Showing papers on "Energy (signal processing) published in 2019"

Fundamentals of Wireless Information and Power Transfer: From RF Energy Harvester Models to Signal and System Designs

Abstract: Radio waves carry both energy and information simultaneously. Nevertheless, radio-frequency (RF) transmissions of these quantities have traditionally been treated separately. Currently, the community is experiencing a paradigm shift in wireless network design, namely, unifying wireless transmission of information and power so as to make the best use of the RF spectrum and radiation as well as the network infrastructure for the dual purpose of communicating and energizing. In this paper, we review and discuss recent progress in laying the foundations of the envisioned dual purpose networks by establishing a signal theory and design for wireless information and power transmission (WIPT) and identifying the fundamental tradeoff between conveying information and power wirelessly. We start with an overview of WIPT challenges and technologies, namely, simultaneous WIPT (SWIPT), wirelessly powered communication networks (WPCNs), and wirelessly powered backscatter communication (WPBC). We then characterize energy harvesters and show how WIPT signal and system designs crucially revolve around the underlying energy harvester model. To that end, we highlight three different energy harvester models, namely, one linear model and two nonlinear models, and show how WIPT designs differ for each of them in single-user and multi-user deployments. Topics discussed include rate-energy region characterization, transmitter and receiver architectures, waveform design, modulation, beamforming and input distribution optimizations, resource allocation, and RF spectrum use. We discuss and check the validity of the different energy harvester models and the resulting signal theory and design based on circuit simulations, prototyping, and experimentation. We also point out numerous directions that are promising for future research.

Cell-Free Massive MIMO: A New Next-Generation Paradigm

Abstract: Cell-free (CF) massive multiple-input-multiple-output (MIMO) systems have a large number of individually controllable antennas distributed over a wide area for simultaneously serving a small number of user equipments (UEs). This solution has been considered as a promising next-generation technology due to its ability to offer a similar quality of service to all UEs despite its low-complexity signal processing. In this paper, we provide a comprehensive survey of CF massive MIMO systems. To be more specific, the benefit of the so-called channel hardening and the favorable propagation conditions are exploited. Furthermore, we quantify the advantages of CF massive MIMO systems in terms of their energy- and cost-efficiency. Additionally, the signal processing techniques invoked for reducing the fronthaul burden for joint channel estimation and for transmit precoding are analyzed. Finally, the open research challenges in both its deployment and network management are highlighted.

Constraint on the maximum mass of neutron stars using GW170817 event

Abstract: We revisit the constraint on the maximum mass of cold spherical neutron stars coming from the observational results of GW170817. We develop a new framework for the analysis by employing both energy and angular momentum conservation laws as well as solid results of latest numerical-relativity simulations and of neutron stars in equilibrium. The new analysis shows that the maximum mass of cold spherical neutron stars can be only weakly constrained as ${M}_{\mathrm{max}}\ensuremath{\lesssim}2.3\text{ }\text{ }{M}_{\ensuremath{\bigodot}}$. Our present result illustrates that the merger remnant neutron star at the onset of collapse to a black hole is not necessarily rapidly rotating and shows that we have to take into account the angular momentum conservation law to impose the constraint on the maximum mass of neutron stars.

A Survey of Deep Learning Techniques: Application in Wind and Solar Energy Resources

Abstract: Nowadays, learning-based modeling system is adopted to establish an accurate prediction model for renewable energy resources. Computational Intelligence (CI) methods have become significant tools in production and optimization of renewable energies. The complexity of this type of energy lies in its coverage of large volumes of data and variables which have to be analyzed carefully. The present study discusses different types of Deep Learning (DL) algorithms applied in the field of solar and wind energy resources and evaluates their performance through a novel taxonomy. It also presents a comprehensive state-of-the-art of the literature leading to an assessment and performance evaluation of DL techniques as well as a discussion about major challenges and opportunities for comprehensive research. Based on results, differences on accuracy, robustness, precision values as well as the generalization ability are the most common challenges for the employment of DL techniques. In case of big dataset, the performance of DL techniques is significantly higher than that for other CI techniques. However, using and developing hybrid DL techniques with other optimization techniques in order to improve and optimize the structure of the techniques is preferably emphasized. In all cases, hybrid networks have better performance compared with single networks, because hybrid techniques take the advantages of two or more methods for preparing an accurate prediction. It is recommended to use hybrid methods in DL techniques.

