J. Hirschauer

We perform a study of the exclusive production of $D \bar D$, $D \bar D^*$, and $D^* \bar D^*$ in initial-state-radiation events, from $e^+ e^-$ annihilations at a center-of-mass energy near 10.58 GeV, to search for charmonium and possible new resonances. The data sample corresponds to an integrated luminosity of 384 $fb^{-1}$ and was recorded by the BaBar experiment at the PEP-II storage rings. The $D \bar D$, $D \bar D^*$, and $D^* \bar D^*$ mass spectra show clear evidence of several $\psi$ resonances. However, there is no evidence for $Y(4260) \to D \bar D^*$ or $Y(4260)\to D^* \bar D^*$.

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.

This document describes the simulation framework used in the Snowmass Energy Frontier studies for future Hadron Colliders. An overview of event generation with Madgraph5 along with parton shower and hadronization with Pythia6 is followed by a detailed description of pile-up and detector simulation with Delphes3. Details of event generation are included in a companion paper cited within this paper. The input parametrization is chosen to reflect the best object performance expected from the future ATLAS and CMS experiments; this is referred to as the "Combined Snowmass Detector". We perform simulations of pp interactions at center-of-mass energies √s = 14, 33, and 100 TeV with 0, 50, and 140 additional pp pile-up interactions. The object performance with multi-TeV pp collisions are studied for the first time using large pile-up interactions.

This document describes the novel techniques used to simulate the common Snowmass 2013 En- ergy Frontier Standard Model backgrounds for future hadron colliders. The purpose of many Energy Frontier studies is to explore the reach of high luminosity data sets at a variety of high energy collid- ers. The generation of high statistics samples which accurately model large integrated luminosities for multiple center-of-mass energies and pile-up environments is not possible using an unweighted event generation strategy | an approach which relies on event weighting was necessary. Even with these improvements in e ciency, extensive computing resources were required. This document de- scribes the speci c approach to event generation using Madgraph5 to produce parton-level processes, followed by parton showering and hadronization with Pythia6, and pile-up and detector simulation with Delphes3. The majority of Standard Model processes for pp interactions at √s = 14, 33, and 100 TeV with 0, 50, and 140 additional pile-up interactions are publicly available.

High granularity silicon pixel sensors are at the heart of energy frontier particle physics collider experiments. At an collision rate of 40\,MHz, these detectors create massive amounts of data. Signal processing that handles data incoming at those rate and intelligently reduces the data within the pixelated region of the detector \textit{at rate} will enhance physics performance and enable physics analyses that are not currently possible. Using the shape of charge clusters deposited in an array of small pixels, the physical properties of the traversing particle can be extracted with locally customized neural networks. In this first work, we present a neural network that can be embedded into the on-sensor readout and filter out hits from low momentum tracks, reducing the detector's data volume by 54.4-75.4\%. The network is designed and simulated as a custom readout integrated circuit with 28\,nm CMOS technology and is expected to operate at less than 300\,$\mu W$ with an area of less than 0.2\,mm$^2$.

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.

A measurement of the Higgs boson Yukawa coupling to the top quark is presented using proton-proton collision data at $\sqrt{s} =$ 13 TeV, corresponding to an integrated luminosity of 137 fb$^{-1}$, recorded with the CMS detector. The coupling strength with respect to the standard model value, $Y_\mathrm{t}$, is determined from kinematic distributions in $\mathrm{t\bar{t}}$ final states containing ee, $μμ$, or e$μ$ pairs. Variations of the Yukawa coupling strength lead to modified distributions for $\mathrm{t\bar{t}}$ production. In particular, the distributions of the mass of the $\mathrm{t\bar{t}}$ system and the rapidity difference of the top quark and antiquark are sensitive to the value of $Y_\mathrm{t}$. The measurement yields a best fit value of $Y_\mathrm{t} =$ 1.16 $^{+0.24}_{-0.35}$, bounding $Y_\mathrm{t}$ $\lt$ 1.54 at a 95% confidence level.

