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T2K Collaboration(Abe, K. et al), Antonova, M., Cervera-Villanueva, A., Fernandez, P., Izmaylov, A., & Novella, P. (2020). Measurement of the charged-current electron (anti-)neutrino inclusive cross-sections at the T2K off-axis near detector ND280. J. High Energy Phys., 10(10), 114–43pp.
Abstract: The electron (anti-)neutrino component of the T2K neutrino beam constitutes the largest background in the measurement of electron (anti-)neutrino appearance at the far detector. The electron neutrino scattering is measured directly with the T2K off-axis near detector, ND280. The selection of the electron (anti-)neutrino events in the plastic scintillator target from both neutrino and anti-neutrino mode beams is discussed in this paper. The flux integrated single differential charged-current inclusive electron (anti-)neutrino cross-sections, d sigma/dp and d sigma/d cos(theta), and the total cross-sections in a limited phase-space in momentum and scattering angle (p 300 MeV/c and theta <= 45 degrees) are measured using a binned maximum likelihood fit and compared to the neutrino Monte Carlo generator predictions, resulting in good agreement.
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T2K Collaboration(Abe, K. et al), Antonova, M., & Cervera-Villanueva, A. (2021). T2K measurements of muon neutrino and antineutrino disappearance using 3.13 x 10(21) protons on target. Phys. Rev. D, 103(1), L011101–9pp.
Abstract: We report measurements by the T2K experiment of the parameters theta(23) and Delta m(32)(2), which govern the disappearance of muon neutrinos and antineutrinos in the three-flavor PMNS neutrino oscillation model at T2K's neutrino energy and propagation distance. Utilizing the ability of the experiment to run with either a mainly neutrino or a mainly antineutrino beam, muon-like events from each beam mode are used to measure these parameters separately for neutrino and antineutrino oscillations. Data taken from 1.49 x 10(21) protons on target (POT) in neutrino mode and 1.64 x 10(21) POT in antineutrino mode are used. The best-fit values obtained by T2K were sin(2)(theta(23)) = 0.51(-0.07)(+0.06) (0.43(-0.05)(+0.21)) and Delta m(32)(2) = 2.47(-0.09)(+0.08) (2.50(-0.13)(+0.18)) x 10(-3) eV(2)/c(4) for neutrinos (antineutrinos). No significant differences between the values of the parameters describing the disappearance of muon neutrinos and antineutrinos were observed. An analysis using an effective two-flavor neutrino oscillation model where the sine of the mixing angle is allowed to take nonphysical values larger than 1 is also performed to check the consistency of our data with the three-flavor model. Our data were found to be consistent with a physical value for the mixing angle.
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Super-Kamiokande Collaboration(Abe, K. et al), & Molina Sedgwick, S. (2022). Neutron tagging following atmospheric neutrino events in a water Cherenkov detector. J. Instrum., 17(10), P10029–41pp.
Abstract: We present the development of neutron-tagging techniques in Super-Kamiokande IV using a neural network analysis. The detection efficiency of neutron capture on hydrogen is estimated to be 26%, with a mis-tag rate of 0.016 per neutrino event. The uncertainty of the tagging efficiency is estimated to be 9.0%. Measurement of the tagging efficiency with data from an Americium-Beryllium calibration agrees with this value within 10%. The tagging procedure was performed on 3,244.4 days of SK-IV atmospheric neutrino data, identifying 18,091 neutrons in 26,473 neutrino events. The fitted neutron capture lifetime was measured as 218 +/- 9 μs.
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Stoppa, F., Vreeswijk, P., Bloemen, S., Bhattacharyya, S., Caron, S., Johannesson, G., et al. (2022). AutoSourceID-Light Fast optical source localization via U-Net and Laplacian of Gaussian. Astron. Astrophys., 662, A109–8pp.
Abstract: Aims. With the ever-increasing survey speed of optical wide-field telescopes and the importance of discovering transients when they are still young, rapid and reliable source localization is paramount. We present AutoSourceID-Light (ASID-L), an innovative framework that uses computer vision techniques that can naturally deal with large amounts of data and rapidly localize sources in optical images. Methods. We show that the ASID-L algorithm based on U-shaped networks and enhanced with a Laplacian of Gaussian filter provides outstanding performance in the localization of sources. A U-Net network discerns the sources in the images from many different artifacts and passes the result to a Laplacian of Gaussian filter that then estimates the exact location. Results. Using ASID-L on the optical images of the MeerLICHT telescope demonstrates the great speed and localization power of the method. We compare the results with SExtractor and show that our method outperforms this more widely used method. ASID-L rapidly detects more sources not only in low- and mid-density fields, but particularly in areas with more than 150 sources per square arcminute. The training set and code used in this paper are publicly available.
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Stoppa, F., Ruiz de Austri, R., Vreeswijk, P., Bhattacharyya, S., Caron, S., Bloemen, S., et al. (2023). AutoSourceID-FeatureExtractor Optical image analysis using a two-step mean variance estimation network for feature estimation and uncertainty characterisation. Astron. Astrophys., 680, A108–14pp.
Abstract: Aims. In astronomy, machine learning has been successful in various tasks such as source localisation, classification, anomaly detection, and segmentation. However, feature regression remains an area with room for improvement. We aim to design a network that can accurately estimate sources' features and their uncertainties from single-band image cutouts, given the approximated locations of the sources provided by the previously developed code AutoSourceID-Light (ASID-L) or other external catalogues. This work serves as a proof of concept, showing the potential of machine learning in estimating astronomical features when trained on meticulously crafted synthetic images and subsequently applied to real astronomical data.Methods. The algorithm presented here, AutoSourceID-FeatureExtractor (ASID-FE), uses single-band cutouts of 32x32 pixels around the localised sources to estimate flux, sub-pixel centre coordinates, and their uncertainties. ASID-FE employs a two-step mean variance estimation (TS-MVE) approach to first estimate the features and then their uncertainties without the need for additional information, for example the point spread function (PSF). For this proof of concept, we generated a synthetic dataset comprising only point sources directly derived from real images, ensuring a controlled yet authentic testing environment.Results. We show that ASID-FE, trained on synthetic images derived from the MeerLICHT telescope, can predict more accurate features with respect to similar codes such as SourceExtractor and that the two-step method can estimate well-calibrated uncertainties that are better behaved compared to similar methods that use deep ensembles of simple MVE networks. Finally, we evaluate the model on real images from the MeerLICHT telescope and the Zwicky Transient Facility (ZTF) to test its transfer learning abilities.
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