D. Noonan

Despite advances in the programmable logic capabilities of modern trigger systems, a significant bottleneck remains in the amount of data to be transported from the detector to off-detector logic where trigger decisions are made. We demonstrate that a neural network autoencoder model can be implemented in a radiation tolerant ASIC to perform lossy data compression alleviating the data transmission problem while preserving critical information of the detector energy profile. For our application, we consider the high-granularity calorimeter from the CMS experiment at the CERN Large Hadron Collider. The advantage of the machine learning approach is in the flexibility and configurability of the algorithm. By changing the neural network weights, a unique data compression algorithm can be deployed for each sensor in different detector regions, and changing detector or collider conditions. To meet area, performance, and power constraints, we perform a quantization-aware training to create an optimized neural network hardware implementation. The design is achieved through the use of high-level synthesis tools and the hls4ml framework, and was processed through synthesis and physical layout flows based on a LP CMOS 65 nm technology node. The flow anticipates 200 Mrad of ionizing radiation to select gates, and reports a total area of 3.6 mm^2 and consumes 95 mW of power. The simulated energy consumption per inference is 2.4 nJ. This is the first radiation tolerant on-detector ASIC implementation of a neural network that has been designed for particle physics applications.

Abstract Measurement of blood pH, po2 and pco2 also involves calculation of two or more derived quantities and correction of the measured values in cases where the body temperature of the patient differs from the temperature of measurement. References to the pertinent calculations and the temperature corrections are scattered through the literature of several medical specialties, and much new information has been gathered in recent years that directly affects these calculations. This review explains each of the derived quantities and correction factors most used in this field and also provides the best available data for the calculations, in a form that can readily be adapted to electronic data processing.

We present a method to resolve combinatorial issues in multi-particle final states at hadron colliders. The use of kinematic variables such as M T2 and invariant mass significantly reduces combinatorial ambiguities in the signal, but at a cost of losing statistics. We illustrate this idea with gluino pair production leading to 4 jets $ {\not{E}_T} $ in the final state as well as $ t\bar{t} $ production in the dilepton channel. Compared to results in recent studies, our method provides greater efficiency with similar purity.

Abstract With over 6 million channels, the High Granularity Calorimeter for the CMS HL-LHC upgrade presents a unique data transmission challenge. The ECON ASICs provide a critical stage of on-detector data compression and selection for the trigger path (ECON-T) and data acquisition path (ECON-D) of the HGCAL. The ASICs, fabricated in 65 nm CMOS, are radiation tolerant up to 200 Mrad and require low power consumption: < 2.5 mW/sensor-channel per chip. We report on the first functionality and radiation tests for the ECON-D-P1 full-functionality prototype. We present a comparison of single event effect (SEE) cross sections measured for different methods of triple modular redundancy using test results from the ECON-T-P1 full-functionality prototype.

Abstract We describe a procedure for the simultaneous quality control of pH, pco2,and po2 measurement. A solution of NaCl (150 mmol/liter) and NaHCO3 (50 mmol/liter) is equilibrated at room temperature (24 ± 2 °C) with a gas having the composition 21% O2, 12% CO2, 67% N2. When this solution is analyzed at 37 °C in a conventional blood-gas analyzer, a pH of 7.20, a pco2 of 110 mmHg (14.6 kPa) and a po2 of 170 mmHg (22.6 kPa) are obtained. We show that data from such an analysis, performed daily, can be simply used to meet many of the unique demands of quality control in blood pH and gas determinations. This same approach may also become the basis for devising an ideal aqueous system for blood gas calibration.

Integrating neural networks for data compression directly in the Read-Out Integrated Circuits (ROICs), i.e. the pixelated front-end, would result in a significant reduction in off-chip data transfer, overcoming the I/O bottleneck. Our ROIC test chip (AI-In-Pixel-65) is designed in a 65 nm Low Power CMOS process for the readout of pixelated X-ray detectors. Each pixel consists of an analog front-end for signal processing and a 10b analog-to-digital converter operating at 100KSPS. We compare two non-reconfigurable techniques, Principal Component Analysis (PCA) and an AutoEncoder (AE) as lossy data compression engines implemented within the pixelated area. The PCA algorithm achieves 50× compression, adds one clock cycle latency, and results in a 21% increase in the pixel area. The AE achieves 70× compression, adds 30 clock cycle latency, and results in a similar area increase.

