Abstract The production of a pair of τ leptons via photon–photon fusion, , is observed for the first time in proton–proton collisions, with a significance of 5.3 standard deviations. This observation is based on a data set recorded with the CMS detector at the LHC at a center-of-mass energy of 13 TeV and corresponding to an integrated luminosity of 138 fb−1. Events with a pair of τ leptons produced via photon–photon fusion are selected by requiring them to be back-to-back in the azimuthal direction and to have a minimum number of charged hadrons associated with their production vertex. The τ leptons are reconstructed in their leptonic and hadronic decay modes. The measured fiducial cross section of is . Constraints are set on the contributions to the anomalous magnetic moment ( ) and electric dipole moments ( ) of the τ lepton originating from potential effects of new physics on the vertex: and (95% confidence level), consistent with the standard model. 
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                            Smart pixel sensors: towards on-sensor filtering of pixel clusters with deep learning
                        
                    
    
            Abstract 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 (40 MHz) and intelligently reduces the data within the pixelated region of the detectorat ratewill 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 57.1%–75.7%. 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  with an area of less than 0.2 mm2. 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. 
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                            - Award ID(s):
- 2310072
- PAR ID:
- 10533248
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Machine Learning: Science and Technology
- Volume:
- 5
- Issue:
- 3
- ISSN:
- 2632-2153
- Format(s):
- Medium: X Size: Article No. 035047
- Size(s):
- Article No. 035047
- Sponsoring Org:
- National Science Foundation
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