A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons ($\tau_\mathrm{h}$) that originate from genuine tau leptons in the CMS detector against $\tau_\mathrm{h}$ candidates that originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from all reconstructed particles in the vicinity of a $\tau_\mathrm{h}$ candidate and employs a deep neural network with convolutional layers to efficiently process the inputs. This algorithm leads to a significantly improved performance compared with the previously used one. For example, the efficiency for a genuine $\tau_\mathrm{h}$ to pass the discriminator against jets increases by 10-30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient $\tau_\mathrm{h}$ reconstruction is introduced that incorporates additional hadronic decay modes. The superior performance of the new algorithm to discriminate against jets, electrons, and muons and the improved $\tau_\mathrm{h}$ reconstruction method are validated with LHC proton-proton collision data at $\sqrt{s} =$ 13 TeV.
Decay mode confusion matrix. For a given generated decay mode, the fractions of reconstructed tau_h in different decay modes are given, as well as the fraction of generated tau_h that are not reconstructed. Both the generated and reconstructed tau_h need to fulfil pt > 20 GeV and |eta| < 2.3. The tau_h candidates come from a Z to tau tau event sample with m(tau, tau) > 50 GeV.
Efficiency for quark and gluon jets to pass different tau identification discriminators versus the efficiency for genuine tau_h. The upper two plots are obtained with jets from the W+jets simulated sample and the lower two plots with jets from the tt sample. The left two plots include jets and genuine tau_h with pt < 100 GeV, whereas the right two plots include those with pt > 100 GeV. The working points are indicated as full circles. The efficiency for jets from the W+jets event sample, enriched in quark jets, to pass the discriminators is higher compared to jets from the tt event sample, which has a larger fraction of gluon and b-quark jets. The jet efficiency for a given tau_h efficiency is larger for jets and tau_h with pt < 100 GeV than for those with pt > 100 GeV. Compared with the previously used MVA discriminator, the DEEPTAU discriminator reduces the jet efficiency for a given tau_h efficiency by consistently more than a factor of 1.8, and by more at high tau_h efficiency. The additional gain at high pt comes from the inclusion of updated decay modes in the tau_h reconstruction, as illustrated by the curves for the previously used MVA discriminator but including reconstructed tau_h candidates with additional decay modes.
Efficiency for quark and gluon jets to pass different tau identification discriminators versus the efficiency for genuine tau_h. The upper two plots are obtained with jets from the W+jets simulated sample and the lower two plots with jets from the tt sample. The left two plots include jets and genuine tau_h with pt < 100 GeV, whereas the right two plots include those with pt > 100 GeV. The working points are indicated as full circles. The efficiency for jets from the W+jets event sample, enriched in quark jets, to pass the discriminators is higher compared to jets from the tt event sample, which has a larger fraction of gluon and b-quark jets. The jet efficiency for a given tau_h efficiency is larger for jets and tau_h with pt < 100 GeV than for those with pt > 100 GeV. Compared with the previously used MVA discriminator, the DEEPTAU discriminator reduces the jet efficiency for a given tau_h efficiency by consistently more than a factor of 1.8, and by more at high tau_h efficiency. The additional gain at high pt comes from the inclusion of updated decay modes in the tau_h reconstruction, as illustrated by the curves for the previously used MVA discriminator but including reconstructed tau_h candidates with additional decay modes.
The STEREO experiment is a very short baseline reactor antineutrino experiment. It is designed to test the hypothesis of light sterile neutrinos being the cause of a deficit of the observed antineutrino interaction rate at short baselines with respect to the predicted rate, known as the reactor antineutrino anomaly. The STEREO experiment measures the antineutrino energy spectrum in six identical detector cells covering baselines between 9 and 11 m from the compact core of the ILL research reactor. In this article, results from 179 days of reactor turned on and 235 days of reactor turned off are reported at a high degree of detail. The current results include improvements in the modelling of detector optical properties and the gamma-cascade after neutron captures by gadolinium, the treatment of backgrounds, and the statistical method of the oscillation analysis. Using a direct comparison between antineutrino spectra of all cells, largely independent of any flux prediction, we find the data compatible with the null oscillation hypothesis. The best-fit point of the reactor antineutrino anomaly is rejected at more than 99.9% C.L.
Data from Figure 30 – Relative comparison between the estimated rates of IBD events $A_{l,i}$ (for cell $l$ and energy bin $i$) and the fitted no-oscillation model $M_{l,i}(0, 0, \vec{\alpha})~\phi_i$ as a function of reconstructed energy $E_\text{rec}$ after a fit to phase-I+II data. Due to less statistics, the highest energy bin is excluded from the oscillation analysis in phase-I. For technical reasons, its value is set equal to zero in this dataset. A graphical presentation can be downloaded at "Resources" for reference.