We describe a procedure for the simultaneous quality control of pH, p co2,and p o2 measurement. A solution of NaCl (150 mmol/liter) and NaHCO3 (50 mmol/liter) is equilibrated at room temperature (24 ± 2 °C) with a gas having the composition 21% O2, 12% CO2, 67% N2. When this solution is analyzed at 37 °C in a conventional blood-gas analyzer, a pH of 7.20, a p co2 of 110 mmHg (14.6 kPa) and a p o2 of 170 mmHg (22.6 kPa) are obtained. We show that data from such an analysis, performed daily, can be simply used to meet many of the unique demands of quality control in blood pH and gas determinations. This same approach may also become the basis for devising an ideal aqueous system for blood gas calibration.

The switch-over to the use of flip-chip Si integrated circuit bonding techniques has been driven by a need to develop higher power and lower voltage devices, capable of carrying larger currents with greater reliability. With the increased use of solder bump interconnections, an understanding of the behaviour of commonly used electroless nickel under bump metallization (UBM) layers is becoming ever more crucial. The aim of this paper is to evaluate the usefulness of white beam synchrotron X-ray topography (WBSXRT) for non-destructive evaluation of the induced mechanical stresses on Si substrates for different Ni(P) based UBM sizes and thicknesses. It is shown that WBSXRT is a powerful tool for non-destructively mapping strain and/or defect distributions within the underlying silicon substrate. Using this technique, it was also found that the crystalline misorientation induced in the underlying silicon is increased for larger UBM diameters. Stress magnitudes in the Si substrate directly under the UBM can reach values as high as 260 MPa.

The Earth mover's distance (EMD) is a useful metric for image recognition and classification, but its usual implementations are not differentiable or too slow to be used as a loss function for training other algorithms via gradient descent. In this paper, we train a convolutional neural network (CNN) to learn a differentiable, fast approximation of the EMD and demonstrate that it can be used as a substitute for computing-intensive EMD implementations. We apply this differentiable approximation in the training of an autoencoder-inspired neural network (encoder NN) for data compression at the high-luminosity LHC at CERN. The goal of this encoder NN is to compress the data while preserving the information related to the distribution of energy deposits in particle detectors. We demonstrate that the performance of our encoder NN trained using the differentiable EMD CNN surpasses that of training with loss functions based on mean squared error.

Abstract The Earth mover’s distance (EMD) is a useful metric for image recognition and classification, but its usual implementations are not differentiable or too slow to be used as a loss function for training other algorithms via gradient descent. In this paper, we train a convolutional neural network (CNN) to learn a differentiable, fast approximation of the EMD and demonstrate that it can be used as a substitute for computing-intensive EMD implementations. We apply this differentiable approximation in the training of an autoencoder-inspired neural network (encoder NN) for data compression at the high-luminosity LHC at CERN The goal of this encoder NN is to compress the data while preserving the information related to the distribution of energy deposits in particle detectors. We demonstrate that the performance of our encoder NN trained using the differentiable EMD CNN surpasses that of training with loss functions based on mean squared error.

