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T2K Collaboration (Abe, K. et al); Antonova, M.; Cervera-Villanueva, A. |
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Title |
T2K measurements of muon neutrino and antineutrino disappearance using 3.13 x 10(21) protons on target |
Type |
Journal Article |
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Year |
2021 |
Publication |
Physical Review D |
Abbreviated Journal |
Phys. Rev. D |
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Volume |
103 |
Issue |
1 |
Pages |
L011101 - 9pp |
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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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Address |
[Berguno, D. Bravo; Labarga, L.] Univ Autonoma Madrid, Dept Theoret Phys, E-28049 Madrid, Spain |
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Publisher |
Amer Physical Soc |
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English |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2470-0010 |
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Expedition |
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Conference |
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Notes |
WOS:000612138600005 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4734 |
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Author |
Super-Kamiokande Collaboration (Abe, K. et al); Molina Sedgwick, S. |
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Title |
Neutron tagging following atmospheric neutrino events in a water Cherenkov detector |
Type |
Journal Article |
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Year |
2022 |
Publication |
Journal of Instrumentation |
Abbreviated Journal |
J. Instrum. |
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Volume |
17 |
Issue |
10 |
Pages |
P10029 - 41pp |
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Keywords |
Particle identification methods; Cherenkov detectors; Neutrino detectors; Large detector systems for particle and astroparticle physics |
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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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Address |
[Abe, K.; Haga, Y.; Hayato, Y.; Hiraide, K.; Ieki, K.; Ikeda, M.; Imaizumi, S.; Iyogi, K.; Kameda, J.; Kanemura, Y.; Kataoka, Y.; Kato, Y.; Kishimoto, Y.; Miki, S.; Mine, S.; Miura, M.; Mochizuki, T.; Moriyama, S.; Nagao, Y.; Nakahata, M.; Nakajima, T.; Nakano, Y.; Nakayama, S.; Okada, T.; Okamoto, K.; Orii, A.; Sato, K.; Sekiya, H.; Shiozawa, M.; Sonoda, Y.; Suzuki, Y.; Takeda, A.; Takemoto, Y.; Takenaka, A.; Tanaka, H.; Tasaka, S.; Tomura, T.; Ueno, K.; Watanabe, S.; Yano, T.; Yokozawa, T.] Univ Tokyo, Inst Cosm Ray Res, Kamioka Observ, Gifu, Akita 5061205, Japan, Email: hayato@icrr.u-tokyo.ac.jp |
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Publisher |
IOP Publishing Ltd |
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English |
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Series Volume |
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Edition |
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ISSN |
1748-0221 |
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Area |
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Expedition |
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Conference |
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Notes |
WOS:000898723700008 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5441 |
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Author |
Strege, C.; Bertone, G.; Cerdeño, D.G.; Fornasa, M.; Ruiz de Austri, R.; Trotta, R. |
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Title |
Updated global fits of the cMSSM including the latest LHC SUSY and Higgs searches and XENON100 data |
Type |
Journal Article |
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Year |
2012 |
Publication |
Journal of Cosmology and Astroparticle Physics |
Abbreviated Journal |
J. Cosmol. Astropart. Phys. |
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Volume |
03 |
Issue |
3 |
Pages |
030 - 22pp |
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Keywords |
dark matter theory; dark matter experiments |
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Abstract |
We present new global fits of the constrained Minimal Supersymmetric Standard Model (cMSSM), including LHC 1/fb integrated luminosity SUSY exclusion limits, recent LHC 5/fb constraints on the mass of the Higgs boson and XENON100 direct detection data. Our analysis fully takes into account astrophysical and hadronic uncertainties that enter the analysis when translating direct detection limits into constraints on the cMSSM parameter space. We provide results for both a Bayesian and a Frequentist statistical analysis. We find that LHC 2011 constraints in combination with XENON100 data can rule out a significant portion of the cMSSM parameter space. Our results further emphasise the complementarity of collider experiments and direct detection searches in constraining extensions of Standard Model physics. The LHC 2011 exclusion limit strongly impacts on low-mass regions of cMSSM parameter space, such as the stau co-annihilation region, while direct detection data can rule out regions of high SUSY masses, such as the Focus-Point region, which is unreachable for the LHC in the near future. We show that, in addition to XENON100 data, the experimental constraint on the anomalous magnetic moment of the muon plays a dominant role in disfavouring large scalar and gaugino masses. We find that, should the LHC 2011 excess hinting towards a Higgs boson at 126 GeV be confirmed, currently favoured regions of the cMSSM parameter space will be robustly ruled out from both a Bayesian and a profile likelihood statistical perspective. |
