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1. First search for K+ → π0πμe decays

Author: M.U. Ashraf, E. Cortina Gil, E. Minucci, S. Padolski, P. Petrov, A. Shaikhiev, R. Volpe, T. Numao, Y. Petrov, B. Velghe, V.W.S. Wong, D. Bryman, J. Fu, Z. Hives, T. Husek, J. Jerhot, K. Kampf, M. Kolesar, M. Zamkovsky, B. De Martino, M. Perrin-Terrin, B. Döbrich, S. Lezki, J. Schubert, A.T. Akmete, R. Aliberti, G. Khoriauli, J. Kunze, D. Lomidze, L. Peruzzo, M. Vormstein, R. Wanke, P. Dalpiaz, M. Fiorini, I. Neri, A. Norton, F. Petrucci, M. Soldani, H. Wahl, L. Bandiera, A. Cotta Ramusino, A. Gianoli, M. Romagnoni, A. Sytov, E. Iacopini, G. Latino, M. Lenti, P. Lo Chiatto, I. Panichi, A. Parenti, A. Bizzeti, F. Bucci, A. Antonelli, G. Georgiev, V. Kozhuharov, G. Lanfranchi, S. Martellotti, M. Moulson, T. Spadaro, G. Tinti, F. Ambrosino, T. Capussela, M. Corvino, M. D'Errico, D. Di Filippo, R. Fiorenza, M. Francesconi, R. Giordano, P. Massarotti, M. Mirra, M. Napolitano, I. Rosa, G. Saracino, G. Anzivino, F. Brizioli, E. Imbergamo, R. Lollini, R. Piandani, C. Santoni, M. Barbanera, P. Cenci, B. Checcucci, P. Lubrano, M. Lupi, M. Pepe, M. Piccini, F. Costantini, L. Di Lella, N. Doble, M. Giorgi, S. Giudici, G. Lamanna, E. Lari, E. Pedreschi, M. Sozzi, C. Cerri, R. Fantechi, L. Pontisso, F. Spinella, I. Mannelli, G. D'Agostini, M. Raggi, A. Biagioni, P. Cretaro, O. Frezza, E. Leonardi, A. Lonardo, M. Turisini, P. Valente, P. Vicini, R. Ammendola, V. Bonaiuto, A. Fucci, A. Salamon, F. Sargeni, R. Arcidiacono, B. Bloch-Devaux, M. Boretto, E. Menichetti, E. Migliore, D. Soldi, C. Biino, A. Filippi, F. Marchetto, A. Briano Olvera, J. Engelfried, N. Estrada-Tristan, M.A. Reyes Santos, K.A. Rodriguez Rivera, P. Boboc, A.M. Bragadireanu, S.A. Ghinescu, O.E. Hutanu, L. Bician, T. Blazek, V. Cerny, Z. Kucerova, T. Velas, J. Bernhard, A. Ceccucci, M. Ceoletta, H. Danielsson, N. De Simone, F. Duval, L. Federici, E. Gamberini, L. Gatignon, R. Guida, F. Hahn, E.B. Holzer, B. Jenninger, M. Koval, P. Laycock, G. Lehmann Miotto, P. Lichard, A. Mapelli, K. Massri, M. Noy, V. Palladino, J. Pinzino, V. Ryjov, S. Schuchmann, S. Venditti, X. Chang, A. Kleimenova, R. Marchevski, T. Bache, M.B. Brunetti, V. Duk, V. Fascianelli, J.R. Fry, F. Gonnella, E. Goudzovski, J. Henshaw, L. Iacobuzio, C. Kenworthy, C. Lazzeroni, N. Lurkin, F. Newson, C. Parkinson, A. Romano, J. Sanders, A. Sergi, A. Sturgess, J. Swallow, A. Tomczak, H. Heath, R. Page, S. Trilov, B. Angelucci, D. Britton, C. Graham, D. Protopopescu, J. Carmignani, J.B. Dainton, R.W.L. Jones, G. Ruggiero, L. Fulton, D. Hutchcroft, E. Maurice, B. Wrona, A. Conovaloff, P. Cooper, D. Coward, P. Rubin, A. Baeva, D. Baigarashev, V. Bautin, D. Emelyanov, T. Enik, V. Falaleev, S. Fedotov, K. Gorshanov, E. Gushchin, V. Kekelidze, D. Kereibay, S. Kholodenko, A. Khotyantsev, A. Korotkova, Y. Kudenko, V. Kurochka, V. Kurshetsov, L. Litov, D. Madigozhin, M. Medvedeva, A. Mefodev, M. Misheva, N. Molokanova, S. Movchan, V. Obraztsov, A. Okhotnikov, A. Ostankov, I. Polenkevich, Yu. Potrebenikov, A. Sadovskiy, K. Salamatin, V. Semenov, S. Shkarovskiy, V. Sugonyaev, O. Yushchenko, and A. Zinchenko
Subjects: Physics, QC1-999
Abstract: The first search for the lepton number violating decay K+→π0π−μ+e+ and lepton flavour violating decays K+→π0π+μ−e+, K+→π0π+μ+e− has been performed using a dataset collected by the NA62 experiment at CERN in 2016–2018. Upper limits of 2.9×10−10, 3.1×10−10 and 5.0×10−10, respectively, are obtained at 90% CL for the branching ratios of the three decays on the assumption of uniform phase-space distributions.
