Measurement of the Higgs boson mass and width using the four-lepton final state in proton-proton collisions at √s = 13 TeV

 

A measurement of the Higgs boson mass and width via its decay to two Z bosons is presented. Proton-proton collision data collected by the CMS experiment, corresponding to an integrated luminosity of 138 fb^−1 at a center-of-mass energy of 13 TeV, is used. The invariant mass distribution of four leptons in the on-shell Higgs boson decay is used to measure its mass and constrain its width. This yields the most precise single measurement of the Higgs boson mass to date, 125.04 ± 0.12 GeV, and an upper limit on the width Γ H < 330 MeV at 95% confidence level. A combination of the on-and off-shell Higgs boson production decaying to four leptons is used to determine the Higgs boson width, assuming that no new virtual particles affect the production, a premise that is tested by adding new heavy particles in the gluon fusion loop model. This result is combined with a previous CMS analysis of the off-shell Higgs boson production with decay to two leptons and two neutrinos, giving a measured Higgs boson width of 3.0 +2.0, −1.5 MeV, in agreement with the standard model prediction of 4.1 MeV. The strength of the off-shell Higgs boson production is also reported. The scenario of no off-shell Higgs boson production is excluded at a confidence level corresponding to 3.8 standard deviations.

Higgs boson mass and width measurements with on-shell production

The Higgs boson mass and width are measured, using on-shell production, by fitting the m4ℓ distribution in the mass range 105 < m4ℓ < 140 GeV. The results have been determined using the CMS statistical analysis tool COMBINE, which is based on the ROOFIT and ROOSTATS frameworks. Table 1 shows the mass measurements obtained from the 1D approach, where no further assumptions have been made. In comparison to the 1D model, the 1D′BS model reduces the uncertainty by about 15%. Implementing the δm4ℓ/m4ℓ categorization then gives the N –1D′BS model, which leads to an additional 10% improvement. Finally, using the D kin, bkg discriminant to reduce the background produces the N –2D′BS model with another 4% improvement. Table 5 shows the resulting m4ℓ measurements using this last model. All the measured m4ℓ values from the different fits are statistically compatible, given their uncertainties and correlations. Figure 1 displays the observed 1D likelihood scans as functions of mH, from the fits for the different 4ℓ categories and combined. Combining all the m4ℓ final states and data-taking years, our final result is mH = 125.04 ± 0.11 (stat) ± 0.05 (syst) = 125.04 ± 0.12 GeV. The largest systematic uncertainty is from the lepton momentum scale and equals 0.03 and 0.04 GeV for final states with muons and electrons, respectively. 

Table 1: Best fit values for the mass of the Higgs boson measured in the inclusive 4ℓ final state and separately for different flavor categories using the 1D approach. Uncertainties are separated into statistical and systematic uncertainties. Expected uncertainties are also given assuming mH = 125.38 GeV 

Table 2: Best fit values for the mass of the Higgs boson measured in the inclusive 4ℓ final state and separately for different flavor categories, using the final fit configuration (N –2D’BS). Uncertainties are separated into statistical and systematic uncertainties. Expected uncertainties are also given assuming mH = 125.38 GeV.

As a check on the analysis technique and the systematic uncertainty from this method, the 1D′BS model is applied to Z → 4ℓ events in the m4ℓ range 70–105 GeV. The signal shape is obtained using a convolution of a Breit–Wigner function and a double-sided Crystal Ball function. The fitted values of mZ in different subchannels are m4µ Z = 91.02 ± 0.14 GeV, m4e Z = 91.18 ± 0.45 GeV, m2e2µ Z = 91.40 ± 0.29 GeV, and m2e2µ Z = 91.40 ± 0.37 GeV, leading to a combined value of mZ = 91.17 ± 0.12 GeV, consistent with the world-average Z boson mass and with the uncertainty in agreement with the expected value of ± 0.12 GeV from simulation. The results from this analysis are combined with those extracted using data recorded with the CMS detector during Run 1 at √ s = 7 and 8 TeV. Since this analysis uses an improved method to extract the systematic uncertainties affecting lepton momentum, the lepton energy scales and resolution uncertainties are considered uncorrelated between the two runs. The combined observed result from both data-taking periods is mH = 125.08 ± 0.12 GeV = 125.08 ± 0.10 (stat)±0.05 (syst) GeV. The corresponding expected statistical and systematic uncertainties are ±0.10 and ±0.05 GeV, respectively. Figure 2 presents a summary of the Higgs boson mass measurements by the CMS Collaboration in the four-lepton decay channel.