The dataset consists of simulated events of electron-positron pairs (<em>e</em>⁺<em>e</em>⁻) with flat transverse momentum <em>p</em>_T distribution <em>p</em>_T ∈ [1,200] GeV, with Phase 2 conditions, 200 pileup, V11 geometry, HLT TDR Summer20 campaign The original dataset (CMS-internal). This derived dataset in ROOT format contains generator-level particle and simulated detector information. More information about how the dataset is derived is available at this TWiki (CMS-internal). A description of each variable is below. Variable Description Type <code>run</code> Run number <code>int</code> <code>event</code> Event number <code>int</code> <code>lumi</code> Luminosity section <code>int</code> <code>gen_n</code> Number of primary generated particles <code>int</code> <code>gen_PUNumInt</code> Number of pileup interactions <code>int</code> <code>gen_TrueNumInt</code> Number of true interactions <code>float</code> <code>vtx_x</code> Simulated primary vertex <em>x</em> position in cm <code>float</code> <code>vtx_y</code> Simulated primary vertex <em>y</em> position in cm <code>float</code> <code>vtx_z</code> Simulated primary vertex <em>z</em> position in cm <code>float</code> <code>gen_eta</code> Primary generated particle pseudorapidity <em>η</em> <code>vector&lt;float&gt;</code> <code>gen_phi</code> Primary generated particle azimuthal angle <em>ϕ</em> <code>vector&lt;float&gt;</code> <code>gen_pt</code> Primary generated particle transverse momentum <em>p</em>_T in GeV <code>vector&lt;float&gt;</code> <code>gen_energy</code> Primary generated particle energy in GeV <code>vector&lt;float&gt;</code> <code>gen_charge</code> Initial generated particle charge <code>vector&lt;int&gt;</code> <code>gen_pdgid</code> Primary generated particle PDG ID <code>vector&lt;int&gt;</code> <code>gen_status</code> Primary generated particle generator status <code>vector&lt;int&gt;</code> <code>gen_daughters</code> Primary generated particle daughters (empty) <code>vector&lt;vector&lt;int&gt;&gt;</code> <code>genpart_eta</code> Primary and secondary generated particle pseudorapidity <em>η</em> <code>vector&lt;float&gt;</code> <code>genpart_phi</code> Primary and secondary generated particle azimuthal angle <em>ϕ</em> <code>vector&lt;float&gt;</code> <code>genpart_pt</code> Primary and secondary generated particle transverse momentum <em>p</em>_T in GeV <code>vector&lt;float&gt;</code> <code>genpart_energy</code> Primary and secondary generated particle energy in GeV <code>vector&lt;float&gt;</code> <code>genpart_dvx</code> Primary and secondary generated particle decay vertex <em>x</em> position in cm <code>vector&lt;float&gt;</code> <code>genpart_dvy</code> Primary and secondary generated particle decay vertex <em>y</em> position in cm <code>vector&lt;float&gt;</code> <code>genpart_dvz</code> Primary and secondary generated particle decay vertex <em>z</em> position in cm <code>vector&lt;float&gt;</code> <code>genpart_ovy</code> Primary and secondary generated particle original vertex <em>y</em> position in cm <code>vector&lt;float&gt;</code> <code>genpart_ovz</code> Primary and secondary generated particle original vertex <em>z</em> position in cm <code>vector&lt;float&gt;</code> <code>genpart_mother</code> Primary and secondary generated particle parent particle index (-1 indicates no parent) <code>vector&lt;int&gt;</code> <code>genpart_exphi</code> Primary and secondary generated particle azimuthal angle <em>ϕ</em> extrapolated to the corresponding HGCAL coordinate <code>vector&lt;float&gt;</code> <code>genpart_exeta</code> Primary and secondary generated particle pseudorapidity <em>η</em> extrapolated to the corresponding HGCAL coordinate <code>vector&lt;float&gt;</code> <code>genpart_exx</code> Primary and secondary generated particle decay vertex <em>x</em> extrapolated to the corresponding HGCAL coordinate <code>vector&lt;float&gt;</code> <code>genpart_exy</code> Primary and secondary generated particle decay vertex <em>y</em> extrapolated to the corresponding HGCAL coordinate <code>vector&lt;float&gt;</code> <code>genpart_fbrem</code> Primary and secondary generated particle decay vertex <em>z</em> extrapolated to the corresponding HGCAL coordinate <code>vector&lt;float&gt;</code> <code>genpart_pid</code> Primary and secondary generated particle PDG ID <code>vector&lt;int&gt;</code> <code>genpart_gen</code> Index of associated primary generated particle <code>vector&lt;int&gt;</code> <code>genpart_reachedEE</code> Primary and secondary generated particle flag: <code>2</code> indicates that the particle reached the HGCAL, <code>1</code> indicates the particle reached the barrel calorimeter, and <code>0</code> indicates other cases <code>vector&lt;int&gt;</code> <code>genpart_fromBeamPipe</code> Deprecated variable, always true <code>vector&lt;bool&gt;</code> <code>genpart_posx</code> Primary and secondary generated particle position <em>x</em> coordinate in cm <code>vector&lt;vector&lt;float&gt;&gt;</code> <code>genpart_posy</code> Primary and secondary generated particle position <em>y</em> coordinate in cm <code>vector&lt;vector&lt;float&gt;&gt;</code> <code>genpart_posz</code> Primary and secondary generated particle position <em>z</em> coordinate in cm <code>vector&lt;vector&lt;float&gt;&gt;</code> <code>ts_n</code> Number of trigger sums <code>int</code> <code>ts_id</code> Trigger sum ID <code>vector&lt;uint&gt;</code> <code>ts_subdet</code> Trigger sum subdetector <code>vector&lt;int&gt;</code> <code>ts_zside</code> Trigger sum endcap (plus or minus endcap) <code>vector&lt;int&gt;</code> <code>ts_layer</code> Trigger sum layer ID <code>vector&lt;int&gt;</code> <code>ts_wafer</code> Trigger sum wafer ID <code>vector&lt;int&gt;</code> <code>ts_wafertype</code> Trigger sum wafer type: 0 indicates fine divisions of wafer with 120 <em>μ</em>m thick silicon, 1 indicates coarse divisions of wafer with 200 <em>μ</em>m thick silicon, and 2 indicates coarse divisions of wafer with 300 <em>μ</em>m thick silicon <code>vector&lt;int&gt;</code> <code>ts_data</code> Trigger sum ADC value <code>vector&lt;uint&gt;</code> <code>ts_pt</code> Trigger sum transverse momentum in GeV <code>vector&lt;float&gt;</code> <code>ts_mipPt</code> Trigger sum energy in units of transverse MIP <code>vector&lt;float&gt;</code> <code>ts_energy</code> Trigger sum energy in GeV <code>vector&lt;float&gt;</code> <code>ts_eta</code> Trigger sum pseudorapidity <em>η</em> <code>vector&lt;float&gt;</code> <code>ts_phi</code> Trigger sum azimuthal angle <em>ϕ</em> <code>vector&lt;float&gt;</code> <code>ts_x</code> Trigger sum <em>x</em> position in cm <code>vector&lt;float&gt;</code> <code>ts_y</code> Trigger sum <em>y</em> position in cm <code>vector&lt;float&gt;</code> <code>ts_z</code> Trigger sum <em>z</em> position in cm <code>vector&lt;float&gt;</code> <code>tc_n</code> Number of trigger cells <code>int</code> <code>tc_id</code> Trigger cell unique ID <code>vector&lt;uint&gt;</code> <code>tc_subdet</code> Trigger cell subdetector ID (EE, EH silicon, or EH scintillator) <code>vector&lt;int&gt;</code> <code>tc_zside</code> Trigger cell endcap (plus or minus endcap) <code>vector&lt;int&gt;</code> <code>tc_layer</code> Trigger cell layer number <code>vector&lt;int&gt;</code> <code>tc_waferu</code> Trigger cell wafer <em>u</em> coordinate; <em>u</em>-axis points along − <em>x</em>-axis <code>vector&lt;int&gt;</code> <code>tc_waferv</code> Trigger cell wafer <em>v</em> coordinate; <em>v</em>-axis points at 60 degrees with respect to <em>x</em>-axis <code>vector&lt;int&gt;</code> <code>tc_wafertype</code> Trigger cell wafer type: <code>0</code> indicates fine divisions of wafer with 120 <em>μ</em>m thick silicon, <code>1</code> indicates coarse divisions of wafer with 200 <em>μ</em>m thick silicon, and <code>2</code> indicates coarse divisions of wafer with 300 <em>μ</em>m thick silicon) <code>tc_cellu</code> Trigger cell <em>u</em> coordinate within wafer; <em>u</em>-axis points along − <em>x</em>-axis <code>vector&lt;int&gt;</code> <code>tc_cellv</code> Trigger cell <em>v</em> coordinate within wafer; <em>v</em>-axis points at 60 degrees with respect to <em>x</em>-axis <code>vector&lt;int&gt;</code> <code>tc_data</code> Trigger cell ADC data at 21-bit precision after decoding from 7-bit encoding <code>vector&lt;uint&gt;</code> <code>tc_uncompressedCharge</code> Trigger cell ADC data at full precision before compression <code>vector&lt;uint&gt;</code> <code>tc_compressedCharge</code> Trigger cell ADC data compressed into 7-bit encoding <code>vector&lt;uint&gt;</code> <code>tc_pt</code> Trigger cell transverse momentum <em>p</em>_T in GeV <code>vector&lt;float&gt;</code> <code>tc_mipPt</code> Trigger cell energy in units of transverse MIPs <code>vector&lt;float&gt;</code> <code>tc_energy</code> Trigger cell energy in GeV <code>vector&lt;float&gt;</code> <code>tc_simenergy</code> Trigger cell energy from simulated particles in GeV <code>vector&lt;float&gt;</code> <code>tc_eta</code> Trigger cell pseudorapidity <em>η</em> <code>vector&lt;float&gt;</code> <code>tc_phi</code> Trigger cell azimuthal angle <em>ϕ</em> <code>vector&lt;float&gt;</code> <code>tc_x</code> Trigger cell <em>x</em> position in cm <code>vector&lt;float&gt;</code> <code>tc_y</code> Trigger cell <em>y</em> position in cm <code>vector&lt;float&gt;</code> <code>tc_z</code> Trigger cell <em>z</em> position in cm <code>vector&lt;float&gt;</code> <code>tc_cluster_id</code> ID of the 2D cluster in which the trigger cell is clustered <code>vector&lt;uint&gt;</code> <code>tc_multicluster_id</code> ID of the 3D cluster in which the trigger cell is clustered <code>vector&lt;uint&gt;</code> <code>tc_multicluster_pt</code> Transverse momentum <em>p</em>_T in GeV of the 3D cluster in which the trigger cell is clustered <code>vector&lt;float&gt;</code>