An Improved Routing Schema with Special Clustering Using PSO Algorithm for Heterogeneous Wireless Sensor Network

Abstract: Energy efficiency and energy balancing are crucial research issues as per routing protocol designing for self-organized wireless sensor networks (WSNs). Many literatures used the clustering algorithm to achieve energy efficiency and energy balancing, however, there are usually energy holes near the cluster heads (CHs) because of the heavy burden of forwarding. As the clustering problem in lossy WSNs is proved to be a NP-hard problem, many metaheuristic algorithms are utilized to solve the problem. In this paper, a special clustering method called Energy Centers Searching using Particle Swarm Optimization (EC-PSO) is presented to avoid these energy holes and search energy centers for CHs selection. During the first period, the CHs are elected using geometric method. After the energy of the network is heterogeneous, EC-PSO is adopted for clustering. Energy centers are searched using an improved PSO algorithm and nodes close to the energy center are elected as CHs. Additionally, a protection mechanism is also used to prevent low energy nodes from being the forwarder and a mobile data collector is introduced to gather the data. We conduct numerous simulations to illustrate that our presented EC-PSO outperforms than some similar works in terms of network lifetime enhancement and energy utilization ratio.

Braindrop: A Mixed-Signal Neuromorphic Architecture With a Dynamical Systems-Based Programming Model

Abstract: Braindrop is the first neuromorphic system designed to be programmed at a high level of abstraction. Previous neuromorphic systems were programmed at the neurosynaptic level and required expert knowledge of the hardware to use. In stark contrast, Braindrop’s computations are specified as coupled nonlinear dynamical systems and synthesized to the hardware by an automated procedure. This procedure not only leverages Braindrop’s fabric of subthreshold analog circuits as dynamic computational primitives but also compensates for their mismatched and temperature-sensitive responses at the network level. Thus, a clean abstraction is presented to the user. Fabricated in a 28-nm FDSOI process, Braindrop integrates 4096 neurons in $0.65~\text {mm}^{2}$ . Two innovations—sparse encoding through analog spatial convolution and weighted spike-rate summation though digital accumulative thinning—cut digital traffic drastically, reducing the energy Braindrop consumes per equivalent synaptic operation to 381 fJ for typical network configurations.

Standardized Matrix Modeling of Multiple Energy Systems

Abstract: Multiple energy systems (MESs) bring together the electric power, heat, natural gas, and other systems to improve the overall efficiency of the energy system. An energy hub (EH) models an MES as a device with multiple ports using a matrix coupling the inputs and outputs. This paper proposes a standardized matrix modeling method based on the concept of EH to build the coupling matrix automatically. The components and the structure of MES are first defined using graph theory. Then, the matrices describing the topology of the MES and the characteristics of the energy converters are developed. On this basis, the energy flow equations are formulated. Gaussian elimination can then be applied to obtain the coupling matrix and analyze the degree of freedom of the EH. A standard data structure for basic information on the MES is proposed to facilitate computerized modeling. Further, extension modeling of energy storage and demand response is also discussed. Finally, a case study of a modified tri-generation system is conducted to illustrate the proposed method.