We report a two-detector measurement of the propagation speed of neutrinos over a baseline of 734 km.The measurement was made with the NuMI beam at Fermilab between the near and far MINOS detectors.The fractional difference between the neutrino speed and the speed of light is determined to be (v/c -1) = (1.0 ± 1.1) × 10 -6 , consistent with relativistic neutrinos.

Simplified Models are a useful way to characterize new physics scenarios for the LHC. Particle decays are often represented using non-renormalizable operators that involve the minimal number of fields required by symmetries. Generalizing to a wider class of decay operators allows one to model a variety of final states. This approach, which we dub the n-body extension of Simplified Models, provides a unifying treatment of the signal phase space resulting from a variety of signals. In this paper, we present the first application of this framework in the context of multijet plus missing energy searches. The main result of this work is a global performance study with the goal of identifying which set of observables yields the best discriminating power against the largest Standard Model backgrounds for a wide range of signal jet multiplicities. Our analysis compares combinations of one, two and three variables, placing emphasis on the enhanced sensitivity gain resulting from non-trivial correlations. Utilizing boosted decision trees, we compare and classify the performance of missing energy, energy scale and energy structure observables. We demonstrate that including an observable from each of these three classes is required to achieve optimal performance. This work additionally serves to establish the utility of n-body extended Simplified Models as a diagnostic for unpacking the relative merits of different search strategies, thereby motivating their application to new physics signatures beyond jets and missing energy.

A search for exclusive two-photon production via photon exchange in proton-proton collisions, pp $\to$ p$γγ$p with intact protons, is presented. The data correspond to an integrated luminosity of 9.4 fb$^{-1}$ collected in 2016 using the CMS and TOTEM detectors at a center-of-mass energy of 13 TeV at the LHC. Events are selected with a diphoton invariant mass above 350 GeV and with both protons intact in the final state, to reduce backgrounds from strong interactions. The events of interest are those where the invariant mass and rapidity calculated from the momentum losses of the forward-moving protons matches the mass and rapidity of the central, two-photon system. No events are found that satisfy this condition. Interpreting this result in an effective dimension-8 extension of the standard model, the first limits are set on the two anomalous four-photon coupling parameters. If the other parameter is constrained to its standard model value, the limits at 95% CL are $\lvertζ_1\rvert$ $\lt$ 2.9 $\times$ 10$^{-13}$ GeV$^{-4}$ and $\lvertζ_2\rvert$ $\lt$ 6.0 $\times$ 10$^{-13}$ GeV$^{-4}$.

The Phase 1 upgrade of the Hadron Calorimeter (HCAL) in the Compact Muon Solenoid (CMS) detector at the Large Hadron Collider (LHC) will include two new generations (named QIE10 and QIE11) of the radiation-tolerant flash ADC chip known as the Charge Integrator and Encoder or QIE. The QIE integrates charge from a photo sensor over a 25 ns time period and encodes the result in a non-linear digital output while having a good sensitivity in both the higher and the lower energy values. The charge integrator has the advantage of analyzing fast signals coming from the calorimeters as long as the timing and pulse information is available. The calorimeters send fast, negative polarity signals, which the QIE integrates in its non-inverting input amplifier. The input analog signal enters the QIE chip through two points: signal and reference. The chip integrates the difference between these two values. This helps in getting rid of the incoming noise, which is effectively cancelled out in the difference. Over a period of about six months between September, 2013 and April, 2014 about 320 QIE10 and about 20 QIE11 chips were tested in Fermilab using a single-chip test stand where every individual chip was tested for its characteristic features using a clam-shell. The results of those tests performed on the QIE10 and QIE11 are summarized in this document.

This report, as part of the 2021 Snowmass Process, summarizes the current status of collider physics at the Energy Frontier, the broad and exciting future prospects identified for the Energy Frontier, the challenges and needs of future experiments, and indicates high priority research areas.