See https://doi.org/10.5281/zenodo.8338607 for a full description of this dataset.

The dataset consists of simulated events of electron-positron pairs (<em>e</em>⁺<em>e</em>⁻) with flat transverse momentum <em>p</em>_T distribution <em>p</em>_T ∈ [1,200] GeV, with Phase 2 conditions, 200 pileup, V11 geometry, HLT TDR Summer20 campaign The original dataset (CMS-internal). This derived dataset in ROOT format contains generator-level particle and simulated detector information. More information about how the dataset is derived is available at this TWiki (CMS-internal). A description of each variable is below. Variable Description Type <code>run</code> Run number <code>int</code> <code>event</code> Event number <code>int</code> <code>lumi</code> Luminosity section <code>int</code> <code>gen_n</code> Number of primary generated particles <code>int</code> <code>gen_PUNumInt</code> Number of pileup interactions <code>int</code> <code>gen_TrueNumInt</code> Number of true interactions <code>float</code> <code>vtx_x</code> Simulated primary vertex <em>x</em> position in cm <code>float</code> <code>vtx_y</code> Simulated primary vertex <em>y</em> position in cm <code>float</code> <code>vtx_z</code> Simulated primary vertex <em>z</em> position in cm <code>float</code> <code>gen_eta</code> Primary generated particle pseudorapidity <em>η</em> <code>vector&lt;float&gt;</code> <code>gen_phi</code> Primary generated particle azimuthal angle <em>ϕ</em> <code>vector&lt;float&gt;</code> <code>gen_pt</code> Primary generated particle transverse momentum <em>p</em>_T in GeV <code>vector&lt;float&gt;</code> <code>gen_energy</code> Primary generated particle energy in GeV <code>vector&lt;float&gt;</code> <code>gen_charge</code> Initial generated particle charge <code>vector&lt;int&gt;</code> <code>gen_pdgid</code> Primary generated particle PDG ID <code>vector&lt;int&gt;</code> <code>gen_status</code> Primary generated particle generator status <code>vector&lt;int&gt;</code> <code>gen_daughters</code> Primary generated particle daughters (empty) <code>vector&lt;vector&lt;int&gt;&gt;</code> <code>genpart_eta</code> Primary and secondary generated particle pseudorapidity <em>η</em> <code>vector&lt;float&gt;</code> <code>genpart_phi</code> Primary and secondary generated particle azimuthal angle <em>ϕ</em> <code>vector&lt;float&gt;</code> <code>genpart_pt</code> Primary and secondary generated particle transverse momentum <em>p</em>_T in GeV <code>vector&lt;float&gt;</code> <code>genpart_energy</code> Primary and secondary generated particle energy in GeV <code>vector&lt;float&gt;</code> <code>genpart_dvx</code> Primary and secondary generated particle decay vertex <em>x</em> position in cm <code>vector&lt;float&gt;</code> <code>genpart_dvy</code> Primary and secondary generated particle decay vertex <em>y</em> position in cm <code>vector&lt;float&gt;</code> <code>genpart_dvz</code> Primary and secondary generated particle decay vertex <em>z</em> position in cm <code>vector&lt;float&gt;</code> <code>genpart_ovy</code> Primary and secondary generated particle original vertex <em>y</em> position in cm <code>vector&lt;float&gt;</code> <code>genpart_ovz</code> Primary and secondary generated particle original vertex <em>z</em> position in cm <code>vector&lt;float&gt;</code> <code>genpart_mother</code> Primary