Parametric Resonance Production of Ultralight Vector Dark Matter

Abstract: Vector bosons heavier than ${10}^{\ensuremath{-}22}\text{ }\text{ }\mathrm{eV}$ can be viable dark matter candidates with distinctive experimental signatures. Ultralight dark matter generally requires a nonthermal origin to achieve the observed density, while still behaving like a pressureless fluid at late times. We show that such a production mechanism naturally occurs for vectors whose mass originates from a dark Higgs. If the dark Higgs has a large field value after inflation, the energy in the Higgs field can be efficiently transferred to vectors through parametric resonance. Computing the resulting abundance and spectra requires careful treatment of the transverse and longitudinal components, whose dynamics are governed by distinct equations of motion. We study these in detail and find that the mass of the vector may be as low as ${10}^{\ensuremath{-}18}\text{ }\text{ }\mathrm{eV}$, while making up the majority of the dark matter abundance. This opens up a wide mass range of vector dark matter as cosmologically viable, and further motivates the experimental searches for such particles.

Sparse ReRAM engine: joint exploration of activation and weight sparsity in compressed neural networks

Abstract: Exploiting model sparsity to reduce ineffectual computation is a commonly used approach to achieve energy efficiency for DNN inference accelerators. However, due to the tightly coupled crossbar structure, exploiting sparsity for ReRAM-based NN accelerator is a less explored area. Existing architectural studies on ReRAM-based NN accelerators assume that an entire crossbar array can be activated in a single cycle. However, due to inference accuracy considerations, matrix-vector computation must be conducted in a smaller granularity in practice, called Operation Unit (OU). An OU-based architecture creates a new opportunity to exploit DNN sparsity. In this paper, we propose the first practical Sparse ReRAM Engine that exploits both weight and activation sparsity. Our evaluation shows that the proposed method is effective in eliminating ineffectual computation, and delivers significant performance improvement and energy savings.

7.7 LNPU: A 25.3TFLOPS/W Sparse Deep-Neural-Network Learning Processor with Fine-Grained Mixed Precision of FP8-FP16

Abstract: Recently, deep neural network (DNN) hardware accelerators have been reported for energy-efficient deep learning (DL) acceleration [1–6]. Most prior DNN inference accelerators are trained in the cloud using public datasets; parameters are then downloaded to implement AI [1–5]. However, local DNN learning with domain-specific and private data is required meet various user preferences on edge or mobile devices. Since edge and mobile devices contain only limited computation capability with battery power, an energy-efficient DNN learning processor is necessary. Only [6] supported on-chip DNN learning, but it was not energy-efficient, as it did not utilize sparsity which represents 37%-61% of the inputs for various CNNs, such as VGG16, AlexNet and ResNet-18, as shown in Fig. 7.7.1. Although [3–5] utilized the sparsity, they only considered the inference phase with inter-channel accumulation in Fig. 7.7.1, and did not support intra-channel accumulation for the weight-gradient generation (WG) step of the learning phase. Also, [6] adopted FP16, but it was not energy optimal because FP8 is enough for many input operands with 4× less energy than FP16.

Interlayer hybridization and moiré superlattice minibands for electrons and excitons in heterobilayers of transition-metal dichalcogenides

Abstract: Geometrical moir\'e patterns, generic for almost aligned bilayers of two-dimensional crystals with similar lattice structure but slightly different lattice constants, lead to zone folding and miniband formation for electronic states. Here, we show that moir\'e superlattice (mSL) effects in $\mathrm{MoSe}{}_{2}/\mathrm{WS}{}_{2}$ and $\mathrm{MoTe}{}_{2}/\mathrm{MoSe}{}_{2}$ heterobilayers that feature alignment of the band edges are enhanced by resonant interlayer hybridization, and anticipate similar features in twisted homobilayers of transition-metal dichalcogenides (TMDs), including examples of narrow minibands close to the actual band edges. Such hybridization determines the optical activity of interlayer excitons in TMD heterostructures, as well as energy shifts in the exciton spectrum. We show that the resonantly hybridized exciton energy should display a sharp modulation as a function of the interlayer twist angle, accompanied by additional spectral features caused by umklapp electron-photon interactions with the mSL. We analyze the appearance of resonantly enhanced mSL features in absorption and emission of light by the interlayer exciton hybridization with both intralayer $A$ and $B$ excitons in $\mathrm{MoSe}{}_{2}/\mathrm{WS}{}_{2}, \mathrm{MoTe}{}_{2}/\mathrm{MoSe}{}_{2}, \mathrm{MoSe}{}_{2}/\mathrm{MoS}{}_{2}, \mathrm{WS}{}_{2}/\mathrm{MoS}{}_{2}$, and $\mathrm{WSe}{}_{2}/\mathrm{MoSe}{}_{2}$.