Published: 2024
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2. Measurement of the K+ → π+γγ decay

Author: E. Cortina Gil, A. Kleimenova, E. Minucci, S. Padolski, P. Petrov, A. Shaikhiev, R. Volpe, T. Numao, Y. Petrov, B. Velghe, V.W.S. Wong, D. Bryman, J. Fu, Z. Hives, T. Husek, J. Jerhot, K. Kampf, M. Zamkovsky, B. De Martino, M. Perrin-Terrin, B. Döbrich, S. Lezki, A.T. Akmete, R. Aliberti, G. Khoriauli, J. Kunze, D. Lomidze, L. Peruzzo, M. Vormstein, R. Wanke, P. Dalpiaz, M. Fiorini, A. Mazzolari, I. Neri, A. Norton, F. Petrucci, M. Soldani, H. Wahl, L. Bandiera, A. Cotta Ramusino, A. Gianoli, M. Romagnoni, A. Sytov, E. Iacopini, G. Latino, M. Lenti, P. Lo Chiatto, I. Panichi, A. Parenti, A. Bizzeti, F. Bucci, A. Antonelli, G. Georgiev, V. Kozhuharov, G. Lanfranchi, S. Martellotti, M. Moulson, T. Spadaro, G. Tinti, F. Ambrosino, T. Capussela, M. Corvino, M. D'Errico, D. Di Filippo, R. Fiorenza, R. Giordano, P. Massarotti, M. Mirra, M. Napolitano, I. Rosa, G. Saracino, G. Anzivino, F. Brizioli, E. Imbergamo, R. Lollini, R. Piandani, C. Santoni, M. Barbanera, P. Cenci, B. Checcucci, P. Lubrano, M. Lupi, M. Pepe, M. Piccini, F. Costantini, L. Di Lella, N. Doble, M. Giorgi, S. Giudici, G. Lamanna, E. Lari, E. Pedreschi, M. Sozzi, C. Cerri, R. Fantechi, L. Pontisso, F. Spinella, I. Mannelli, G. D'Agostini, M. Raggi, A. Biagioni, P. Cretaro, O. Frezza, E. Leonardi, A. Lonardo, M. Turisini, P. Valente, P. Vicini, R. Ammendola, V. Bonaiuto, A. Fucci, A. Salamon, F. Sargeni, R. Arcidiacono, B. Bloch-Devaux, M. Boretto, E. Menichetti, E. Migliore, D. Soldi, C. Biino, A. Filippi, F. Marchetto, A. Briano Olvera, J. Engelfried, N. Estrada-Tristan, M.A. Reyes Santos, K.A. Rodriguez Rivera, P. Boboc, A.M. Bragadireanu, S.A. Ghinescu, O.E. Hutanu, L. Bician, T. Blazek, V. Cerny, Z. Kucerova, J. Bernhard, A. Ceccucci, M. Ceoletta, H. Danielsson, N. De Simone, F. Duval, L. Federici, E. Gamberini, L. Gatignon, R. Guida, F. Hahn, E.B. Holzer, B. Jenninger, M. Koval, P. Laycock, G. Lehmann Miotto, P. Lichard, A. Mapelli, R. Marchevski, K. Massri, M. Noy, V. Palladino, J. Pinzino, V. Ryjov, S. Schuchmann, S. Venditti, T. Bache, M.B. Brunetti, V. Duk, V. Fascianelli, J.R. Fry, F. Gonnella, E. Goudzovski, J. Henshaw, L. Iacobuzio, C. Kenworthy, C. Lazzeroni, N. Lurkin, F. Newson, C. Parkinson, A. Romano, J. Sanders, A. Sergi, A. Sturgess, J. Swallow, A. Tomczak, H. Heath, R. Page, S. Trilov, B. Angelucci, D. Britton, C. Graham, D. Protopopescu, J. Carmignani, J.B. Dainton, R.W.L. Jones, G. Ruggiero, L. Fulton, D. Hutchcroft, E. Maurice, B. Wrona, A. Conovaloff, P. Cooper, D. Coward, P. Rubin, A. Baeva, D. Baigarashev, D. Emelyanov, T. Enik, V. Falaleev, S. Fedotov, K. Gorshanov, E. Gushchin, V. Kekelidze, D. Kereibay, S. Kholodenko, A. Khotyantsev, A. Korotkova, Y. Kudenko, V. Kurochka, V. Kurshetsov, L. Litov, D. Madigozhin, M. Medvedeva, A. Mefodev, M. Misheva, N. Molokanova, S. Movchan, V. Obraztsov, A. Okhotnikov, A. Ostankov, I. Polenkevich, Yu. Potrebenikov, A. Sadovskiy, V. Semenov, S. Shkarovskiy, V. Sugonyaev, O. Yushchenko, and A. Zinchenko
Subjects: Physics, QC1-999
Abstract: A sample of 3984 candidates of the K+→π+γγ decay, with an estimated background of 291±14 events, was collected by the NA62 experiment at CERN during 2017–2018. In order to describe the observed di-photon mass spectrum, the next-to-leading order contribution in chiral perturbation theory was found to be necessary. The decay branching ratio in the full kinematic range is measured to be (9.61±0.17)×10−7. The first search for production and prompt decay of an axion-like particle with gluon coupling in the process K+→π+a, a→γγ is also reported.
Published: 2024
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3. Improved calorimetric particle identification in NA62 using machine learning techniques

Author: The NA62 collaboration, E. Cortina Gil, A. Kleimenova, E. Minucci, S. Padolski, P. Petrov, A. Shaikhiev, R. Volpe, W. Fedorko, T. Numao, Y. Petrov, B. Velghe, V. W. S. Wong, M. Yu, D. Bryman, J. Fu, Z. Hives, T. Husek, J. Jerhot, K. Kampf, M. Zamkovsky, B. De Martino, M. Perrin-Terrin, A. T. Akmete, R. Aliberti, G. Khoriauli, J. Kunze, D. Lomidze, L. Peruzzo, M. Vormstein, R. Wanke, P. Dalpiaz, M. Fiorini, A. Mazzolari, I. Neri, A. Norton, F. Petrucci, M. Soldani, H. Wahl, L. Bandiera, A. Cotta Ramusino, A. Gianoli, M. Romagnoni, A. Sytov, E. Iacopini, G. Latino, M. Lenti, P. Lo Chiatto, I. Panichi, A. Parenti, A. Bizzeti, F. Bucci, A. Antonelli, G. Georgiev, V. Kozhuharov, G. Lanfranchi, S. Martellotti, M. Moulson, T. Spadaro, G. Tinti, F. Ambrosino, T. Capussela, M. Corvino, M. D’Errico, D. Di Filippo, R. Fiorenza, R. Giordano, P. Massarotti, M. Mirra, M. Napolitano, I. Rosa, G. Saracino, G. Anzivino, F. Brizioli, E. Imbergamo, R. Lollini, R. Piandani, C. Santoni, M. Barbanera, P. Cenci, B. Checcucci, P. Lubrano, M. Lupi, M. Pepe, M. Piccini, F. Costantini, L. Di Lella, N. Doble, M. Giorgi, S. Giudici, G. Lamanna, E. Lari, E. Pedreschi, M. Sozzi, C. Cerri, R. Fantechi, L. Pontisso, F. Spinella, I. Mannelli, G. D’Agostini, M. Raggi, A. Biagioni, P. Cretaro, O. Frezza, E. Leonardi, A. Lonardo, M. Turisini, P. Valente, P. Vicini, R. Ammendola, V. Bonaiuto, A. Fucci, A. Salamon, F. Sargeni, R. Arcidiacono, B. Bloch-Devaux, M. Boretto, E. Menichetti, E. Migliore, D. Soldi, C. Biino, A. Filippi, F. Marchetto, A. Briano Olvera, J. Engelfried, N. Estrada-Tristan, M. A. Reyes Santos, P. Boboc, A. M. Bragadireanu, S. A. Ghinescu, O. E. Hutanu, L. Bician, T. Blazek, V. Cerny, Z. Kucerova, J. Bernhard, A. Ceccucci, M. Ceoletta, H. Danielsson, N. De Simone, F. Duval, B. Döbrich, L. Federici, E. Gamberini, L. Gatignon, R. Guida, F. Hahn, E. B. Holzer, B. Jenninger, M. Koval, P. Laycock, G. Lehmann