and secondary generated particle parent particle index (-1 indicates no parent) <code>vector&lt;int&gt;</code> <code>genpart_exphi</code> Primary and secondary generated particle azimuthal angle <em>ϕ</em> extrapolated to the corresponding HGCAL coordinate <code>vector&lt;float&gt;</code> <code>genpart_exeta</code> Primary and secondary generated particle pseudorapidity <em>η</em> extrapolated to the corresponding HGCAL coordinate <code>vector&lt;float&gt;</code> <code>genpart_exx</code> Primary and secondary generated particle decay vertex <em>x</em> extrapolated to the corresponding HGCAL coordinate <code>vector&lt;float&gt;</code> <code>genpart_exy</code> Primary and secondary generated particle decay vertex <em>y</em> extrapolated to the corresponding HGCAL coordinate <code>vector&lt;float&gt;</code> <code>genpart_fbrem</code> Primary and secondary generated particle decay vertex <em>z</em> extrapolated to the corresponding HGCAL coordinate <code>vector&lt;float&gt;</code> <code>genpart_pid</code> Primary and secondary generated particle PDG ID <code>vector&lt;int&gt;</code> <code>genpart_gen</code> Index of associated primary generated particle <code>vector&lt;int&gt;</code> <code>genpart_reachedEE</code> Primary and secondary generated particle flag: <code>2</code> indicates that the particle reached the HGCAL, <code>1</code> indicates the particle reached the barrel calorimeter, and <code>0</code> indicates other cases <code>vector&lt;int&gt;</code> <code>genpart_fromBeamPipe</code> Deprecated variable, always true <code>vector&lt;bool&gt;</code> <code>genpart_posx</code> Primary and secondary generated particle position <em>x</em> coordinate in cm <code>vector&lt;vector&lt;float&gt;&gt;</code> <code>genpart_posy</code> Primary and secondary generated particle position <em>y</em> coordinate in cm <code>vector&lt;vector&lt;float&gt;&gt;</code> <code>genpart_posz</code> Primary and secondary generated particle position <em>z</em> coordinate in cm <code>vector&lt;vector&lt;float&gt;&gt;</code> <code>ts_n</code> Number of trigger sums <code>int</code> <code>ts_id</code> Trigger sum ID <code>vector&lt;uint&gt;</code> <code>ts_subdet</code> Trigger sum subdetector <code>vector&lt;int&gt;</code> <code>ts_zside</code> Trigger sum endcap (plus or minus endcap) <code>vector&lt;int&gt;</code> <code>ts_layer</code> Trigger sum layer ID <code>vector&lt;int&gt;</code> <code>ts_wafer</code> Trigger sum wafer ID <code>vector&lt;int&gt;</code> <code>ts_wafertype</code> Trigger sum wafer type: 0 indicates fine divisions of wafer with 120 <em>μ</em>m thick silicon, 1 indicates coarse divisions of wafer with 200 <em>μ</em>m thick silicon, and 2 indicates coarse divisions of wafer with 300 <em>μ</em>m thick silicon <code>vector&lt;int&gt;</code> <code>ts_data</code> Trigger sum ADC value <code>vector&lt;uint&gt;</code> <code>ts_pt</code> Trigger sum transverse momentum in GeV <code>vector&lt;float&gt;</code> <code>ts_mipPt</code> Trigger sum energy in units of transverse MIP <code>vector&lt;float&gt;</code> <code>ts_energy</code> Trigger sum energy in GeV <code>vector&lt;float&gt;</code> <code>ts_eta</code> Trigger sum pseudorapidity <em>η</em> <code>vector&lt;float&gt;</code> <code>ts_phi</code> Trigger sum azimuthal angle <em>ϕ</em> <code>vector&lt;float&gt;</code> <code>ts_x</code> Trigger sum <em>x</em> position in cm <code>vector&lt;float&gt;</code> <code>ts_y</code> Trigger sum <em>y</em> position in cm <code>vector&lt;float&gt;</code> <code>ts_z</code> Trigger sum <em>z</em> position in cm <code>vector&lt;float&gt;</code> <code>tc_n</code> Number of trigger cells <code>int</code> <code>tc_id</code> Trigger cell unique ID <code>vector&lt;uint&gt;</code> <code>tc_subdet</code> Trigger cell subdetector ID (EE, EH silicon, or EH scintillator) <code>vector&lt;int&gt;</code> <code>tc_zside</code> Trigger cell endcap (plus or minus endcap) <code>vector&lt;int&gt;</code> <code>tc_layer</code> Trigger cell layer number <code>vector&lt;int&gt;</code> <code>tc_waferu</code> Trigger cell wafer <em>u</em> coordinate; <em>u</em>-axis points along − <em>x</em>-axis <code>vector&lt;int&gt;</code> <code>tc_waferv</code> Trigger cell wafer <em>v</em> coordinate; <em>v</em>-axis points at 60 degrees with respect to <em>x</em>-axis <code>vector&lt;int&gt;</code> <code>tc_wafertype</code> Trigger cell wafer type: <code>0</code> indicates fine divisions of wafer with 120 <em>μ</em>m thick silicon, <code>1</code> indicates coarse divisions of wafer with 200 <em>μ</em>m thick silicon, and <code>2</code> indicates coarse divisions of wafer with 300 <em>μ</em>m thick silicon) <code>tc_cellu</code> Trigger cell <em>u</em> coordinate within wafer; <em>u</em>-axis points along − <em>x</em>-axis <code>vector&lt;int&gt;</code> <code>tc_cellv</code> Trigger cell <em>v</em> coordinate within wafer; <em>v</em>-axis points at 60 degrees with respect to <em>x</em>-axis <code>vector&lt;int&gt;</code> <code>tc_data</code> Trigger cell ADC data at 21-bit precision after decoding from 7-bit encoding <code>vector&lt;uint&gt;</code> <code>tc_uncompressedCharge</code> Trigger cell ADC data at full precision before compression <code>vector&lt;uint&gt;</code> <code>tc_compressedCharge</code> Trigger cell ADC data compressed into 7-bit encoding <code>vector&lt;uint&gt;</code> <code>tc_pt</code> Trigger cell transverse momentum <em>p</em>_T in GeV <code>vector&lt;float&gt;</code> <code>tc_mipPt</code> Trigger cell energy in units of transverse MIPs <code>vector&lt;float&gt;</code> <code>tc_energy</code> Trigger cell energy in GeV <code>vector&lt;float&gt;</code> <code>tc_simenergy</code> Trigger cell energy from simulated particles in GeV <code>vector&lt;float&gt;</code> <code>tc_eta</code> Trigger cell pseudorapidity <em>η</em> <code>vector&lt;float&gt;</code> <code>tc_phi</code> Trigger cell azimuthal angle <em>ϕ</em> <code>vector&lt;float&gt;</code> <code>tc_x</code> Trigger cell <em>x</em> position in cm <code>vector&lt;float&gt;</code> <code>tc_y</code> Trigger cell <em>y</em> position in cm <code>vector&lt;float&gt;</code> <code>tc_z</code> Trigger cell <em>z</em> position in cm <code>vector&lt;float&gt;</code> <code>tc_cluster_id</code> ID of the 2D cluster in which the trigger cell is clustered <code>vector&lt;uint&gt;</code> <code>tc_multicluster_id</code> ID of the 3D cluster in which the trigger cell is clustered <code>vector&lt;uint&gt;</code> <code>tc_multicluster_pt</code> Transverse momentum <em>p</em>_T in GeV of the 3D cluster in which the trigger cell is clustered <code>vector&lt;float&gt;</code>