We present a sensitivity study for the pair-production of supersymmetric particles which decay through R-parity violating channels. As the scope of possible RPV signatures is very broad, the reach of several selected signatures spanning a representative variety of possible final states is considered. Preference in representation is given to spectra motivated by naturalness, i.e. light higgsinos, stops and gluinos. The sensitivity studies are presented for proton-proton collisions at 14 TeV with an integrated luminosity of 300 and 3000 fb^-1, as well as at 33 TeV with an integrated luminosity of 3000 fb^-1.

In the context of time-of-flight measurements, the timing at the departure and arrival locations is obviously critical to the outcome of the experiment. In the case of neutrino time-of-flight experiments, the locations are many hundreds of kilometers apart with synchronization requirements of nanoseconds for several months at a time. In addition to the already stringent set of requirements outlined above, the locations of the origin of the particle beam and the detector need to be precisely determined. NIST and USNO have provided the MINOS (Main Injector Neutrino Oscillation Search) collaboration with both hardware and expertise to synchronize the two sites of the experiment, the accelerator at Fermilab in Batavia, IL and the Soudan Mine in northern Minnesota. Two GPS receivers are installed at each location where the local clocks are commercial Cesium clocks. Two more GPS receivers are constantly traveling between locations (including NIST in Boulder, CO) to provide multiple differential calibrations of the fixed receivers. The availability of the TWTFST equipment from USNO allowed for one comparison between the GPS and TWSTFT for the link between the locations, providing an independent means of determining the accuracy of the synchronization. Several months of continuous GPS data are now available, including the two-way calibration instance and several differential GPS calibrations. The results of data processing yielded synchronization stability below one nanosecond with accuracy at the nanosecond level over several months.

The CMS experiment at the CERN Large Hadron Collider (LHC) will upgrade the photon detection and readout systems of its barrel and endcap hadron calorimeters (HCAL) through the second long shutdown of the LHC in 2018. A central feature of this upgrade is the development of two new versions of the QIE (Charge Integrator and Encoder), a Fermilab-designed custom ASIC for measurement of charge from detectors in high-rate environments. These most recent additions to the QIE family feature 17-bits of dynamic range with 1% digitization precision for high charge and a time-to-digital converter (TDC) with half nanosecond resolution all with 16 bits of readout per bunch crossing. For the first time, the CMS experiment has produced and characterized in great detail a large volume of chips. The characteristics and performance of the new QIE and their related chip-to-chip variations as measured in a sample of 10,000 chips is described.

Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devices and systems are extremely valuable across a broad range of application domains. In scientific domains, real-time near-sensor processing can drastically improve experimental design and accelerate scientific discoveries. To support domain scientists, we have developed hls4ml, an open-source software-hardware codesign workflow to interpret and translate machine learning algorithms for implementation with both FPGA and ASIC technologies. We expand on previous hls4ml work by extending capabilities and techniques towards low-power implementations and increased usability: new Python APIs, quantization-aware pruning, end-to-end FPGA workflows, long pipeline kernels for low power, and new device backends include an ASIC workflow. Taken together, these and continued efforts in hls4ml will arm a new generation of domain scientists with accessible, efficient, and powerful tools for machine-learning-accelerated discovery.

The CMS central hadron calorimeters will undergo a complete replacement of their data acquisition system electronics. The replacement is phased, with portions of the replacement starting in 2014 and continuing through LHC Long Shutdown 2 in 2018. The existing VME electronics will be replaced with a μTCA-based system. New on-detector QIE electronics cards will transmit data at 4.8 GHz to the new μHTR cards residing in μTCA crates in the CMS electronics cavern. The μTCA crates are controlled by the AMC13, which accepts system clock and trigger throttling control from the CMS global DAQ system. The AMC13 distributes the clock to the μHTR and reads out data buffers from the μHTR into the CMS data acquisition system. The AMC 13 also provides the clock for in-crate GLIBs which in turn distribute the clock to the on-detector front end electronics. We report on the design, development status, and schedule of the DAQ system upgrades.