Miotto, P. Lichard, A. Mapelli, R. Marchevski, K. Massri, M. Noy, V. Palladino, J. Pinzino, V. Ryjov, S. Schuchmann, S. Venditti, T. Bache, M. B. Brunetti, V. Duk, V. Fascianelli, J. R. Fry, F. Gonnella, E. Goudzovski, J. Henshaw, L. Iacobuzio, C. Kenworthy, C. Lazzeroni, N. Lurkin, F. Newson, C. Parkinson, A. Romano, J. Sanders, A. Sergi, A. Sturgess, J. Swallow, A. Tomczak, H. Heath, R. Page, S. Trilov, B. Angelucci, D. Britton, C. Graham, D. Protopopescu, J. Carmignani, J. B. Dainton, R. W. L. Jones, G. Ruggiero, L. Fulton, D. Hutchcroft, E. Maurice, B. Wrona, A. Conovaloff, P. Cooper, D. Coward, P. Rubin, A. Baeva, D. Baigarashev, D. Emelyanov, T. Enik, V. Falaleev, S. Fedotov, K. Gorshanov, E. Gushchin, V. Kekelidze, D. Kereibay, S. Kholodenko, A. Khotyantsev, A. Korotkova, Y. Kudenko, V. Kurochka, V. Kurshetsov, L. Litov, D. Madigozhin, M. Medvedeva, A. Mefodev, M. Misheva, N. Molokanova, S. Movchan, V. Obraztsov, A. Okhotnikov, A. Ostankov, I. Polenkevich, Yu. Potrebenikov, A. Sadovskiy, V. Semenov, S. Shkarovskiy, V. Sugonyaev, O. Yushchenko, and A. Zinchenko
Subjects: Fixed Target Experiments, Branching fraction, Rare Decay, Flavour Physics, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract: Abstract Measurement of the ultra-rare K + → π + ν ν ¯ $$ {K}^{+}\to {\pi}^{+}\nu \overline{\nu} $$ decay at the NA62 experiment at CERN requires high-performance particle identification to distinguish muons from pions. Calorimetric identification currently in use, based on a boosted decision tree algorithm, achieves a muon misidentification probability of 1.2 × 10 −5 for a pion identification efficiency of 75% in the momentum range of 15–40 GeV/c. In this work, calorimetric identification performance is improved by developing an algorithm based on a convolutional neural network classifier augmented by a filter. Muon misidentification probability is reduced by a factor of six with respect to the current value for a fixed pion-identification efficiency of 75%. Alternatively, pion identification efficiency is improved from 72% to 91% for a fixed muon misidentification probability of 10 −5.
Published: 2023
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4. A study of the K + → π 0 e + νγ decay

Author: The NA62 collaboration, E. Cortina Gil, A. Kleimenova, E. Minucci, S. Padolski, P. Petrov, A. Shaikhiev, R. Volpe, T. Numao, Y. Petrov, B. Velghe, V. W. S. Wong, D. Bryman, J. Fu, Z. Hives, T. Husek, J. Jerhot, K. Kampf, M. Zamkovsky, B. De Martino, M. Perrin-Terrin, A. T. Akmete, R. Aliberti, G. Khoriauli, J. Kunze, D. Lomidze, L. Peruzzo, M. Vormstein, R. Wanke, P. Dalpiaz, M. Fiorini, A. Mazzolari, I. Neri, A. Norton, F. Petrucci, M. Soldani, H. Wahl, L. Bandiera, A. Cotta Ramusino, A. Gianoli, M. Romagnoni, A. Sytov, E. Iacopini, G. Latino, M. Lenti, P. Lo Chiatto, I. Panichi, A. Parenti, A. Bizzeti, F. Bucci, A. Antonelli, G. Georgiev, V. Kozhuharov, G. Lanfranchi, S. Martellotti, M. Moulson, T. Spadaro, G. Tinti, F. Ambrosino, T. Capussela, M. Corvino, M. D’Errico, D. Di Filippo, R. Fiorenza, R. Giordano, P. Massarotti, M. Mirra, M. Napolitano, I. Rosa, G. Saracino, G. Anzivino, F. Brizioli, E. Imbergamo, R. Lollini, R. Piandani, C. Santoni, M. Barbanera, P. Cenci, B. Checcucci, P. Lubrano, M. Lupi, M. Pepe, M. Piccini, F. Costantini, L. Di Lella, N. Doble, M. Giorgi, S. Giudici, G. Lamanna, E. Lari, E. Pedreschi, M. Sozzi, C. Cerri, R. Fantechi, L. Pontisso, F. Spinella, I. Mannelli, G. D’Agostini, M. Raggi, A. Biagioni, P. Cretaro, O. Frezza, E. Leonardi, A. Lonardo, M. Turisini, P. Valente, P. Vicini, R. Ammendola, V. Bonaiuto, A. Fucci, A. Salamon, F. Sargeni, R. Arcidiacono, B. Bloch-Devaux, M. Boretto, E. Menichetti, E. Migliore, D. Soldi, C. Biino, A. Filippi, F. Marchetto, A. Briano Olvera, J. Engelfried, N. Estrada-Tristan, M. A. Reyes Santos, P. Boboc, A. M. Bragadireanu, S. A. Ghinescu, O. E. Hutanu, L. Bician, T. Blazek, V. Cerny, Z. Kucerova, J. Bernhard, A. Ceccucci, M. Ceoletta, H. Danielsson, N. De Simone, F. Duval, B. Döbrich, L. Federici, E. Gamberini, L. Gatignon, R. Guida, F. Hahn, E. B. Holzer, B. Jenninger, M. Koval, P. Laycock, G. Lehmann Miotto, P. Lichard, A. Mapelli, R. Marchevski, K. Massri, M. Noy, V. Palladino, J. Pinzino, V. Ryjov, S. Schuchmann, S. Venditti, T. Bache, M. B. Brunetti, V. Duk, V. Fascianelli, J. R. Fry, F. Gonnella, E. Goudzovski, J. Henshaw, L. Iacobuzio, C. Kenworthy, C. Lazzeroni, N. Lurkin, F. Newson, C. Parkinson, A. Romano, J. Sanders, A. Sergi, A. Sturgess, J. Swallow, A. Tomczak, H. Heath, R. Page, S. Trilov, B. Angelucci, D. Britton, C. Graham, D. Protopopescu, J. Carmignani, J. B. Dainton, R. W. L. Jones, G. Ruggiero, L. Fulton, D. Hutchcroft, E. Maurice, B. Wrona, A. Conovaloff, P. Cooper, D. Coward, P. Rubin, A. Baeva, D. Baigarashev, D. Emelyanov, T. Enik, V. Falaleev, S. Fedotov, K. Gorshanov, E. Gushchin, V. Kekelidze, D. Kereibay, S. Kholodenko, A. Khotyantsev, A. Korotkova, Y. Kudenko, V. Kurochka, V. Kurshetsov, L. Litov, D. Madigozhin, M. Medvedeva, A. Mefodev, M. Misheva, N. Molokanova, S. Movchan, V. Obraztsov, A. Okhotnikov, A. Ostankov, I. Polenkevich, Yu. Potrebenikov, A. Sadovskiy, V. Semenov, S. Shkarovskiy, V. Sugonyaev, O. Yushchenko, and A. Zinchenko
Subjects: Branching fraction, Electroweak Interaction, Fixed Target Experiments, Flavour Physics, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract: Abstract A sample of 1.3 × 105 K + → π 0 e + νγ candidates with less than 1% background was collected by the NA62 experiment at the CERN SPS in 2017–2018. Branching fraction measurements are obtained at percent relative precision in three restricted kinematic regions, improving on existing results by a factor larger than two. An asymmetry, possibly related to T-violation, is investigated with no evidence observed within the achieved precision.
Published: 2023
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5. Search for dark photon decays to μ + μ − at NA62

Author: The NA62 collaboration, E. Cortina Gil, J. Jerhot, A. Kleimenova, N. Lurkin, M. Zamkovsky, T. Numao, B. Velghe, V. W. S. Wong, D. Bryman, Z. Hives, T. Husek, K. Kampf, M. Koval, B. De Martino, M. Perrin-Terrin, A. T. Akmete, R. Aliberti, L. Di Lella, N. Doble, L. Peruzzo, S. Schuchmann, H. Wahl, R. Wanke, P. Dalpiaz, A. Mazzolari, I. Neri, F. Petrucci, M. Soldani, L. Bandiera, A. Cotta Ramusino, A. Gianoli, M. Romagnoni, A. Sytov, M. Lenti, P. Lo Chiatto, R. Marchevski, I. Panichi, G. Ruggiero, A. Bizzeti, F. Bucci, A. Antonelli, V. Kozhuharov, G. Lanfranchi, S. Martellotti, M. Moulson, T. Spadaro, G. Tinti, F. Ambrosino, M. D’Errico, R. Fiorenza, R. Giordano, P. Massarotti, M. Mirra, M. Napolitano, I. Rosa, G. Saracino, G. Anzivino, F. Brizioli, P. Cenci, V. Duk, R. Lollini, P. Lubrano, M. Pepe, M. Piccini, F. Costantini, M. Giorgi, S. Giudici, G. Lamanna, E. Lari, E. Pedreschi, J. Pinzino, M. Sozzi, R. Fantechi, F. Spinella, I. Mannelli, M. Raggi, A. Biagioni, P. Cretaro, O. Frezza, A. Lonardo, M. Turisini, P. Vicini, R. Ammendola, V. Bonaiuto, A. Fucci, A. Salamon, F. Sargeni, R. Arcidiacono, B. Bloch-Devaux, E. Menichetti, E. Migliore, C. Biino, A. Filippi, F. Marchetto, D. Soldi, A. Briano Olvera, J. Engelfried, N. Estrada-Tristan, R. Piandani, M. A. Reyes Santos, P. Boboc, A. M. Bragadireanu, S. A. Ghinescu, O. E. Hutanu, T. Blazek, V. Cerny, Z. Kucerova, R. Volpe, J. Bernhard, L. Bician, M. Boretto, A. Ceccucci, M. Ceoletta, M. Corvino, H. Danielsson, F. Duval, B. Döbrich, L. Federici, E. Gamberini, R. Guida, E. B. Holzer, B. Jenninger, G. Lehmann Miotto, P. Lichard, K. Massri, E. Minucci, M. Noy, V. Ryjov, J. Swallow, J. R. Fry, F. Gonnella, E. Goudzovski, J. Henshaw, C. Kenworthy, C. Lazzeroni, C. Parkinson, A. Romano, J. Sanders, A. Sergi, A. Shaikhiev, A. Tomczak, H. Heath, D. Britton, A. Norton, D. Protopopescu, J. B. Dainton, L. Gatignon, R. W. L. Jones, P. Cooper, D. Coward, P. Rubin, A. Baeva, D. Baigarashev, D. Emelyanov, T. Enik, V. Falaleev, S. Fedotov, K. Gorshanov, E. Gushchin, V. Kekelidze, D. Kereibay, S. Kholodenko, A. Khotyantsev, A. Korotkova, Y. Kudenko, V. Kurochka, V. Kurshetsov, L. Litov, D. Madigozhin, A. Mefodev, M. Misheva, N. Molokanova, V. Obraztsov, A. Okhotnikov, I. Polenkevich, Yu. Potrebenikov, A. Sadovskiy, S. Shkarovskiy, V. Sugonyaev, and O. Yushchenko
Subjects: Beyond Standard Model, Exotics, Fixed Target Experiments, Dark Matter, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract: Abstract The NA62 experiment at CERN, designed to study the ultra-rare decay K + → π + ν ν ¯ $$ \nu \overline{\nu} $$ , has also collected data in beam-dump mode. In this configuration, dark photons may be produced by protons dumped on an absorber and reach a decay volume beginning 80 m downstream. A search for dark photons decaying in flight to μ + μ − pairs is reported, based on a sample of 1.4 × 1017 protons on dump collected in 2021. No evidence for a dark photon signal is observed. A region of the parameter space is excluded at 90% CL, improving on previous experimental limits for dark photon masses between 215 and 550 MeV/c 2.