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Address |
[Strege, C.; Trotta, R.] Univ London Imperial Coll Sci Technol & Med, Blackett Lab, Astrophys Grp, London SW7 2AZ, England, Email: charlotte.strege09@imperial.ac.uk; |
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Corporate Author |
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Publisher |
Iop Publishing Ltd |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
1475-7516 |
ISBN |
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Medium |
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Area |
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Expedition |
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Conference |
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Notes |
WOS:000302949600030 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
1001 |
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Author |
Stoppa, F.; Vreeswijk, P.; Bloemen, S.; Bhattacharyya, S.; Caron, S.; Johannesson, G.; Ruiz de Austri, R.; van den Oetelaar, C.; Zaharijas, G.; Groot, P.J.; Cator, E.; Nelemans, G. |
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Title |
AutoSourceID-Light Fast optical source localization via U-Net and Laplacian of Gaussian |
Type |
Journal Article |
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Year |
2022 |
Publication |
Astronomy & Astrophysics |
Abbreviated Journal |
Astron. Astrophys. |
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Volume |
662 |
Issue |
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Pages |
A109 - 8pp |
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Keywords |
astronomical databases; miscellaneous; methods; data analysis; stars; imaging; techniques; image processing |
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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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Address |
[Stoppa, F.; Vreeswijk, P.; Bloemen, S.; Groot, P. J.; Nelemans, G.] Radboud Univ Nijmegen, Dept Astrophys, IMAPP, POB 9010, NL-6500 GL Nijmegen, Netherlands, Email: f.stoppa@astro.ru.nl |
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Corporate Author |
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Thesis |
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Publisher |
Edp Sciences S A |
Place of Publication |
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Editor |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0004-6361 |
ISBN |
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Conference |
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Notes |
WOS:000818665600009 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5291 |
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Author |
Stoppa, F.; Ruiz de Austri, R.; Vreeswijk, P.; Bhattacharyya, S.; Caron, S.; Bloemen, S.; Zaharijas, G.; Principe, G.; Vodeb, V.; Groot, P.J.; Cator, E.; Nelemans, G. |
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Title |
AutoSourceID-FeatureExtractor Optical image analysis using a two-step mean variance estimation network for feature estimation and uncertainty characterisation |
Type |
Journal Article |
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Year |
2023 |
Publication |
Astronomy & Astrophysics |
Abbreviated Journal |
Astron. Astrophys. |
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Volume |
680 |
Issue |
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Pages |
A108 - 14pp |
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Keywords |
astronomical databases: miscellaneous; methods: data analysis; stars: imaging; techniques: image processing |
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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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Address |
[Stoppa, F.; Vreeswijk, P.; Bloemen, S.; Groot, P. J.; Nelemans, G.] Radboud Univ Nijmegen, Dept Astrophys, IMAPP, POB 9010, NL-6500 GL Nijmegen, Netherlands, Email: f.stoppa@astro.ru.nl |
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Corporate Author |
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Thesis |
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Publisher |
Edp Sciences S A |
Place of Publication |
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Editor |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0004-6361 |
ISBN |
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Area |
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Expedition |
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Conference |
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Notes |
WOS:001131898100003 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5887 |
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