Abstract : The MINOS experiment uses a beam of predominantly muon-type neutrinos generated using protons from the Main Injector at Fermilab in Batavia, IL, and travelling 735 km through the Earth to a disused iron mine in Soudan, MN. The 10 microsecond-long beam pulse contains fine time structure which allows a precise measurement of the neutrino time of flight to be made. The time structure of the parent proton pulse is measured in the beamline after extraction from the Main Injector, and neutrino interactions are timestamped at the Fermilab site in the Near Detector (ND), and at the Soudan site in the Far Detector (FD). Small, transportable auxiliary detectors, consisting of scintillator planes and associated readout electronics, are used to measure the relative latency between the two large detectors. Time at each location is measured with respect to HP5071A Cesium clocks, and time is transferred using GPS Precise Point Positioning (PPP) solutions for the clock offset at each location. We describe the timing calibration of the detectors and derive a measurement of the neutrino velocity, based on data from March and April 2012. We discuss the prospects for further improvements that would yield a still more accurate result.

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.

Highly granular pixel detectors allow for increasingly precise measurements of charged particle tracks. Next-generation detectors require that pixel sizes will be further reduced, leading to unprecedented data rates exceeding those foreseen at the High Luminosity Large Hadron Collider. Signal processing that handles data incoming at a rate of O(40MHz) and intelligently reduces the data within the pixelated region of the detector at rate will enhance physics performance at high luminosity and enable physics analyses that are not currently possible. Using the shape of charge clusters deposited in an array of small pixels, the physical properties of the traversing particle can be extracted with locally customized neural networks. In this first demonstration, we present a neural network that can be embedded into the on-sensor readout and filter out hits from low momentum tracks, reducing the detector's data volume by 54.4-75.4%. The network is designed and simulated as a custom readout integrated circuit with 28 nm CMOS technology and is expected to operate at less than 300 $\mu W$ with an area of less than 0.2 mm$^2$. The temporal development of charge clusters is investigated to demonstrate possible future performance gains, and there is also a discussion of future algorithmic and technological improvements that could enhance efficiency, data reduction, and power per area.

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 combinatorics of track seeding has long been a computational bottleneck for triggering and offline computing in High Energy Physics (HEP), and remains so for the HL-LHC. Next-generation pixel sensors will be sufficiently fine-grained to determine angular information of the charged particle passing through from pixel-cluster properties. This detector technology immediately improves the situation for offline tracking, but any major improvements in physics reach are unrealized since they are dominated by lowest-level hardware trigger acceptance. We will demonstrate track angle and hit position prediction, including errors, using a mixture density network within a single layer of silicon as well as the progress towards and status of implementing the neural network in hardware on both FPGAs and ASICs.

The combinatorics of track seeding has long been a computational bottleneck for triggering and offline computing in High Energy Physics (HEP), and remains so for the HL-LHC. Next-generation pixel sensors will be sufficiently fine-grained to determine angular information of the charged particle passing through from pixel-cluster properties. This detector technology immediately improves the situation for offline tracking, but any major improvements in physics reach are unrealized since they are dominated by lowest-level hardware trigger acceptance. We will demonstrate track angle and hit position prediction, including errors, using a mixture density network within a single layer of silicon as well as the progress towards and status of implementing the neural network in hardware on both FPGAs and ASICs.

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.

Using a sample of 471×10^6 BB[overbar] events collected with the BABAR detector, we study the sum of seven exclusive final states B→X_(s(d))γ, where X_(s(d)) is a strange (nonstrange) hadronic system with a mass of up to 2.0  GeV/c^2. After correcting for unobserved decay modes, we obtain a branching fraction for b→dγ of (9.2±2.0(stat)±2.3(syst))×10^(-6) in this mass range, and a branching fraction for b→sγ of (23.0±0.8(stat)±3.0(syst))×10^(-5) in the same mass range. We find B[script](b→dγ)/B[script](b→sγ)=0.040±0.009(stat)±0.010(syst), from which we determine |V_(td)/V_(ts)|=0.199±0.022(stat)±0.024(syst)±0.002(th).