Published: 2023
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6. Addendum to: A measurement of the K + → π + μ + μ − decay

Author: The NA62 collaboration, E. Cortina Gil, A. Kleimenova, E. Minucci, S. Padolski, P. Petrov, A. Shaikhiev, R. Volpe, T. Numao, Y. Petrov, B. Velghe, V. W. S. Wong, D. Bryman, J. Fu, T. Husek, J. Jerhot, K. Kampf, M. Zamkovsky, R. Aliberti, G. Khoriauli, J. Kunze, D. Lomidze, L. Peruzzo, M. Vormstein, R. Wanke, P. Dalpiaz, M. Fiorini, I. Neri, A. Norton, F. Petrucci, H. Wahl, A. Cotta Ramusino, A. Gianoli, E. Iacopini, G. Latino, M. Lenti, A. Parenti, A. Bizzeti, F. Bucci, A. Antonelli, G. Georgiev, V. Kozhuharov, G. Lanfranchi, S. Martellotti, M. Moulson, T. Spadaro, G. Tinti, F. Ambrosino, T. Capussela, M. Corvino, D. Di Filippo, R. Fiorenza, P. Massarotti, M. Mirra, M. Napolitano, G. Saracino, G. Anzivino, F. Brizioli, E. Imbergamo, R. Lollini, R. Piandani, C. Santoni, M. Barbanera, P. Cenci, B. Checcucci, P. Lubrano, M. Lupi, M. Pepe, M. Piccini, F. Costantini, L. Di Lella, N. Doble, M. Giorgi, S. Giudici, G. Lamanna, E. Lari, E. Pedreschi, M. Sozzi, C. Cerri, R. Fantechi, L. Pontisso, F. Spinella, I. Mannelli, G. D’Agostini, M. Raggi, A. Biagioni, P. Cretaro, O. Frezza, E. Leonardi, A. Lonardo, M. Turisini, P. Valente, P. Vicini, R. Ammendola, V. Bonaiuto, A. Fucci, A. Salamon, F. Sargeni, R. Arcidiacono, B. Bloch-Devaux, M. Boretto, E. Menichetti, E. Migliore, D. Soldi, C. Biino, A. Filippi, F. Marchetto, J. Engelfried, N. Estrada-Tristan, A. M. Bragadireanu, S. A. Ghinescu, O. E. Hutanu, A. Baeva, D. Baigarashev, D. Emelyanov, T. Enik, V. Falaleev, V. Kekelidze, A. Korotkova, L. Litov, D. Madigozhin, M. Misheva, N. Molokanova, S. Movchan, I. Polenkevich, Yu. Potrebenikov, S. Shkarovskiy, A. Zinchenko, S. Fedotov, E. Gushchin, A. Khotyantsev, Y. Kudenko, V. Kurochka, M. Medvedeva, A. Mefodev, S. Kholodenko, V. Kurshetsov, V. Obraztsov, A. Ostankov, V. Semenov, V. Sugonyaev, O. Yushchenko, L. Bician, T. Blazek, V. Cerny, Z. Kucerova, J. Bernhard, A. Ceccucci, H. Danielsson, N. De Simone, F. Duval, B. Döbrich, L. Federici, E. Gamberini, L. Gatignon, R. Guida, F. Hahn, E. B. Holzer, B. Jenninger, M. Koval, P. Laycock, G. Lehmann Miotto, P. Lichard, A. Mapelli, R. Marchevski, K. Massri, M. Noy, V. Palladino, M. Perrin-Terrin, J. Pinzino, V. Ryjov, S. Schuchmann, S. Venditti, T. Bache, M. B. Brunetti, V. Duk, V. Fascianelli, J. R. Fry, F. Gonnella, E. Goudzovski, J. Henshaw, L. Iacobuzio, C. Lazzeroni, N. Lurkin, F. Newson, C. Parkinson, A. Romano, A. Sergi, A. Sturgess, J. Swallow, A. Tomczak, H. Heath, R. Page, S. Trilov, B. Angelucci, D. Britton, C. Graham, D. Protopopescu, J. Carmignani, J. B. Dainton, R. W. L. Jones, G. Ruggiero, L. Fulton, D. Hutchcroft, E. Maurice, B. Wrona, A. Conovaloff, P. Cooper, D. Coward, and P. Rubin
Subjects: Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Published: 2023
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7. Performance of the NA62 trigger system

Author: The NA62 collaboration, E. Cortina Gil, A. Kleimenova, E. Minucci, S. Padolski, P. Petrov, A. Shaikhiev, R. Volpe, T. Numao, Y. Petrov, B. Velghe, V. W. S. Wong, D. Bryman, J. Fu, T. Husek, J. Jerhot, K. Kampf, M. Zamkovsky, R. Aliberti, G. Khoriauli, J. Kunze, D. Lomidze, L. Peruzzo, M. Vormstein, R. Wanke, P. Dalpiaz, M. Fiorini, I. Neri, A. Norton, F. Petrucci, H. Wahl, A. Cotta Ramusino, A. Gianoli, E. Iacopini, G. Latino, M. Lenti, A. Parenti, A. Bizzeti, F. Bucci, A. Antonelli, G. Georgiev, V. Kozhuharov, G. Lanfranchi, S. Martellotti, M. Moulson, T. Spadaro, G. Tinti, F. Ambrosino, T. Capussela, M. Corvino, D. Di Filippo, R. Fiorenza, P. Massarotti, M. Mirra, M. Napolitano, G. Saracino, G. Anzivino, F. Brizioli, E. Imbergamo, R. Lollini, R. Piandani, C. Santoni, M. Barbanera, P. Cenci, B. Checcucci, P. Lubrano, M. Lupi, M. Pepe, M. Piccini, F. Costantini, L. Di Lella, N. Doble, M. Giorgi, S. Giudici, G. Lamanna, E. Lari, E. Pedreschi, M. Sozzi, C. Cerri, R. Fantechi, L. Pontisso, F. Spinella, I. Mannelli, G. D’Agostini, M. Raggi, A. Biagioni, P. Cretaro, O. Frezza, E. Leonardi, A. Lonardo, M. Turisini, P. Valente, P. Vicini, R. Ammendola, V. Bonaiuto, A. Fucci, A. Salamon, F. Sargeni, R. Arcidiacono, B. Bloch-Devaux, M. Boretto, E. Menichetti, E. Migliore, D. Soldi, C. Biino, A. Filippi, F. Marchetto, J. Engelfried, N. Estrada-Tristan, A. M. Bragadireanu, S. A. Ghinescu, O. E. Hutanu, A. Baeva, D. Baigarashev, D. Emelyanov, T. Enik, V. Falaleev, V. Kekelidze, A. Korotkova, L. Litov, D. Madigozhin, M. Misheva, N. Molokanova, S. Movchan, I. Polenkevich, Yu. Potrebenikov, S. Shkarovskiy, A. Zinchenko, S. Fedotov, E. Gushchin, A. Khotyantsev, Y. Kudenko, V. Kurochka, M. Medvedeva, A. Mefodev, S. Kholodenko, V. Kurshetsov, V. Obraztsov, A. Ostankov, V. Semenov, V. Sugonyaev, O. Yushchenko, L. Bician, T. Blazek, V. Cerny, Z. Kucerova, J. Bernhard, A. Ceccucci, H. Danielsson, N. De Simone, F. Duval, B. Döbrich, L. Federici, E. Gamberini, L. Gatignon, R. Guida, F. Hahn, E. B. Holzer, B. Jenninger, M. Koval, P. Laycock, G. Lehmann Miotto, P. Lichard, A. Mapelli, R. Marchevski, K. Massri, M. Noy, V. Palladino, M. Perrin-Terrin, J. Pinzino, V. Ryjov, S. Schuchmann, S. Venditti, T. Bache, M. B. Brunetti, V. Duk, V. Fascianelli, J. R. Fry, F. Gonnella, E. Goudzovski, J. Henshaw, L. Iacobuzio, C. Lazzeroni, N. Lurkin, F. Newson, C. Parkinson, A. Romano, A. Sergi, A. Sturgess, J. Swallow, A. Tomczak, H. Heath, R. Page, S. Trilov, B. Angelucci, D. Britton, C. Graham, D. Protopopescu, J. Carmignani, J. B. Dainton, R. W. L. Jones, G. Ruggiero, L. Fulton, D. Hutchcroft, E. Maurice, B. Wrona, A. Conovaloff, P. Cooper, D. Coward, and P. Rubin