Figure 1: The profile likelihood from the mH fit using the N –2D′BS model for each of the 4ℓ categories and combined. The change in likelihood corresponding to 68 and 95% CLs are shown by the dashed horizontal lines. Both statistical and systematic uncertainties are included in the fits.

Figure 2: Summary of the CMS Higgs boson mass measurements using the four-lepton final state. The red vertical line and the gray column represent the best fit value and the total uncertainty, respectively, as measured by combining the Runs 1 and 2 data. The yellow band and horizontal black bars show the statistical and total uncertainties in each measurement, respectively. The value of each measurement is given, along with the total and statistical only (in parentheses) uncertainties.

 Higgs boson width measurement with off-shell production

Table 3: Summary of the total Higgs boson width ΓH measurement, showing the 68% CL (central values with uncertainties) and 95% CL (in square brackets) intervals for the H → ZZ → 4ℓ channel alone and in combination with the off-shell H → ZZ → 2ℓ2ν channel.

Figure 3: Observed (solid) and expected (dashed) profile likelihood projections from the Higgs boson width fit using on- and off-shell production from this analysis. The analysis of the offshell H → ZZ → 4ℓ channel combined with the on-shell H → ZZ → 4ℓ channel is shown in black. The full combination of H → ZZ → 4ℓ with the off-shell H → ZZ → 2ℓ2ν is given in red. The black horizontal dashed lines show the 68 and 95% CL values. 

The observed limits on ΓH are stronger than the average expected values from simulation. This is supported by the upper left, where the number of observed events in the sensitive region of m4ℓ > 340 GeV and Dbkg > 0.6 in the Untagged category is below the expected value, but still consistent with it. The smaller number of events in this region favors the hypothesis of negative interference between the signal and background contributions, which dominates over the pure signal contributions for ΓH values near the SM value. Therefore, large and very small values of ΓH are disfavored. 

A measurement of the Higgs boson mass (mH) and width (ΓH) using the decays to two Z bosons is presented. The data sample comes from proton-proton collisions at the LHC recorded by the CMS experiment at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 138 fb−1 . On-shell Higgs boson production with the H → 4ℓ decay (ℓ = e, µ) is used to measure its mass and constrain its width. The mass measurement yields mH = 125.04± 25.

 Table 4: Measured values of the signal strengths µ off-shell , µ off-shell, F , and µ off-shell, V , and their 68% and 95% (in square brackets) CL intervals from the combined fit to the off-shell H → ZZ → 4ℓ and 2ℓ2ν channels.

Figure 4: Observed 2D profile likelihood projection of the off-shell signal strength parameters (µ off-shell, F , and µ off-shell, V  ) from the fit to the combined off-shell H → ZZ → 4ℓ and 2ℓ2ν channels. The best fit value is shown by the black cross and the SM prediction by the red x. The 68 and 95% CL contours are given by the dashed and solid curves, respectively. The color scale to the right of the plot relates the quantitative values. 

0.11 (stat) ± 0.05 (syst) GeV = 125.04 ± 0.12 GeV, in agreement with the expected precision of ±0.12 GeV. From on-shell production events, an upper limit of ΓH < 330 MeV is set at 95% confidence level. The mass measurement is further improved by combining data from Runs 1 and 2, leading to the most precise single measurement of the mass to date in this channel, mH = 125.08 ± 0.10 (stat) ± 0.05 (syst) GeV = 125.08 ± 0.12 GeV. Using on- and off-shell Higgs boson production with the decay to four leptons, and combining them with a separate analysis with Higgs boson decay to two leptons plus two neutrinos, we measure ΓH = 3.0+2.0, −1.5 MeV, consistent with the standard model prediction of 4.1 MeV. These results are summarized in Table 5. The strength of the off-shell Higgs boson production is also reported, and the scenario of no off-shell Higgs boson production is excluded at a confidence level corresponding to 3.8 standard deviations. Results of the measurements are tabulated in the HEPData record for this analysis.

Table 5: Summary of the Higgs boson mass and total width ΓH measurements, showing the allowed 68% CL (central values with uncertainties) and 95% CL (in square brackets) intervals. Uncertainties are reported as a combination of statistical and systematic uncertainties. The first two rows display the outcomes of the analysis conducted within the on-shell H → ZZ → 4ℓ region, where the width is restricted to be positive. The third row incorporates results from the off-shell H → ZZ → 4ℓ region combined with the on-shell H → ZZ → 4ℓ and off-shell H → ZZ → 2ℓ2ν.