A search for long-lived particles (LLPs) produced in association with a Z boson is presented. The study is performed using data from proton-proton collisions with a center-of-mass energy of 13 TeV recorded by the CMS experiment during 2016-2018, corresponding to an integrated luminosity of 117 fb$^{-1}$. The LLPs are assumed to decay to a pair of standard model quarks that are identified as displaced jets within the CMS tracker system. Triggers and selections based on Z boson decays to electron or muon pairs improve the sensitivity to light LLPs (down to 15 GeV). This search provides sensitivity to beyond the standard model scenarios which predict LLPs produced in association with a Z boson. In particular, the results are interpreted in the context of exotic decays of the Higgs boson to a pair of scalar LLPs (H $\to$ SS). The Higgs boson decay branching fraction is constrained to values less than 6% for proper decay lengths of 10-100 mm and for LLP masses between 40 and 55 GeV. In the case of low-mass ($\approx$15 GeV) scalar particles that subsequently decay to a pair of b quarks, the search is sensitive to branching fractions $\mathcal{B}$(H $\to$ SS) $\lt$ 20% for proper decay lengths of 10-50 mm. The use of associated production with a Z boson increases the sensitivity to low-mass LLPs of this analysis with respect to gluon fusion searches. In the case of 15 GeV scalar LLPs, the improvement corresponds to a factor of 2 at a proper decay length of 30 mm.

A measurement of inclusive four-jet production in proton-proton collisions at a center-of-mass energy of 13\TeV is presented. The transverse momenta of jets within $\lvert\eta\rvert \lt$ 4.7 reach down to 35, 30, 25, and 20 GeV for the first-, second-, third-, and fourth-leading jet, respectively. Differential cross sections are measured as functions of the jet transverse momentum, jet pseudorapidity, and several other observables that describe the angular correlations between the jets. The measured distributions show sensitivity to different aspects of the underlying event, parton shower, and matrix element calculations. In particular, the interplay between angular correlations caused by parton shower and double-parton scattering contributions is shown to be important. The double-parton scattering contribution is extracted by means of a template fit to the data, using distributions for single-parton scattering obtained from Monte Carlo event generators and a double-parton scattering distribution constructed from inclusive single-jet events in data. The effective double-parton scattering cross section is calculated and discussed in view of previous measurements and of its dependence on the models used to describe the single-parton scattering background.

Transformative discovery in science is driven by innovation in technology. Our boldest undertakings in particle physics have at their foundation precision instrumentation. To reveal the profound connections underlying everything we see from the smallest scales to the largest distances in the Universe, to understand its fundamental constituents, and to reveal what is still unknown, we must invent, develop, and deploy advanced instrumentation. Investments in High Energy Physics (HEP) enabled by instrumentation have been richly rewarded with discoveries of the tiny masses of the neutrinos, the origin of mass itself: the enigmatic Higgs boson, and the surprising accelerating expansion of the Universe. What we have learned is remarkable, unexpected, exciting and mysterious; raising many new questions waiting to be answered. The quest to answer them drives innovation that improves the nation's health, wealth, and security, inspiring the public and drawing young people to science. Excellence and innovation come most effectively from diverse teams of people. Success, therefore, depends critically on attracting, engaging, and supporting a diverse cadre of young people to the field, and ensuring an inclusive environment at all levels. The program laid out in the 2014 Particle Physics Projects Prioritization Panel (P5) report "Building for Discovery - A Strategic Plan for U.S. Particle Physics in a Global Context" guides current and near future experiments to exploit these and other discoveries, and the instrumentation innovation they require, to push the frontiers of science into new territory. To explore this territory HEP will soon embark on planning the next generation of experiments. Realizing these experiments will require giant leaps in capabilities beyond the instrumentation of today. Accordingly, now is a pivotal moment to invest in the accelerated development of cost-effective instrumentation with greatly improved sensitivity and performance that will make measurable the unmeasurable, enabling a tool-driven revolution to open the door to future discoveries. Historic scientific opportunities await us, enabled by executing the instrumentation research plan outlined here.