Subjects: Fixed Target Experiments, Flavour Physics, Flavour Changing Neutral Currents, Rare Decay, Trigger, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract: Abstract The NA62 experiment at CERN targets the measurement of the ultra-rare K + → π + ν ν ¯ $$ {K}^{+}\to {\pi}^{+}\nu \overline{\nu} $$ decay, and carries out a broad physics programme that includes probes for symmetry violations and searches for exotic particles. Data were collected in 2016–2018 using a multi-level trigger system, which is described highlighting performance studies based on 2018 data.
Published: 2023
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8. Search for K+ decays into the π+e+e−e+e− final state

Author: E. Cortina Gil, A. Kleimenova, E. Minucci, S. Padolski, P. Petrov, A. Shaikhiev, R. Volpe, T. Numao, Y. Petrov, B. Velghe, V.W.S. Wong, D. Bryman, J. Fu, Z. Hives, T. Husek, J. Jerhot, K. Kampf, M. Zamkovsky, B. De Martino, M. Perrin-Terrin, A.T. Akmete, R. Aliberti, G. Khoriauli, J. Kunze, D. Lomidze, L. Peruzzo, M. Vormstein, R. Wanke, P. Dalpiaz, M. Fiorini, A. Mazzolari, I. Neri, A. Norton, F. Petrucci, M. Soldani, H. Wahl, L. Bandiera, A. Cotta Ramusino, A. Gianoli, M. Romagnoni, A. Sytov, E. Iacopini, G. Latino, M. Lenti, P. Lo Chiatto, I. Panichi, A. Parenti, A. Bizzeti, F. Bucci, A. Antonelli, G. Georgiev, V. Kozhuharov, G. Lanfranchi, S. Martellotti, M. Moulson, T. Spadaro, G. Tinti, F. Ambrosino, T. Capussela, M. Corvino, M. D'Errico, D. Di Filippo, R. Fiorenza, R. Giordano, P. Massarotti, M. Mirra, M. Napolitano, I. Rosa, G. Saracino, G. Anzivino, F. Brizioli, E. Imbergamo, R. Lollini, R. Piandani, C. Santoni, M. Barbanera, P. Cenci, B. Checcucci, P. Lubrano, M. Lupi, M. Pepe, M. Piccini, F. Costantini, L. Di Lella, N. Doble, M. Giorgi, S. Giudici, G. Lamanna, E. Lari, E. Pedreschi, M. Sozzi, C. Cerri, R. Fantechi, L. Pontisso, F. Spinella, I. Mannelli, G. D'Agostini, M. Raggi, A. Biagioni, P. Cretaro, O. Frezza, E. Leonardi, A. Lonardo, M. Turisini, P. Valente, P. Vicini, R. Ammendola, V. Bonaiuto, A. Fucci, A. Salamon, F. Sargeni, R. Arcidiacono, B. Bloch-Devaux, M. Boretto, E. Menichetti, E. Migliore, D. Soldi, C. Biino, A. Filippi, F. Marchetto, A. Briano Olvera, J. Engelfried, N. Estrada-Tristan, M.A. Reyes Santos, P. Boboc, A.M. Bragadireanu, S.A. Ghinescu, O.E. Hutanu, L. Bician, T. Blazek, V. Cerny, Z. Kucerova, J. Bernhard, A. Ceccucci, M. Ceoletta, H. Danielsson, N. De Simone, F. Duval, B. Döbrich, L. Federici, E. Gamberini, L. Gatignon, R. Guida, F. Hahn, E.B. Holzer, B. Jenninger, M. Koval, P. Laycock, G. Lehmann Miotto, P. Lichard, A. Mapelli, R. Marchevski, K. Massri, M. Noy, V. Palladino, J. Pinzino, V. Ryjov, S. Schuchmann, S. Venditti, T. Bache, M.B. Brunetti, V. Duk, V. Fascianelli, J.R. Fry, F. Gonnella, E. Goudzovski, J. Henshaw, L. Iacobuzio, C. Kenworthy, C. Lazzeroni, N. Lurkin, F. Newson, C. Parkinson, A. Romano, J. Sanders, A. Sergi, A. Sturgess, J. Swallow, A. Tomczak, H. Heath, R. Page, S. Trilov, B. Angelucci, D. Britton, C. Graham, D. Protopopescu, J. Carmignani, J.B. Dainton, R.W.L. Jones, G. Ruggiero, L. Fulton, D. Hutchcroft, E. Maurice, B. Wrona, A. Conovaloff, P. Cooper, D. Coward, P. Rubin, A. Baeva, D. Baigarashev, D. Emelyanov, T. Enik, V. Falaleev, S. Fedotov, K. Gorshanov, E. Gushchin, V. Kekelidze, D. Kereibay, S. Kholodenko, A. Khotyantsev, A. Korotkova, Y. Kudenko, V. Kurochka, V. Kurshetsov, L. Litov, D. Madigozhin, M. Medvedeva, A. Mefodev, M. Misheva, N. Molokanova, S. Movchan, V. Obraztsov, A. Okhotnikov, A. Ostankov, I. Polenkevich, Yu. Potrebenikov, A. Sadovskiy, V. Semenov, S. Shkarovskiy, V. Sugonyaev, O. Yushchenko, and A. Zinchenko
Subjects: Physics, QC1-999
Abstract: The first search for ultra-rare K+ decays into the π+e+e−e+e− final state is reported, using a dataset collected by the NA62 experiment at CERN in 2017–2018. An upper limit of 1.4×10−8 at 90% CL is obtained for the branching ratio of the K+→π+e+e−e+e− decay, predicted in the Standard Model to be (7.2±0.7)×10−11. Upper limits at 90% CL are obtained at the level of 10−9 for the branching ratios of two prompt decay chains involving pair-production of hidden-sector mediators: K+→π+aa, a→e+e− and K+→π+S, S→A′A′, A′→e+e−.