Data from Figure 30 – Relative comparison between the estimated rates of IBD events $A_{l,i}$ (for cell $l$ and energy bin $i$) and the fitted no-oscillation model $M_{l,i}(0, 0, \vec{\alpha})~\phi_i$ as a function of reconstructed energy $E_\text{rec}$ after a fit to phase-I+II data. Due to less statistics, the highest energy bin is excluded from the oscillation analysis in phase-I. For technical reasons, its value is set equal to zero in this dataset. A graphical presentation can be downloaded at "Resources" for reference.
Data from Figure 30 – Relative comparison between the estimated rates of IBD events $A_{l,i}$ (for cell $l$ and energy bin $i$) and the re-normalised no-oscillation model $\phi_i M_{l,i}(\sin^2(2\theta_{ee}) = 0)$ as a function of reconstructed energy $E_\text{rec}$ after a fit to phase-I+II data. Due to less statistics, the highest energy bin is excluded from the oscillation analysis in phase-I. For technical reasons, its value is set equal to zero in this dataset. A full graphical presentation can be downloaded at "Resources" for reference.
A powerful and robust control system is a crucial, often neglected, pillar of any modern, complex physics experiment that requires the management of a multitude of different devices and their precise time synchronisation. The AEgIS collaboration presents CIRCUS, a novel, autonomous control system optimised for time-critical experiments such as those at CERN's Antiproton Decelerator and, more broadly, in atomic and quantum physics research. Its setup is based on Sinara/ARTIQ and TALOS, integrating the ALPACA analysis pipeline, the last two developed entirely in AEgIS. It is suitable for strict synchronicity requirements and repeatable, automated operation of experiments, culminating in autonomous parameter optimisation via feedback from real-time data analysis. CIRCUS has been successfully deployed and tested in AEgIS; being experiment-agnostic and released open-source, other experiments can leverage its capabilities.
Synchronous voltage ramp-up to 20 V on three high-voltage amplifier channels 10 μs subsequent to the arrival of a common trigger pulse at zero time in the figure. The inset shows a zoom to the shoulder region for a better visualisation of the synchronicity.
A feedback loop uses the uncorrected laser pulse timings (red squares) to calculate the deviation from the user setting (solid black line) over the course of an hour, and corrects the timing of the subsequent desired laser pulse that is used for the actual experiment (blue circles). Independent of short-term to long-term drifts or even sudden jumps, the resulting timing is always close to the desired value.
A feedback loop uses the uncorrected laser pulse timings (red squares) to calculate the deviation from the user setting (solid black line) over the course of an hour, and corrects the timing of the subsequent desired laser pulse that is used for the actual experiment (blue circles). Independent of short-term to long-term drifts or even sudden jumps, the resulting timing is always close to the desired value.
Modern physics experiments are frequently very complex, relying on multiple simultaneous events to happen in order to obtain the desired result. The experiment control system plays a central role in orchestrating the measurement setup: However, its development is often treated as secondary with respect to the hardware, its importance becoming evident only during the operational phase. Therefore, the AEgIS (Antimatter Experiment: Gravity, Interferometry, Spectroscopy) collaboration has created a framework for easily coding control systems, specifically targeting atomic, quantum, and antimatter experiments. This framework, called Total Automation of LabVIEW Operations for Science (TALOS), unifies all the machines of the experiment in a single entity, thus enabling complex high-level decisions to be taken, and it is constituted by separate modules, called MicroServices, that run concurrently and asynchronously. This enhances the stability and reproducibility of the system while allowing for continuous integration and testing while the control system is running. The system demonstrated high stability and reproducibility, running completely unsupervised during the night and weekends of the data-taking campaigns. The results demonstrate the suitability of TALOS to manage an entire physics experiment in full autonomy: being open-source, experiments other than the AEgIS experiment can benefit from it.
Graph showing the number of antiprotons captured vs the closure timing of the trap. It clearly shows the presence of a best working point. Closing too fast lets some antiprotons out, and, conversely, closing too slow lets some antiprotons escape after the bounce on the second electrode.
Graph showing the number of antiprotons captured varying the potential of the catching electrodes. This scan characterizes the energy profile of the p's passing through the degrader, and their ratio is in good accordance with our GEANT4 simulations.
Two graphs show the results of the scan over the horizontal and vertical displacements of the antiproton beam (on the left) and the horizontal and vertical angles (see Table 4, after). The color represents the intensity of the signal obtained on the MCP from the annihilations of the trapped antiprotons. The parameter space has been organized in this way, assuming that displacements and angles have independent effects, not for physics reasons, but because scanning over the full parameter space would have been impossible time-wise (10 steps per dimension 4 dimensions x 5 min of duration of the script ~35 days!).
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