We present updated measurements of time-dependent CP asymmetries in fully reconstructed neutral B decays containing a charmonium meson. The measurements reported here use a data sample of (465±5)×106 Υ(4S)→BB decays collected with the BABAR detector at the PEP-II asymmetric energy e+e- storage rings operating at the SLAC National Accelerator Laboratory. The time-dependent CP asymmetry parameters measured from JψKS0, JψKL0, ψ(2S)KS0, ηcKS0, χc1KS0, and J/ψK*(892)0 decays are: Cf=0.024±0.020(stat)±0.016(syst) and -ηfSf=0.687±0.028(stat)±0.012(syst).

We report a two-detector measurement of the propagation speed of neutrinos over a baseline of 734 km. The measurement was made with the NuMI beam at Fermilab between the near and far MINOS detectors. The fractional difference between the neutrino speed and the speed of light is determined to be $(v/c-1) = (1.0 \pm 1.1) \times 10^{-6}$, consistent with relativistic neutrinos.

1 Irradiation test of the HCAL Forward and Endcap upgrade electronics at the CHARM facility at CERN Francesco Costanza, Tugba Karakaya, Ozgur Sahin, (DESY, Germany) Tullio Grassi (Univ. of MD, USA), James F Hirschauer, Don Lincoln, Nadja Strobbe (Fermilab, USA), Alexander Kaminskiy (M.V. Lomonosov Moscow State University), Danila Tlisov (Russian Academy of Sciences), Yanchu Wang (Univ. ov VA, USA)

Many theories of new physics predict the existence of new particles decaying to leptons and hadronic jets, including leptoquarks of grand unified theories, heavy neutrinos and right-handed WR bosons of left-right symmetric extensions of the standard model, heavy Majorana neutrinos that mix with the standard model lepton sector, and scalar quarks in supersymmetric theories that violate R-parity. The results of searches for these particles, in final states including jets, electrons, muons, and tau leptons, are reported. The studies are based on samples of protonproton collisions at p s=7 TeV (corresponding to 5.0 fb 1 and 4.8 fb 1 of integrated luminosity) and p s = 8 TeV (corresponding to 3.6 fb 1 of integrated luminosity) collected with the CMS detector at the CERN Large Hadron Collider. Based on good agreement of the data with the standard model expectations, the data are used to determine limits on new particle production at the 95% confidence level.

Accurate timing and frequency is becoming increasingly important in many applications that influence our daily lives. Eleven out of sixteen sectors of the Critical Infrastructure and Key Resources (CIKR) identified by the Department of Homeland Security (DHS) use GPS for timing and for ten it is deemed essential. More and more systems are becoming solely dependent on GPS or other GNSS for their precise position, timing, and frequency information, especially as additional multi-constellation GNSS, i.e. Galileo, Compass, and GLONASS, and Regional Navigation Satellite Systems (RNSS) become fully operational and “fill the world’s skies.” Along with the explosive growth of systems and applications comes an increasing awareness of GNSS vulnerabilities. Interference, jamming and spoofing reduce availability and reliability of all GNSS. The General Lighthouse Authorities of the UK and Ireland have started the deployment of equipment for an Initial Operating Capability eLoran system along the east coast of the UK, and the Republic of Korea announced plans to deploy a nation-wide eLoran system. Other countries are likely to follow their example. eLoran is a High Power, Low Frequency (LF), Ground Wave radio broadcast system, capable of providing 10-meter positioning accuracy, Stratum-1 frequency distribution, and UTC timing within 100 ns across large areas. LF technology, including eLoran, is a well-established solution for providing services very similar to those delivered by GNSS, with characteristics and failure modes that are complementary to GNSS. UrsaNav has entered a Cooperative Research and Development Agreement (CRADA) with the U.S. Government, which allows using existing infrastructure to broadcast signals in the spectrum between 90-110 kHz in the U.S. UrsaNav broadcasts eLoran signals on a semi-regular basis from the former USCG Loran Support Unit in Wildwood, NJ, using a 400 kW transmitter. Monitor receivers are set up in locations in Virginia, Washington, DC, and Massachusetts to monitor the transmissions and analyze the timing performance against GPS disciplined PRS*, or better. One such monitor is installed at the U.S. Naval Observatory and is compared directly to the USNO master clock. These trials have been received with a great deal of interest in the U.S. and abroad, especially from telecommunications, power grid synchronization, and other timing application users that require alternatives or back-ups for GPS-based timing solutions. Included in the paper is a description of the transmitter and monitor receiver set-up, as well as system improvements to increase timing accuracy, such as differential eLoran. The data shows that eLoran is easily capable of sub 100 nanosecond accuracy and that further improvements can be made. This level of accuracy can be an important component in a national resilient position, navigation, and timing infrastructure. * - Throughout this paper “PRS” is used to refer to a cesium-based 5071A Primary Reference Standard (PRS).