Published: 2023
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9. Centering Quality, Centering Equity: Lessons Learned in Increasing Early Childhood Educator Credentials. A Joint Report of the Institute for College Access & Success and the Georgetown University Center on Poverty and Inequality

Author: The Institute for College Access & Success (TICAS), Georgetown University Law Center, Center on Poverty and Inequality (GCPI), Casey Khánh Nguy?n, Marshall Anthony, Jaime Ramirez-Mendoza, Mayra Nuñez Martinez, Jalil Bishop, Tanya I. Garcia, Natalia Cooper, and Danilo Trisi
Abstract: Thriving communities depend on a strong early childhood education (ECE) system--one where both young children and members of the workforce are served and supported. In recent years, state government leaders have increasingly focused on changing qualifications for specific ECE roles, as increasing credential requirements has sometimes been associated with increasing quality. However, across the country, early childhood educators face significant barriers to economic security and continuing education--all while supporting children, parents, and their communities with specialized education services. This report examines the barriers that even highly qualified early child educators experience in the face of increased credential requirements. Featuring case studies of California and Washington, D.C., the report offers implications for policies that protect educators and advance racial and economic equity in the profession. Included with the report are an Executive Summary encapsulating the findings and a Technical Document describing the methodology, data collection, and quantitative analysis.
Published: 2024

10. Overview of T and D–T results in JET with ITER-like wall

Author: C.F. Maggi, D. Abate, N. Abid, P. Abreu, O. Adabonyan, M. Afzal, I. Ahmad, M. Akhtar, R. Albanese, S. Aleiferis, E. Alessi, P. Aleynikov, J. Alguacil, J. Alhage, M. Ali, H. Allen, M. Allinson, M. Alonzo, E. Alves, R. Ambrosino, E. Andersson Sundén, P. Andrew, M. Angelone, C. Angioni, I. Antoniou, L. Appel, C. Appelbee, C. Aramunde, M. Ariola, G. Arnoux, G. Artaserse, J.-F. Artaud, W. Arter, V. Artigues, F.J. Artola, A. Ash, O. Asztalos, D. Auld, F. Auriemma, Y. Austin, L. Avotina, J. Ayllón, E. Aymerich, A. Baciero, L. Bähner, F. Bairaktaris, I. Balboa, M. Balden, N. Balshaw, V.K. Bandaru, J. Banks, A. Banon Navarro, C. Barcellona, O. Bardsley, M. Barnes, R. Barnsley, M. Baruzzo, M. Bassan, A. Batista, P. Batistoni, L. Baumane, B. Bauvir, L. Baylor, C. Bearcroft, P. Beaumont, D. Beckett, A. Begolli, M. Beidler, N. Bekris, M. Beldishevski, E. Belli, F. Belli, S. Benkadda, J. Bentley, E. Bernard, J. Bernardo, M. Bernert, M. Berry, L. Bertalot, H. Betar, M. Beurskens, P.G. Bhat, S. Bickerton, J. Bielecki, T. Biewer, R. Bilato, P. Bílková, G. Birkenmeier, R. Bisson, J.P.S. Bizarro, P. Blatchford, A. Bleasdale, V. Bobkov, A. Boboc, A. Bock, G. Bodnar, P. Bohm, L. Bonalumi, N. Bonanomi, D. Bonfiglio, X. Bonnin, P. Bonofiglo, J. Booth, D. Borba, D. Borodin, I. Borodkina, T.O.S.J. Bosman, C. Bourdelle, M. Bowden, I. Božičević Mihalić, S.C. Bradnam, B. Breizman, S. Brezinsek, D. Brida, M. Brix, P. Brown, D. Brunetti, M. Buckley, J. Buermans, H. Bufferand, P. Buratti, A. Burckhart, A. Burgess, A. Buscarino, A. Busse, D. Butcher, G. Calabrò, L. Calacci, R. Calado, R. Canavan, B. Cannas, M. Cannon, M. Cappelli, S. Carcangiu, P. Card, A. Cardinali, S. Carli, P. Carman, D. Carnevale, B. Carvalho, I.S. Carvalho, P. Carvalho, I. Casiraghi, F.J. Casson, C. Castaldo, J.P. Catalan, N. Catarino, F. Causa, M. Cavedon, M. Cecconello, L. Ceelen, C.D. Challis, B. Chamberlain, R. Chandra, C.S. Chang, A. Chankin, B. Chapman, P. Chauhan, M. Chernyshova, A. Chiariello, G.