Collider constraints on massive gravitons coupling to photons

We study the discovery potential of massive graviton-like spin-2 particles coupled to standard model fields, produced in photon-photon collisions at the Large Hadron Collider (LHC) as well as in electron-positron ($e^+e^-$) collisions, within an effective theory with and without universal couplings. Our focus is on a massive graviton G coupled to the electromagnetic field, which decays via $\mathrm{G}\to \gamma \gamma$ and leads to a resonant excess of diphotons over the light-by-light scattering continuum at the LHC, and of triphoton final states at $e^+e^-$ colliders. Based on similar searches performed for pseudoscalar axion-like particles (ALPs), and taking into account the different cross sections, $\gamma \gamma$ partial widths, and decay kinematics of the pseudoscalar and tensor particles, we reinterpret existing experimental bounds on the ALP-$\gamma$ coupling into G-$\gamma$ ones. Using the available data, exclusion limits on the graviton-photon coupling are set down to $g_{\mathrm{G}\gamma\gamma}\approx 1$--0.05~TeV$^{-1}$ for masses $m_\mathrm{G} \approx 100$~MeV--2~TeV. Such bounds can be improved by factors of 100 at Belle~II in the low-mass region, and of 4 at the HL-LHC at high masses, with their expected full integrated luminosities.Comment: 26 pages, 7 figure

Exploiting synergies between neutrino telescopes for the next galactic core-collapse supernova

Observing and characterizing the next galactic core-collapse supernova will be a critical step for neutrino experiments. Extracting information about the supernova progenitors and neutrino properties within minutes after an observation will in particular be crucial in order to optimize analysis strategies at other observatories. Moreover, certain classes of progenitors, with strong magnetic fields, could give rise to gamma-ray bursts but have been underinvestigated to date. In this contribution we propose a strategy to combine results from next-generation neutrino experiments, focusing notably on the determination of the progenitor mass and the neutrino mass ordering. Additionally, we investigate the impact of strong magnetic fields on neutrino observations, and demonstrate the detectability of the associated effects in upcoming experiments

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements