Estimation of a coronal mass ejection magnetic field strength using radio observations of gyrosynchrotron radiation

Coronal mass ejections (CMEs) are large eruptions of plasma and magnetic field from the low solar corona into interplanetary space. These eruptions are often associated with the acceleration of energetic electrons which produce various sources of high intensity plasma emission. In relatively rare cases, the energetic electrons may also produce gyrosynchrotron emission from within the CME itself, allowing for a diagnostic of the CME magnetic field strength. Such a magnetic field diagnostic is important for evaluating the total magnetic energy content of the CME, which is ultimately what drives the eruption. Here, we report on an unusually large source of gyrosynchrotron radiation in the form of a type IV radio burst associated with a CME occurring on 2014-September-01, observed using instrumentation from the Nançay Radio Astronomy Facility. A combination of spectral flux density measurements from the Nançay instruments and the Radio Solar Telescope Network (RSTN) from 300 MHz to 5 GHz reveals a gyrosynchrotron spectrum with a peak flux density at ∼1 GHz. Using this radio analysis, a model for gyrosynchrotron radiation, a non-thermal electron density diagnostic using the Fermi Gamma Ray Burst Monitor (GBM) and images of the eruption from the GOES Soft X-ray Imager (SXI), we were able to calculate both the magnetic field strength and the properties of the X-ray and radio emitting energetic electrons within the CME. We find the radio emission is produced by non-thermal electrons of energies >1 MeV with a spectral index of δ ∼ 3 in a CME magnetic field of 4.4 G at a height of 1.3 R�, while the X-ray emission is produced from a similar distribution of electrons but with much lower energies on the order of 10 keV. We conclude by comparing the electron distribution characteristics derived from both X-ray and radio and show how such an analysis can be used to define the plasma and bulk properties of a CME

Direct comparison of sterile neutrino constraints from cosmological data, νe\nu_{e} disappearance data and νμ→νe\nu_{\mu}\rightarrow\nu_{e} appearance data in a 3+13+1 model

We present a quantitative, direct comparison of constraints on sterile neutrinos derived from neutrino oscillation experiments and from Planck data, interpreted assuming standard cosmological evolution. We extend a $1+1$ model, which is used to compare exclusions contours at the 95% CL derived from Planck data to those from $\nu_{e}$-disappearance measurements, to a $3+1$ model. This allows us to compare the Planck constraints with those obtained through $\nu_{\mu}\rightarrow\nu_{e}$ appearance searches, which are sensitive to more than one active-sterile mixing angle. We find that the cosmological data fully exclude the allowed regions published by the LSND, MiniBooNE and Neutrino-4 collaborations, and those from the gallium and rector anomalies, at the 95% CL. Compared to the exclusion regions from the Daya Bay $\nu_{e}$-disappearance search, the Planck data are more strongly excluding above $|\Delta m^{2}_{41}|\approx 0.1\, \mathrm{eV}^{2}$ and $m_\mathrm{eff}^\mathrm{sterile}\approx 0.2\, \mathrm{eV}$, with the Daya Bay exclusion being stronger below these values. Compared to the combined Daya Bay/Bugey/MINOS exclusion region on $\nu_{\mu}\rightarrow\nu_{e}$ appearance, the Planck data is more strongly excluding above $\Delta m^{2}_{41}\approx 5\times 10^{-2}\,\mathrm{eV}^{2}$, with the exclusion strengths of the Planck data and the Daya Bay/Bugey/MINOS combination becoming comparable below this value.Comment: 9 pages, 4 figures, accepted by Eur. Phys. J.

Direct comparison of sterile neutrino constraints from cosmological data, ν e disappearance data and ν μ → ν e appearance data in a 3 + 1 model

From Springer Nature via Jisc Publications RouterHistory: received 2020-02-22, registration 2020-07-03, accepted 2020-07-03, pub-print 2020-08, pub-electronic 2020-08-19, online 2020-08-19Publication status: PublishedFunder: H2020 Marie Sklodowska-Curie Actions; doi: http://dx.doi.org/10.13039/100010665; Grant(s): 752309Abstract: We present a quantitative, direct comparison of constraints on sterile neutrinos derived from neutrino oscillation experiments and from Planck data, interpreted assuming standard cosmological evolution. We extend a 1+1 model, which is used to compare exclusion contours at the 95% Cl derived from Planck data to those from νe-disappearance measurements, to a 3+1 model. This allows us to compare the Planck constraints with those obtained through νμ→νe appearance searches, which are sensitive to more than one active-sterile mixing angle. We find that the cosmological data fully exclude the allowed regions published by the LSND, MiniBooNE and Neutrino-4 collaborations, and those from the gallium and rector anomalies, at the 95% Cl. Compared to the exclusion region from the Daya Bay νe-disappearance search, the Planck data are more strongly excluding above |Δm412|≈0.1eV2 and meffsterile≈0.2eV, with the Daya Bay exclusion being stronger below these values. Compared to the combined Daya Bay/Bugey/MINOS exclusion region on νμ→νe appearance, the Planck data is more strongly excluding above Δm412≈5×10-2eV2, with the exclusion strengths of the Planck data and the Daya Bay/Bugey/MINOS combination becoming comparable below this value

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