                                                       ====== Physics =======

Read more:

Ultrafast Switch from a Bose-Einstein Condensate Matter - Practicalintroduction

A Theoretical Perspective : Spin−Orbit Coupling in 2D Semiconductors

Bill Gates Reveals Technology That Will Dethrone Smartphones, What The Technology Is?

Neuromorphic Spintronics - Neuromorphic Computing with Spin Torque Nano-oscillators

Cellular Lasers for Cell Imaging and Biosensing

References :

S. L. Glashow, “Partial-symmetries of weak interactions”, Nucl. Phys. 22 (1961) 579, doi:10.1016/0029-5582(61)90469-2. 

F. Englert and R. Brout, “Broken symmetry and the mass of gauge vector mesons”, Phys. Rev. Lett. 13 (1964) 321, doi:10.1103/PhysRevLett.13.321. 

CMS Collaboration, “The CMS statistical analysis and combination tool: COMBINE”, Comput. Softw. Big Sci. 8 (2024) 19, doi:10.1007/s41781-024-00121-4, arXiv:2404.06614. 

W. Verkerke and D. P. Kirkby, “The RooFit toolkit for data modeling”, in Proceedings of the 13th International Conference for Computing in High-Energy and Nuclear Physics (CHEP03). 2003. arXiv:physics/0306116. 

L. Moneta et al., “The RooStats project”, PoS ACAT2010 (2010) 057, doi:10.22323/1.093.0057, arXiv:1009.1003. 

CMS Collaboration, “A measurement of the Higgs boson mass in the diphoton decay channel”, Phys. Lett. B 805 (2020) 135425, doi:10.1016/j.physletb.2020.135425, arXiv:2002.06398. 

Particle Data Group, K. A. Olive et al., “Review of Particle Physics”, Chin. Phys. C 38 (2014) 090001, doi:10.1088/1674-1137/38/9/090001. 

CMS Collaboration, “Measurement of the properties of a Higgs boson in the four-lepton final state”, Phys. Rev. D 89 (2014) 092007, doi:10.1103/PhysRevD.89.092007, arXiv:1312.5353.

“HEPData record for this analysis”, 2024. doi:10.17182/hepdata.153670.

Read More

Neuromorphic Spintronics - Neuromorphic Computing with Spin Torque Nano-oscillators

 

Neuromorphic computing uses basic principles inspired by the brain to design circuits that perform artificial intelligence tasks with superior energy efficiency. Traditional approaches have been limited by the energy area of artificial neurons and synapses realized with conventional electronic devices.

In recent years, multiple groups have demonstrated that spintronic nanodevices, which exploit the magnetic as well as electrical properties of electrons, can increase the energy efficiency and decrease the area of these circuits. Among the variety of spintronic devices that have been used, magnetic tunnel junctions play a prominent role because of their established compatibility with standard integrated circuits and their multifunctionality.

Magnetic tunnel junctions can serve as synapses, storing connection weights, functioning as local, nonvolatile digital memory or as continuously varying resistances. As nano-oscillators, they can serve as neurons, emulating the oscillatory behavior of sets of biological neurons. As superparamagnets, they can do so by emulating the random spiking of biological neurons. Magnetic textures like domain walls or skyrmions can be configured to function as neurons through their non-linear dynamics.

Several implementations of neuromorphic computing with spintronic devices demonstrate their promise in this context. Used as variable resistance synapses, magnetic tunnel junctions perform pattern recognition in an associative memory. As oscillators, they perform spoken digit recognition in reservoir computing and when coupled together, classification of signals. As superparamagnets, they perform population coding and probabilistic computing.

Simulations demonstrate that arrays of nanomagnets and films of skyrmions can operate as components of neuromorphic computers. While these examples show the unique promise of spintronics in this field, there are several challenges to scaling up, including the efficiency of coupling between devices and the relatively low ratio of maximum to minimum resistances in the individual devices.

figure 1.

(a) Magnetic tunnel junctions for memory applications. A magnetic junction consists of two ferromagnetic layers (gray) separated by an insulating layer (blue) with the magnetization of one layer fixed and that of the other either parallel (low resistance) or antiparallel (high resistance) to it.

(b) Cross-bar array of magnetic tunnel junctions for high density storage (Magnetic Random Access Memory). The resistance of a particular tunnel junction is measured by activating the appropriate word line (red) allowing conduction between the bottom bit line and the top sense line (both blue). The alignment of the magnetization can be switched by passing sufficient currents through the device.