This document describes the novel techniques used to simulate the common Snowmass 2013 Energy Frontier Standard Model backgrounds for future hadron colliders. The purpose of many Energy Frontier studies is to explore the reach of high luminosity data sets at a variety of high energy colliders. The generation of high statistics samples which accurately model large integrated luminosities for multiple center-of-mass energies and pile-up environments is not possible using an unweighted event generation strategy -- an approach which relies on event weighting was necessary. Even with these improvements in efficiency, extensive computing resources were required. This document describes the specific approach to event generation using Madgraph5 to produce parton-level processes, followed by parton showering and hadronization with Pythia6, and pile-up and detector simulation with Delphes3. The majority of Standard Model processes for pp interactions at $\sqrt(s)$ = 14, 33, and 100 TeV with 0, 50, and 140 additional pile-up interactions are publicly available.

This document describes the simulation framework used in the Snowmass Energy Frontier studies for future Hadron Colliders. An overview of event generation with {\sc Madgraph}5 along with parton shower and hadronization with {\sc Pythia}6 is followed by a detailed description of pile-up and detector simulation with {\sc Delphes}3. Details of event generation are included in a companion paper cited within this paper. The input parametrization is chosen to reflect the best object performance expected from the future ATLAS and CMS experiments; this is referred to as the "Combined Snowmass Detector". We perform simulations of $pp$ interactions at center-of-mass energies $\sqrt{s}=$ 14, 33, and 100 TeV with 0, 50, and 140 additional $pp$ pile-up interactions. The object performance with multi-TeV $pp$ collisions are studied for the first time using large pile-up interactions.

In this note we summarize the progress made by the calorimeter simulation task force (CALOTF) over the past year. The CALOTF was established in February 2008 in order to understand and reconcile the discrepancies observed between the CMS calorimetry simulation and test beam data recorded during 2004 and 2006. The simulation has been significantly improved by using a newer version of GEANT4 and an improved physics list for the full CMS detector simulation. Simulation times have been reduced by introducing flexible parameterizations to describe showering in the calorimeter (using a GFLASH-like approach) which have been tuned to the test beam data.

Charge-dependent anisotropy Fourier coefficients ($v_n$) of particle azimuthal distributions are measured in pPb and PbPb collisions at $ \sqrt{\smash[b]{s_{_{\mathrm{NN}}}}} = $ 5.02 TeV with the CMS detector at the LHC. The normalized difference in the second-order anisotropy coefficients ($v_2$) between positively and negatively charged particles is found to depend linearly on the observed event charge asymmetry with comparable slopes for both pPb and PbPb collisions over a wide range of charged particle multiplicity. In PbPb, the third-order anisotropy coefficient, $v_3$, shows a similar linear dependence with the same slope as seen for $v_2$. The observed similarities between the $v_2$ slopes for pPb and PbPb, as well as the similar slopes for $v_2$ and $v_3$ in PbPb, are compatible with expectations based on local charge conservation in the decay of clusters or resonances, and constitute a challenge to the hypothesis that the observed charge asymmetry dependence of $v_2$ in heavy ion collisions arises from a chiral magnetic wave.

