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Black, K. M. et al, & Zurita, J. (2024). Muon Collider Forum report. J. Instrum., 19(2), T02015–95pp.
Abstract: A multi-TeV muon collider offers a spectacular opportunity in the direct exploration of the energy frontier. Offering a combination of unprecedented energy collisions in a comparatively clean leptonic environment, a high energy muon collider has the unique potential to provide both precision measurements and the highest energy reach in one machine that cannot be paralleled by any currently available technology. The topic generated a lot of excitement in Snowmass meetings and continues to attract a large number of supporters, including many from the early career community. In light of this very strong interest within the US particle physics community, Snowmass Energy, Theory and Accelerator Frontiers created a cross-frontier Muon Collider Forum in November of 2020. The Forum has been meeting on a monthly basis and organized several topical workshops dedicated to physics, accelerator technology, and detector R&D. Findings of the Forum are summarized in this report.
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LHCb Collaboration(Aaij, R. et al), Jaimes Elles, S. J., Jashal, B. K., Martinez-Vidal, F., Oyanguren, A., Rebollo De Miguel, M., et al. (2024). Helium identification with LHCb. J. Instrum., 19(2), P02010–23pp.
Abstract: The identification of helium nuclei at LHCb is achieved using a method based on measurements of ionisation losses in the silicon sensors and timing measurements in the Outer Tracker drift tubes. The background from photon conversions is reduced using the RICH detectors and an isolation requirement. The method is developed using pp collision data at root s = 13 TeV recorded by the LHCb experiment in the years 2016 to 2018, corresponding to an integrated luminosity of 5.5 fb(-1). A total of around 10(5) helium and antihelium candidates are identified with negligible background contamination. The helium identification efficiency is estimated to be approximately 50% with a corresponding background rejection rate of up to O(10(12)). These results demonstrate the feasibility of a rich programme of measurements of QCD and astrophysics interest involving light nuclei.
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Gammaldi, V., Zaldivar, B., Sanchez-Conde, M. A., & Coronado-Blazquez, J. (2023). A search for dark matter among Fermi-LAT unidentified sources with systematic features in machine learning. Mon. Not. Roy. Astron. Soc., 520(1), 1348–1361.
Abstract: Around one-third of the point-like sources in the Fermi-LAT catalogues remain as unidentified sources (unIDs) today. Indeed, these unIDs lack a clear, univocal association with a known astrophysical source. If dark matter (DM) is composed of weakly interacting massive particles (WIMPs), there is the exciting possibility that some of these unIDs may actually be DM sources, emitting gamma-rays from WIMPs annihilation. We propose a new approach to solve the standard, machine learning (ML) binary classification problem of disentangling prospective DM sources (simulated data) from astrophysical sources (observed data) among the unIDs of the 4FGL Fermi-LAT catalogue. We artificially build two systematic features for the DM data which are originally inherent to observed data: the detection significance and the uncertainty on the spectral curvature. We do it by sampling from the observed population of unIDs, assuming that the DM distributions would, if any, follow the latter. We consider different ML models: Logistic Regression, Neural Network (NN), Naive Bayes, and Gaussian Process, out of which the best, in terms of classification accuracy, is the NN, achieving around 93 . 3 per cent +/- 0 . 7 per cent performance. Other ML evaluation parameters, such as the True Ne gativ e and True Positive rates, are discussed in our work. Applying the NN to the unIDs sample, we find that the de generac y between some astrophysical and DM sources can be partially solved within this methodology. None the less, we conclude that there are no DM source candidates among the pool of 4FGL Fermi-LAT unIDs.
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