(c) Associative memory. (i) Handwritten digits from the MNIST dataset used for training the associative memory. (ii) Sample test input after training. (iii) Output of trained network from the test input showing successful association.

Figure 2. Neuromorphic computing with Spin Torque nano-oscillators. 

(a) Schematic spin torque nano-oscillator. When designed appropriately, the free layer magnetization of a tunnel junction precesses when a dc current is passed through it. Because of the oscillating magnetoresistance, a fixed input current gives an oscillating voltage across the junction. 

(b) Reservoir computing with a spin torque nano-oscillator. Using time multiplexing in pre- and post-processing, a single spin torque nano-oscillator gives state of the art performance as a reservoir in a reservoir computing scheme. 

(c) Schematic use of coupled nano-oscillators for vowel recognition. The input is represented by the frequencies of two microwaves applied through a stripline to the oscillators. The natural frequencies of the oscillators are tuned by dc bias currents through the devise. These can be tuned so that the synchronization pattern between the oscillators corresponds to the desired output.

Figure 3

(a) Schematic skyrmion structure. The magnetization direction of a single skyrmion is schematically given both by the directions of the arrows and the color coding, ranging from blue for magnetization up, through white for in-plane magnetization directions, to red for magnetization down. (b) Simulated skyrmion assembly. A reservoir computing scheme based on skyrmions in a random potential makes use of the distortions of the assembly due to current flow to provide the necessary non-linearity and memory.

Read more:

Ultrafast Switch from a Bose-Einstein Condensate Matter - Practicalintroduction

A Theoretical Perspective : Spin−Orbit Coupling in 2D Semiconductors

Bill Gates Reveals Technology That Will Dethrone Smartphones, What The Technology Is?

Measurement of the Higgs boson mass and width using the four-lepton final state in proton-proton collisions at √s = 13 TeV

Cellular Lasers for Cell Imaging and Biosensing

References :

1. Big data needs a hardware revolution. Nature (2018). doi:10.1038/d41586-018-01683-1 

2. Furber S Large-scale neuromorphic computing systems. J. Neural Eng 13, 051001 (2016). [PubMed: 27529195] 

3. Indiveri G et al. Neuromorphic silicon neuron circuits. Neuromorphic Eng 5, 73 (2011). 

4. Locatelli N, Cros V & Grollier J Spin-torque building blocks. Nat. Mater 13, 11–20 (2014). [PubMed: 24343514] 

5. Grollier J, Querlioz D & Stiles MD Spintronic Nanodevices for Bioinspired Computing. Proc. IEEE 104, 2024–2039 (2016).

Read More

Bio-Supercapacitor for Newest Biomedical Applications in 20th Century By Management Practicalintroduction

Research team from Chemnitz University of Technology, IFW Dresden, and IPF Dresden presents biocompatible energy storage device for sensor applications in blood vessels.

The miniaturization of microelectronic sensor technology, microelectronic robots or intravascular implants is progressing rapidly. However, it also poses major challenges for research. One of the biggest is the development of tiny but efficient energy storage devices that enable the operation of autonomously working microsystems – in more and more smaller areas of the human body for example. In addition, these energy storage devices must be bio-compatible if they are to be used in the body at all. Now there is a prototype that combines these essential properties. The breakthrough was achieved by an international research team led by Prof. Dr. Oliver G. Schmidt, Professorship of Materials Systems for Nanoelectronics at Chemnitz University of Technology, initiator of the Center for Materials, Architectures and Integration of Nanomembranes (MAIN) at Chemnitz University of Technology and director at the Leibniz Institute for Solid State and Materials Research (IFW) Dresden. The Leibniz Institute of Polymer Research Dresden (IPF) was also involved in the study as a cooperation partner.

In the current issue of Nature Communications, the researchers report on the smallest microsupercapacitors to date, which already functions in (artificial) blood vessels and can be used as an energy source for a tiny sensor system to measure pH. This storage system opens up possibilities for intravascular implants and microrobotic systems for next-generation biomedicine that could operate in hard-to-reach small spaces deep inside the human body. For example, real-time detection of blood pH can help predict early tumor growing. “It is extremely encouraging to see how new, extremely flexible, and adaptive microelectronics is making it into the miniaturized world of biological systems”, says research group leader Prof. Dr. Oliver G. Schmidt, who is extremely pleased with this research success.

The fabrication of the samples and the investigation of the biosupercapacitor were largely carried out at the Research Center MAIN at Chemnitz University of Technology.