See https://doi.org/10.5281/zenodo.8338607 for a full description of this dataset.

The long-term precision and stability of a new quality-control system for blood pH and gas measurements are compared to that of tonometered bicarbonate solutions and serum-based preparations. The new system, consisting of gas-equilibrated bicarbonate solutions in glass ampuls, is shown to be as stable as the serum-based preparation, and as reproducible as either of the other methods. The new system, offering three discrete sets of control values, has certain advantages in the simultaneous quality of pH, carbon dioxide tension, and oxygen tension measurements.

Top quark is the heaviest known elementary particle and plays a special role in the dynamics of fundamental interactions. At the LHC the top quarks are predominantly produced through strong interactions. Here, photons can originate in the final state considering initial state and final state radiations and thus involve an additional electroweak vertex [1]. Therefore, studying the top-antitop pair ( $$t\bar{t}$$ ) production in association with a photon can lead to a thorough scrutiny of the Standard Model (SM) predictions. Any deviation in the measured cross section of this process can lead to beyond standard model (BSM) physics. The results presented here are performed in events containing an well isolated, high $$p_{T}$$ lepton (electron and muon), at least four jets from the hadronization of quarks and an isolated photon. The analysis makes use of simultaneous likelihood fits in several control regions to distinguish $$t\bar{t}+ \gamma $$ signal from background.

Abstract Two different blood-gas analyzers were tested to determine the effects on blood pH measurement of changing the reference bridge solution from saturated KCl to normal saline (0.16 mol of NaCl per liter). This change, which necessitated the preparation of modified buffers equimolal in NaCl with respect to blood, virtually eliminated salt depletion of the bridge solution and improved the stability of the liquid-junction potential between the bridge solution and the sample. The instruments we used were the Corning 165 pH Blood Gas Analyzer and the Radiometer E5021 pH Electrode with PHM72 Acid Base Analyzer. Comparison of results on clinical blood samples indicates that performance with the modified bufferbridge system is the same as that obtained with the conventional scheme. Analytical performances of the Corning and Radiometer instruments for PO2 and PCO2, as well as for pH, were comparable.

ADVERTISEMENT RETURN TO ISSUEPREVBook and Media Revie...Book and Media ReviewNEXTAcid-base balance; chemistry, physiology, pathophysiology (Hills, A. Gorman)Daniel C. Noonan Cite this: J. Chem. Educ. 1974, 51, 10, A504Publication Date (Print):October 1, 1974Publication History Received3 August 2009Published online1 October 1974Published inissue 1 October 1974https://doi.org/10.1021/ed051pA504.2Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views215Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (1 MB) Get e-Alertsclose Get e-Alerts