Micro-UAV Detection and Classification from RF Fingerprints Using Machine Learning Techniques

Abstract: This paper focuses on the detection and classification of micro-unmanned aerial vehicles (UAVs)using radio frequency (RF)fingerprints of the signals transmitted from the controller to the micro-UAV. In the detection phase, raw signals are split into frames and transformed into the wavelet domain to remove the bias in the signals and reduce the size of data to be processed. A naive Bayes approach, which is based on Markov models generated separately for UAV and non-UAV classes, is used to check for the presence of a UAV in each frame. In the classification phase, unlike the traditional approaches that rely solely on time-domain signals and corresponding features, the proposed technique uses the energy transient signal. This approach is more robust to noise and can cope with different modulation techniques. First, the normalized energy trajectory is generated from the energy-time-frequency distribution of the raw control signal. Next, the start and end points of the energy transient are detected by searching for the most abrupt changes in the mean of the energy trajectory. Then, a set of statistical features is extracted from the energy transient. Significant features are selected by performing neighborhood component analysis (NCA)to keep the computational cost of the algorithm low. Finally, selected features are fed to several machine learning algorithms for classification. The algorithms are evaluated experimentally using a database containing 100 RF signals from each of 14 different UAV controllers. The signals are recorded wirelessly using a high-frequency oscilloscope. The data set is randomly partitioned into training and test sets for validation with the ratio 4:1. Ten Monte Carlo simulations are run and results are averaged to assess the performance of the methods. All the micro-UAVs are detected correctly and an average accuracy of 96.3% is achieved using the k-nearest neighbor (kNN)classification. Proposed methods are also tested for different signal-to-noise ratio (SNR)levels and results are reported.

Low-Complexity Non-Intrusive Load Monitoring Using Unsupervised Learning and Generalized Appliance Models

Abstract: Awareness of electric energy usage has both societal and economic benefits, which include reduced energy bills and stress on non-renewable energy sources. In recent years, there has been a surge in interest in the field of load monitoring, also referred to as energy disaggregation, which involves methods and techniques for monitoring electric energy usage and providing appropriate feedback on usage patterns to homeowners. The use of unsupervised learning in non-intrusive load monitoring (NILM) is a key area of study, with practical solutions having wide implications for energy monitoring. In this paper, a low-complexity unsupervised NILM algorithm is presented, which is designed toward practical implementation. The algorithm is inspired by a fuzzy clustering algorithm called entropy index constraints competitive agglomeration, but facilitated and improved in a practical load monitoring environment to produce a set of generalized appliance models for the detection of appliance usage within a household. Experimental evaluation conducted using energy data from the reference energy data disaggregation dataset indicates that the algorithm has out-performance for event detection compared with recent state-of-the-art work for unsupervised NILM when considering common NILM metrics, such as accuracy, precision, recall, ${F}$ -measure, and total energy correctly assigned.