“The architecture of our nano-bio supercapacitors offers the first potential solution to one of the biggest challenges – tiny integrated energy storage devices that enable the self-sufficient operation of multifunctional microsystems,” says Dr. Vineeth Kumar, researcher in Prof. Schmidt’s team and a research associate at the MAIN research center.

Smaller than a speck of dust – voltage comparable to a AAA battery.

Ever smaller energy storage devices in the submillimeter range – so-called “nano-supercapacitors” (nBSC) – for even smaller microelectronic components are not only a major technical challenge, however. This is because, as a rule, these supercapacitors do not use biocompatible materials but, for example, corrosive electrolytes and quickly discharge themselves in the event of defects and contamination. Both aspects make them unsuitable for biomedical applications in the body. So-called “biosupercapacitors (BSCs)” offer a solution. They have two outstanding properties: they are fully biocompatible, which means that they can be used in body fluids such as blood and can be used for further medical studies. In addition, biosupercapacitors can compensate for self-discharge behavior through bio-electrochemical reactions. 

In doing so, they even benefit from the body’s own reactions. This is because, in addition to typical charge storage reactions of a supercapacitor, redox enzymatic reactions and living cells naturally present in the blood increase the performance of the device by 40%.

Currently, the smallest such energy storage devices are larger than 3 mm3. Prof. Oliver Schmidt’s team has now succeeded in producing a 3,000 times smaller tubular nBSC, which, with a volume of 0.001 mm3 (1 nanolitre), occupies less space than a grain of dust and yet delivers up to 1.6 V supply voltage for microelectronic sensors. This energy can be used for a sensor system in the blood, for example. The power level also is roughly equivalent to the voltage of a standard AAA battery, although the actual current flow on these smallest scales is of course significantly lower. 

The flexible tubular geometry of the nano-biosupercapacitor provides efficient self-protection against deformations caused by pulsating blood or muscle contraction. At full capacity, the presented nano-biosupercapacitor can operate a complex fully integrated sensor system for measuring the pH value in blood.

Thanks to Origami structure technology: flexible, robust, tiny.

Origami structure technology involves placing the materials required for the nBSC components on a wafer-thin surface under high mechanical tension. When the material layers are subsequently detached from the surface in a controlled manner, the strain energy is released and the layers wind themselves into compact 3D devices with high accuracy and yield (95%). The nano-biosupercapacitors produced in this way were tested in three solutions called electrolytes: Saline, blood plasma, and blood. In all three electrolytes, energy storage was sufficiently successful, albeit with varying efficiency. In blood, the nano-biosupercapacitor showed excellent lifetime, holding up to 70% of its initial capacity even after 16 hours. A proton exchange separator (PES) was used to suppress the rapid self-discharge.

Performance stability even under realistic conditions

In order to maintain natural body functions in different situations, the flow characteristics of the blood and the pressure in the vessels are under constant change. Blood flow pulsates and varies according to vessel diameter and blood pressure. 

Any implantable system within the circulatory system must withstand these physiological conditions while maintaining stable performance. The team therefore studied the performance of their development – similar to a wind tunnel – in so-called microfluidic channels with diameters of 120 to 150 µm (0.12 to 0.15 mm) to mimic blood vessels of different sizes. 

In these channels, the researchers simulated and tested the behavior of their energy storage devices under different flow and pressure conditions. They found that the nano-biosupercapacitors can provide their power well and stably under physiologically relevant conditions. Self-contained sensor technology can support diagnostics – such as tumor diagnostics. The hydrogen potential (pH) of blood is subject to fluctuations. Continuous measurement of the pH can thus help in the early detection of tumors, for example. 

For this purpose, the researchers developed a pH sensor that is supplied with energy by the nano-biosupercapacitor. The 5 µm thin film transistor (TFT) technology previously established in Prof. Oliver Schmidt’s research team could be used to develop a ring oscillator with exceptional mechanical flexibility, operating at low power (nW to µW) and high frequencies (up to 100MHz). For the current project, the team used a nBSC based ring oscillator. The team integrated a pH-sensitive BSC into the ring oscillator so that there is a change in output frequency depending on the pH of the electrolyte. This pH-sensitive ring oscillator was also formed into a tubular 3D geometry using the “Swiss-roll” Origami technique, creating a fully integrated and ultra-compact system of energy storage and sensor. The hollow inner core of this micro sensor system serves as a channel for the blood plasma. 

In addition, three nBSCs connected in series with the sensor enable particularly efficient and self-sufficient pH measurement. These properties open up a wide range of possible applications, for example in diagnostics and medication.

Read More