaneously, providing the complete covariance matrix of all track parameters. This can not be provided by the standard Kalman filter  [21] , because it is a sequential method. Therefore the mathematically equivalent general broken lines method  [22, 23]  has been implemented. This is a refit of the trajectory defined by the track parameters from the Kalman filter at one given hit, e. g. the first. The output to binary MILLE files contains the subset of the trajectory attributes that are needed by MILLEPEDE-II. Only the global derivatives have to be added. PEDE This is an experiment-independent Fortran program that builds and solves the linear equation system from Eq. (  4 ). It reads a text file with steering information and the tracks with the hit information from the MILLE binary files to perform the local (track) fits to construct the global matrix C.
The number of iterations can be several dozen up to a hundred. This means multiple runs of the CPU-expensive track fits are needed, which limits the practical application of this algorithm. Advantages include the native integration with CMS software, immediately providing features such as the CMS Kalman filter code for track propagation without additional development. As a result, any constraint, such as mass or vertex constraints, implemented in the CMS software can be incorporated in the algorithm. Each iteration of the algorithm is a very simple application of a small matrix inversion. This simplicity and dependence on the CMS software makes the HIPPY algorithm complementary to MILLEPEDE-II. Since our previous publications, the most important technical improvements in the HIPPY alignment fit algorithm are: 1.
The optical survey data constraint was used during the Run 1 start-up  [1]  and was used as a constraint to a prior geometry during the Run 2 start-up. The main new features of the HIPPY algorithm discussed above are indicated in the diagram. These are the multi-IOV reconstructed track data, the sensor surface deformation database object in addition to the sensor position, the division of input track data into categories with the corresponding constraints, and the application of weights to certain types of input in the minimization process.
Start-up of Run 2 During the shutdown period between Runs 1 and 2 of the LHC, extensive maintenance and repair work was performed on the tracking detector. The pixel tracker was removed to replace the beam pipe. The pixel detector half barrel on the +x side was repaired, including replacements of several modules. During reinstallation, the barrel pixel detector was centred around the beam pipe by displacing it upward by 3. 4 mm and horizontally by 1. 3 mm relative to its previous position. The strip detector was kept in place, but the cooling system was partially replaced. Initially, the latest available geometry of the Run 1 detector was used to reconstruct the data. An observed asymmetry in the track rate in the forward pixel endcaps quickly invalidated the assumed alignment constants and provided hints of a large initial misalignment of the FPIX.
This was confirmed by the first alignment constants derived using 0 T cosmic data to determine the relative positions and rotations of the high-level mechanical structures of the pixel and strip detectors. The several million cosmic ray muon tracks collected at 3. 8 T were used to further improve the alignment configuration up to the level of single modules, by performing several successive alignment fits with an increasing number of degrees of freedom. Figure  11  shows the shifts, relative to the Run 1 geometry, of the pixel module positions measured after the module-level alignment fit. The BPIX as a whole was subject to a 3-3. 5 mm shift, attributed to the aforementioned recentring procedure of the BPIX around the beam pipe during the reinstallation. In addition, a relative shift between the two half barrel structures -π/2 < φ < π/2 and φ < -π/2, φ > π/2 was visible, which is attributed to the extensive repair and replacement work in the -π/2 < φ < π/2 half barrel.
The module-level alignment of the pixel detector was performed using the full data set collected during the CRUZET run, with around 3. 2 × 10 6 tracks used for the alignment. For the pixel modules, the APUs were reduced to 100 µm. The distributions of the unbiased track-hit residuals in the local x and y coordinates for the BPIX and FPIX are shown in Fig. 16 , for the two alignment iterations and for the initial geometry assumed before performing any alignment. After each alignment iteration, the bias and the width of the residual distributions decrease, demonstrating the reduction of systematic misalignment effects and an improved local precision. The improvements achieved by the different alignment iterations are visible in the cosmic ray muon track split validation as well. Figure  17  shows the differences in the η and φ parameters of the tracks at the different stages of the pixel detector realignment.
CMS 0T cosmic rays (2017) Figure  17 : Distributions of the difference in reconstructed parameters between the two halves of a cosmic ray muon track, split at the point of closest approach to the interaction region: track η (left) and φ (right) from a dataset recorded at 0 T. The difference is scaled by a factor √ 2 to account for the two independent measurements. The black circles show the geometry assumed before performing any alignment, the triangles show the FPIX and BPIX high-level (HL) structures alignment, and the red squares represent the module-level (ML) alignment of the pixel detector. The mean of the distributions (µ) is given in the legend. Vertical error bars represent the statistical uncertainty; they are smaller than the marker size in most of the cases. An additional alignment was performed after the CMS magnetic field was turned on, using the tracks recorded in around 300 000 CRAFT events.
The beginning of data taking in each of the three years shows a period where the automated deployment of the alignment constants was not active. In 2016 and 2017 these periods show only small movements. The movements in the corresponding period of 2018 are at a stable high level, indicating that the alignment was not corrected by an automated update. During the transition between 2017 and 2018, a few detector modules of layer 1 of the BPIX were replaced. The entire pixel detector was opened for this procedure. This resulted in large movements in the detector, as the PCL alignment correctly indicated, and a manual alignment procedure was performed. Figure  21  shows the movements of all the individual pixel modules during this transition period. Large movements in the entire pixel detector, in addition to the relative movements of the replaced parts, are clearly visible in this figure.