An Ultra-Dense 2FeFET TCAM Design Based on a Multi-Domain FeFET Model

Abstract: Ternary content addressable memories (TCAMs) represent a form of logic-in-memory and are currently widely used in routers, caches, and efficient machine learning models. From a technology prospective, researchers have begun to consider various non-volatile (NV) memory technologies to design NV TCAMs that may offer improvements with respect to figures of merit, such as energy and delay when compared to conventional CMOS designs. Among these devices, ferroelectric field effect transistors (FeFETs) stand out due to their high $ {I}_{\text {ON}}/{I}_{\text {OFF}}$ ratio, efficient voltage-driven write mechanism, low-cost, and CMOS-compatible fabrication process. We propose a 2FeFET TCAM design based on a state-of-the-art, experimentally calibrated FeFET model. We evaluate and compare our design with other TCAMs at the cell and array levels. Our results suggest that a 2FeFET TCAM requires $3.5{\times }/3200 {\times }$ less write energy than CMOS/resistive random access memory (ReRAM) TCAMs, respectively. The cell area is 13% of that of a CMOS TCAM, and is on par with ReRAM designs. The search energy-delay-product of a 2FeFET TCAM is also $4.1 {\times }/2.8 {\times }$ less than CMOS/ReRAM TCAMs, respectively.

Fingerprints of heavy element nucleosynthesis in the late-time lightcurves of kilonovae

Abstract: The kilonova emission observed following the binary neutron star merger event GW170817 provided the first direct evidence for the synthesis of heavy nuclei through the rapid neutron capture process ($r$ process) The late-time transition in the spectral energy distribution to near-infrared wavelengths was interpreted as indicating the production of lanthanide nuclei, with atomic mass number $A\ensuremath{\gtrsim}140$ However, compelling evidence for the presence of even heavier third-peak ($A\ensuremath{\approx}195$) $r$-process elements (eg, gold, platinum) or translead nuclei remains elusive At early times ($\ensuremath{\sim}\mathrm{days}$) most of the $r$-process heating arises from a large statistical ensemble of $\ensuremath{\beta}$ decays, which thermalize efficiently while the ejecta is still dense, generating a heating rate that is reasonably approximated by a single power law However, at later times of weeks to months, the decay energy input can also possibly be dominated by a discrete number of $\ensuremath{\alpha}$ decays, $^{223}\mathrm{Ra}$ (half-life ${t}_{1/2}=1143\text{ }\text{ }\mathrm{d}$), $^{225}\mathrm{Ac}$ (${t}_{1/2}=100\text{ }\text{ }\mathrm{d}$, following the $\ensuremath{\beta}$ decay of $^{225}\mathrm{Ra}$ with ${t}_{1/2}=149\text{ }\text{ }\mathrm{d}$), and the fissioning isotope $^{254}\mathrm{Cf}$ (${t}_{1/2}=605\text{ }\text{ }\mathrm{d}$), which liberate more energy per decay and thermalize with greater efficiency than $\ensuremath{\beta}$-decay products Late-time nebular observations of kilonovae which constrain the radioactive power provide the potential to identify signatures of these individual isotopes, thus confirming the production of heavy nuclei In order to constrain the bolometric light to the required accuracy, multiepoch and wideband observations are required with sensitive instruments like the James Webb Space Telescope In addition, by comparing the nuclear heating rate obtained with an abundance distribution that follows the solar $r$ abundance pattern, to the bolometric lightcurve of AT2017gfo, we find that the yet-uncertain $r$ abundance of $^{72}\mathrm{Ge}$ plays a decisive role in powering the lightcurve, if one assumes that GW170817 has produced a full range of the solar $r$ abundances down to mass number $A\ensuremath{\sim}70$

Thermal vorticity and spin polarization in heavy-ion collisions

Abstract: The hot and dense matter generated in heavy-ion collisions contains intricate vortical structure in which the local fluid vorticity can be very large. Such vorticity can polarize the spin of the produced particles. We study the event-by-event generation of the so-called thermal vorticity in Au+Au collisions at energy region $\sqrt{s}=7.7--200$ GeV and calculate its time evolution, spatial distribution, etc., in a multiphase transport model. We then compute the spin polarization of the $\mathrm{\ensuremath{\Lambda}}$ and $\overline{\mathrm{\ensuremath{\Lambda}}}$ hyperons as a function of $\sqrt{s}$, transverse momentum ${p}_{T}$, rapidity, and azimuthal angle. Furthermore, we study the harmonic flow of the spin, in a manner analogous to the harmonic flow of the particle number. The measurement of the spin harmonic flow may provide a way to probe the vortical structure in heavy-ion collisions. We also discuss the spin polarization of ${\mathrm{\ensuremath{\Xi}}}^{0}$ and ${\mathrm{\ensuremath{\Omega}}}^{\ensuremath{-}}$ hyperons, which may provide further information about the spin polarization mechanism of hadrons.