The authors present preliminary measurements of time-dependent CP-violation parameters in the decay B{sup 0} {yields} {omega}K{sub S}{sup 0}, B{sup 0} {yields} {eta}{prime}K{sup 0}, B{sup 0} {yields} {pi}{sup 0}K{sub S}{sup 0}, B{sup 0} {yields} {phi}K{sub S}{sup 0}{pi}{sup 0}, and B{sup 0} {yields} K{sup +}K{sup -}K{sub S}{sup 0}, which includes the resonant final states {phi}K{sub S}{sup 0} and f{sub 0}(980)K{sub S}{sup 0}. The data sample corresponds to the full BABAR dataset of 467 x 10{sup 6} B{bar B} pairs produced at the PEP-II asymmetric-energy E{sup +}e{sup -} collider at the Stanford Linear Accelerator Center.

We describe studies of the decays of B mesons to final states ηK*(892), ηK*0(S-wave), ηK*2(1430), and η'K based on data collected with the BABAR detector at the PEP-II asymmetric-energy e+e- collier at the Stanford Linear Accelerator Center. We measure branching fractions and charge asymmetries for the decays B → ηK*, where K* indicates a spin 0, 1, or 2 Kπ system, making first observations of decays to final states ηK0*0(S-wave), ηK+*0 (S-wave), and ηK0*2(1430). We measure the time-dependent CP-violation parameters S and C for the decays B0 → η'K0, observing CP violation in a charmless B decay with 5σ significance considering both statistical and systematic uncertainties.

We summarize measurements of the CKM angle β at the B‐factories emphasizing a comparison of β measured in the B0→cc̄K(*)0 decay channels and βeff measured in b→qq̄s decay channels, such as B0→ωKS0, B0→η′K0, B0→π0KS0, and B0→S0KS0KS0.

Calorimeters will provide critical measurements at future collider detectors. As the traditional challenge of high dynamic range, high precision, and high readout rates for signal amplitudes is compounded by increasing granularity and precision timing the readout systems will become increasingly complex. This white paper reviews the challenges and opportunities in calorimeter readout at future collider detectors.

We present a flexible framework for interpretation of SUSY sensitivity studies for future colliders in terms of the phenomenological minimal supersymmetric standard model (pMSSM). We perform a grand scan of the pMSSM 19-dimensional parameter space that covers the accessible ranges of many collider scenarios, including electron, muon, and hadron colliders at a variety of center of mass energies. This enables comparisons of sensitivity and complementarity across different future experiments, including both colliders and precision measurements in the Cosmological and Rare Frontiers. The details of the scan framework are discussed, and the impact of future precision measurements on Higgs couplings, the anomalous muon magnetic moment, and dark matter quantities is presented. The next steps for this ongoing effort include performing studies with simulated events in order to quantitatively assess the sensitivity of selected future colliders in the context of the pMSSM.

We present a flexible framework for interpretation of SUSY sensitivity studies for future colliders in terms of the phenomenological minimal supersymmetric standard model (pMSSM). We perform a grand scan of the pMSSM 19-dimensional parameter space that covers the accessible ranges of many collider scenarios, including electron, muon, and hadron colliders at a variety of center of mass energies. This enables comparisons of sensitivity and complementarity across different future experiments, including both colliders and precision measurements in the Cosmological and Rare Frontiers. The details of the scan framework are discussed, and the impact of future precision measurements on Higgs couplings, the anomalous muon magnetic moment, and dark matter quantities is presented. The next steps for this ongoing effort include performing studies with simulated events in order to quantitatively assess the sensitivity of selected future colliders in the context of the pMSSM.

This is the Snowmass2021 Energy Frontier (EF) Beyond the Standard Model (BSM) report. It combines the EF topical group reports of EF08 (Model-specific explorations), EF09 (More general explorations), and EF10 (Dark Matter at Colliders). The report includes a general introduction to BSM motivations and the comparative prospects for proposed future experiments for a broad range of potential BSM models and signatures, including compositeness, SUSY, leptoquarks, more general new bosons and fermions, long-lived particles, dark matter, charged-lepton flavor violation, and anomaly detection.

This report, as part of the 2021 Snowmass Process, summarizes the current status of collider physics at the Energy Frontier, the broad and exciting future prospects identified for the Energy Frontier, the challenges and needs of future experiments, and indicates high priority research areas.