We perform a new dark matter hot spot analysis using ten years of public IceCube data. In this analysis we assume dark matter self-annihilates to neutrino pairs and treat the production sites as discrete point sources. For neutrino telescopes these s
ites will appear as hot spots in the sky, possibly outshining other standard model neutrino sources. Comparing to galactic center analyses, we show that this approach is a powerful tool and capable of setting the highest neutrino detector limits for dark matter masses between 10 TeV and 100 PeV. This is due to the inclusion of spatial information in addition to the typically used energy deposition in the analysis.
We develop a modeling framework for bioluminescence light found in the deep sea near neutrino telescopes by combining a hydrodynamic model with a stochastic one. The bioluminescence is caused by organisms when exposed to a non-constant water flow, su
ch as past the neutrino telescopes. We model the flow using the incompressible Navier-Stokes equations for Reynolds numbers between 4000 and 23000. The discretization relies on a finite element method which includes upwind-stabilization for the velocity field. On top of the flow model, we simulate a population of random microscopic organisms. Their movement and emission are stochastic processes which we model using Monte Carlo methods. We observe unique time-series for the photon counts depending on the flow velocity and detector specifications. This opens up the possibility of categorizing organisms using neutrino detectors. We show that the average light-yield and pulse shapes require precise flow modeling, while the emission timing is chaotic. From this we construct a fast modeling scheme, requiring only a subset of computationally expensive flow and population modeling.
We demonstrate that megaton-mass neutrino telescopes are able to observe the signal from long-lived particles beyond the Standard Model, in particular the stau, the supersymmetric partner of the tau lepton. Its signature is an excess of charged parti
cle tracks with horizontal arrival directions and energy deposits between 0.1 and 1 TeV inside the detector. We exploit this previously-overlooked signature to search for stau particles in the publicly available IceCube data. The data shows no evidence of physics beyond the Standard Model. We derive a new lower limit on the stau mass of $320$ GeV (95% C.L.) and estimate that this new approach, when applied to the full data set available to the IceCube collaboration, will reach world-leading sensitivity to the stau mass ($m_{tilde{tau}}=450,mathrm{GeV}$).
Atmospheric muons are one of the main backgrounds for current Water- and Ice-Cherenkov neutrino telescopes designed to detect astrophysical neutrinos. The inclusive fluxes of atmospheric muons and neutrinos from hadronic interactions of cosmic rays h
ave been extensively studied with Monte Carlo and cascade equation methods, for example, CORSIKA and MCEq. However, the muons that are pair produced in electromagnetic interaction of high energy photons are quantitatively not well understood. We present new simulation results and assess the model dependencies of the high-energy atmospheric muon flux including those from electromagnetic interactions, using a new numerical electromagnetic cascade equation solver EmCa that can be easily coupled with the hadronic solver MCEq. Both codes are in active development with the particular aim to become part of the next generation CORSIKA 8 air shower simulation package. The combination of EmCa and MCEq accounts for material effects that have not been previously included in most of the available codes. Hence, the influence of these effects on the air showers will also be briefly discussed.
Electromagnetic-Cascades (EmCa) is a Python package for the simulation of electromagnetic cascades in various materials. The showers are modeled using cascade equations and the relevant interactions, specifically pair production, Bremsstrahlung, Comp
ton scattering and ionization. This methodology has the advantage of being computationally inexpensive and fast, unlike Monte Carlo methods. The code includes low and high energy material effects, allowing for a high range of validity of the simulation results. EmCa is easily extendable and offers a framework for testing different electromagnetic interaction models. In combination with MCEq, a Python package for hadronic particle showers using cascade equations, full simulations of atmospheric fluxes can be done.