Thouless and relaxation time scales in many-body quantum systems.

Abstract: A major open question in studies of nonequilibrium quantum dynamics is the identification of the time scales involved in the relaxation process of isolated quantum systems that have many interacting particles. We demonstrate that long time scales can be analytically found by analyzing dynamical manifestations of spectral correlations. Using this approach, we show that the Thouless time ${t}_{\text{Th}}$ and the relaxation time ${t}_{\text{R}}$ increase exponentially with system size. We define ${t}_{\text{Th}}$ as the time at which the spread of the initial state in the many-body Hilbert space is complete and verify that it agrees with the inverse of the Thouless energy. ${t}_{\text{Th}}$ marks the point beyond which the dynamics acquire universal features, while relaxation happens later when the evolution reaches a stationary state. In chaotic systems, ${t}_{\text{Th}}\ensuremath{\ll}{t}_{\text{R}}$, while for systems approaching a many-body localized phase, ${t}_{\text{Th}}\ensuremath{\rightarrow}{t}_{\text{R}}$. Our analytical results for ${t}_{\text{Th}}$ and ${t}_{\text{R}}$ are obtained for the survival probability, which is a global quantity. We show numerically that the same time scales appear also in the evolution of the spin autocorrelation function, which is an experimental local observable. Our studies are carried out for realistic many-body quantum models. The results are compared with those for random matrices.

Eco-Approach and Departure (EAD) Application for Actuated Signals in Real-World Traffic

Abstract: The connected vehicle eco-approach and departure (EAD) application for signalized intersections has been widely studied and is deemed to be effective in terms of reducing energy consumption and both greenhouse gas and other criteria pollutant emissions. Prior studies have shown that tangible environmental benefits can be gained by communicating the driver with the signal phase and timing (SPaT) information of the upcoming traffic signals with fixed time control to the driver. However, similar applications to actuated signals pose a significant challenge due to their randomness to some extent caused by vehicle actuation. Based on the framework previously developed by the authors, a real-world testing has been conducted along the El Camino Real corridor in Palo Alto, CA, USA, to evaluate the system performance in terms of energy savings and emissions reduction. Strategies and algorithms are designed to be adaptive to the dynamic uncertainty for actuated signal and real-world traffic. It turns out that the proposed EAD system can save 6% energy for the trip segments when activated within DSRC ranges and 2% energy for all trips. The proposed system can also reduce 7% of CO, 18% of HC, and 13% of NOx for all trips. Those results are compatible with the simulation results and validate the previously developed EAD framework.

IoT Communications With $M$ -PSK Modulated Ambient Backscatter: Algorithm, Analysis, and Implementation

Abstract: Ambient backscatter (AB), making use of both energy harvesting and backscattering, has recently become a promising solution to communications among low-power devices and demonstrates its potential application in the Internet of Things. Existing AB systems adopt two-state amplitude shift keying or phase shift keying (PSK), where data are transmitted at the rate of one bit per symbol period. To increase the data rate, we investigate the high-order modulation where ${M}$ -PSK is employed for backscattering. We derive the optimal multilevel energy detector and compute the closed-form symbol error rate. To show the realizability of the proposed design, we build a 4PSK-AB hardware prototype, in which the selection of load impedance is discussed with the aid of phasor diagram illustration. The hardware prototype can achieve the date rate of 20 kb/s. Besides, higher date rate is achievable for 98.7% of the time compared with binary AB communications, and the mean number of distinguishable symbols is 3.66.

Direct Determination of Band-Gap Renormalization in the Photoexcited Monolayer MoS 2

Abstract: A key feature of monolayer semiconductors, such as transition-metal dichalcogenides, is the poorly screened Coulomb potential, which leads to a large exciton binding energy (${E}_{b}$) and strong renormalization of the quasiparticle band gap (${E}_{g}$) by carriers. The latter has been difficult to determine due to a cancellation in changes of ${E}_{b}$ and ${E}_{g}$, resulting in little change in optical transition energy at different carrier densities. Here, we quantify band-gap renormalization in macroscopic single crystal ${\mathrm{MoS}}_{2}$ monolayers on ${\mathrm{SiO}}_{2}$ using time and angle-resolved photoemission spectroscopy. At an excitation density above the Mott threshold, ${E}_{g}$ decreases by as much as 360 meV. We compare the carrier density-dependent ${E}_{g}$ with previous theoretical calculations and show the necessity of knowing both doping and excitation densities in quantifying the band gap.

Extracting nuclear symmetry energies at high densities from observations of neutron stars and gravitational waves

Abstract: By numerically inverting the Tolman-Oppenheimer-Volkov (TOV) equation using an explicitly isospin-dependent parametric Equation of State (EOS) of dense neutron-rich nucleonic matter, a restricted EOS parameter space is established using observational constraints on the radius, maximum mass, tidal deformability and causality condition of neutron stars (NSs). The constraining band obtained for the pressure as a function of energy (baryon) density is in good agreement with that extracted recently by the LIGO+Virgo Collaborations from their improved analyses of the NS tidal deformability in GW170817. Rather robust upper and lower boundaries on nuclear symmetry energies are extracted from the observational constraints up to about twice the saturation density $\rho_{0}$ of nuclear matter. More quantitatively, the symmetry energy at $2\rho_{0}$ is constrained to $ E_{\mathrm{sym}}(2\rho_{0})= 46.9\pm 10.1$ MeV excluding many existing theoretical predictions scattered between $ E_{\mathrm{sym}}(2\rho_{0}) =15$ and 100 MeV. Moreover, by studying variations of the causality surface where the speed of sound equals that of light at central densities of the most massive neutron stars within the restricted EOS parameter space, the absolutely maximum mass of neutron stars is found to be 2.40 $ \mathrm{M}_{\odot}$ approximately independent of the EOSs used. This limiting mass is consistent with findings of several recent analyses and numerical general relativity simulations about the maximum mass of the possible super-massive remanent produced in the immediate aftermath of GW170817. deformability

The High Energy X-ray telescope (HE) onboard the Insight-HXMT astronomy satellite

Abstract: The Insight-Hard X-ray Modulation Telescope (Insight-HXMT) is a broad band X-ray and gamma-ray (1-3000 keV) astronomy satellite. The High Energy X-ray telescope (HE) is one of its three main telescopes. The main detector plane of HE is composed of 18 NaI(Tl)/CsI(Na) phoswich detectors, where NaI(Tl) serves as primary detector to measure ~ 20-250 keV photons incident from the field of view (FOV) defined by the collimators, and CsI(Na) is used as an active shield detector to NaI(Tl) by pulse shape discrimination. CsI(Na) is also used as an omnidirectional gamma-ray monitor. The HE collimators have a diverse FOV: 1.1{\deg}x 5.7{\deg} (15 units), 5.7{\deg}x 5.7{\deg} (2 units) and blocked (1 unit), thus the combined FOV of HE is about 5.7{\deg}x 5.7{\deg}. Each HE detector has a diameter of 190 mm, resulting in the total geometrical area of about 5100 cm_2. The energy resolution is ~15% at 60 keV. The timing accuracy is better than 10 {\mu}s and dead-time for each detector is less than 10 {\mu}s. HE is devoted to observe the spectra and temporal variability of X-ray sources in the 20-250 keV band either by pointing observations for known sources or scanning observations to unveil new sources, and to monitor the gamma-ray sky in 0.2-3 MeV. This paper presents the design and performance of the HE instruments. Results of the on-ground calibration experiments are also reported.