-C. Chira, P. Chmielewski, A. Chomiczewska, L. Chone, J. Cieslik, G. Ciraolo, D. Ciric, J. Citrin, Ł. Ciupinski, R. Clarkson, M. Cleverly, P. Coates, V. Coccorese, R. Coelho, J.W. Coenen, I.H. Coffey, A. Colangeli, L. Colas, J. Collins, S. Conroy, C. Contré, N.J. Conway, D. Coombs, P. Cooper, S. Cooper, L. Cordaro, C. Corradino, Y. Corre, G. Corrigan, D. Coster, T. Craciunescu, S. Cramp, D. Craven, R. Craven, G. Croci, D. Croft, K. Crombé, T. Cronin, N. Cruz, A. Cufar, A. Cullen, A. Dal Molin, S. Dalley, P. David, A. Davies, J. Davies, S. Davies, G. Davis, K. Dawson, S. Dawson, I. Day, G. De Tommasi, J. Deane, M. Dearing, M. De Bock, J. Decker, R. Dejarnac, E. Delabie, E. de la Cal, E. de la Luna, D. Del Sarto, A. Dempsey, W. Deng, A. Dennett, G.L. Derks, G. De Temmerman, F. Devasagayam, P. de Vries, P. Devynck, A. di Siena, D. Dickinson, T. Dickson, M. Diez, P. Dinca, T. Dittmar, L. Dittrich, J. Dobrashian, T. Dochnal, A.J.H. Donné, W. Dorland, S. Dorling, S. Dormido-Canto, R. Dotse, D. Douai, S. Dowson, R. Doyle, M. Dreval, P. Drews, G. Drummond, Ph. Duckworth, H.G. Dudding, R. Dumont, P. Dumortier, D. Dunai, T. Dunatov, M. Dunne, I. Ďuran, F. Durodié, R. Dux, T. Eade, E. Eardley, J. Edwards, T. Eich, A. Eksaeva, H. El-Haroun, R.D. Ellis, G. Ellwood, C. Elsmore, S. Emery, G. Ericsson, B. Eriksson, F. Eriksson, J. Eriksson, L.G. Eriksson, S. Ertmer, G. Evans, S. Evans, E. Fable, D. Fagan, M. Faitsch, D. Fajardo Jimenez, M. Falessi, A. Fanni, T. Farmer, I. Farquhar, B. Faugeras, S. Fazinić, N. Fedorczak, K. Felker, R. Felton, H. Fernandes, D.R. Ferreira, J. Ferreira, G. Ferrò, J. Fessey, O. Février, O. Ficker, A.R. Field, A. Figueiredo, J. Figueiredo, A. Fil, N. Fil, P. Finburg, U. Fischer, G. Fishpool, L. Fittill, M. Fitzgerald, D. Flammini, J. Flanagan, S. Foley, N. Fonnesu, M. Fontana, J.M. Fontdecaba, L. Fortuna, E. Fortuna-Zalesna, M. Fortune, C. Fowler, P. Fox, O. Franklin, E. Fransson, L. Frassinetti, R. Fresa, D. Frigione, T. Fülöp, M. Furseman, S. Gabriellini, D. Gadariya, S. Gadgil, K. Gál, S. Galeani, A. Galkowski, D. Gallart, M. Gambrioli, T. Gans, J. Garcia, M. García-Muñoz, L. Garzotti, J. Gaspar, R. Gatto, P. Gaudio, D. Gear, T. Gebhart, S. Gee, M. Gelfusa, R. George, S.N. Gerasimov, R. Gerru, G. Gervasini, M. Gethins, Z. Ghani, M. Gherendi, P.-I. Gherghina, F. Ghezzi, L. Giacomelli, C. Gibson, L. Gil, M.R. Gilbert, A. Gillgren, E. Giovannozzi, C. Giroud, G. Giruzzi, J. Goff, V. Goloborodko, R. Gomes, J.-F. Gomez, B. Gonçalves, M. Goniche, J. Gonzalez-Martin, A. Goodyear, S. Gore, G. Gorini, T. Görler, N. Gotts, E. Gow, J.P. Graves, J. Green, H. Greuner, E. Grigore, F. Griph, W. Gromelski, M. Groth, C. Grove, R. Grove, N. Gupta, S. Hacquin, L. Hägg, A. Hakola, M. Halitovs, J. Hall, C.J. Ham, M. Hamed, M.R. Hardman, Y. Haresawa, G. Harrer, J.R. Harrison, D. Harting, D.R. Hatch, T. Haupt, J. Hawes, N.C. Hawkes, J. Hawkins, S. Hazael, J. Hearmon, P. Heesterman, P. Heinrich, M. Held, W. Helou, O. Hemming, S.S. Henderson, R. Henriques, R.B. Henriques, D. Hepple, J. Herfindal, G. Hermon, J.C. Hillesheim, K. Hizanidis, A. Hjalmarsson, A. Ho, J. Hobirk, O. Hoenen, C. Hogben, A. Hollingsworth, S. Hollis, E. Hollmann, M. Hölzl, M. Hook, M. Hoppe, J. Horáček, N. Horsten, A. Horton, L.D. Horton, L. Horvath, S. Hotchin, Z. Hu, Z. Huang, E. Hubenov, A. Huber, V. Huber, T. Huddleston, G.T.A. Huijsmans, Y. Husain, P. Huynh, A. Hynes, D. Iglesias, M.V. Iliasova, M. Imríšek, J. Ingleby, P. Innocente, V. Ioannou-Sougleridis, N. Isernia, I. Ivanova-Stanik, E. Ivings, S. Jachmich, T. Jackson, A.S. Jacobsen, P. Jacquet, H. Järleblad, A. Järvinen, F. Jaulmes, N. Jayasekera, F. Jenko, I. Jepu, E. Joffrin, T. Johnson, J. Johnston, C. Jones, E. Jones, G. Jones, L. Jones, T.T.C. Jones, A. Joyce, M. Juvonen, A. Kallenbach, P. Kalnina, D. Kalupin, P. Kanth, A. Kantor, A. Kappatou, O. Kardaun, J. Karhunen, E. Karsakos, Ye.O. Kazakov, V. Kazantzidis, D.L. Keeling, W. Kelly, M. Kempenaars, D. Kennedy, K. Khan, E. Khilkevich, C. Kiefer, H.-T. Kim, J. Kim, S.H. Kim, D.B. King, D.J. Kinna, V.G. Kiptily, A. Kirjasuo, K.K. Kirov, A. Kirschner, T. Kiviniemi, G. Kizane, C. Klepper, A. Klix, G. Kneale, M. Knight, P. Knight, R. Knights, S. Knipe, U. Knoche, M. Knolker, M. Kocan, F. Köchl, G. Kocsis, J.T.W. Koenders, Y. Kolesnichenko, Y. Kominis, M. Kong, B. Kool, V. Korovin, S.B. Korsholm, B. Kos, D. Kos, M. Koubiti, Y. Kovtun, E. Kowalska-Strzęciwilk, K. Koziol, Y. Krasikov, A. Krasilnikov, V. Krasilnikov, M. Kresina, A. Kreter, K. Krieger, A. Krivska, U. Kruezi, I. Książek, H. Kumpulainen, B. Kurzan, S. Kwak, O.J. Kwon, B. Labit, M. Lacquaniti, A. Lagoyannis, L. Laguardia, A. Laing, V. Laksharam, N. Lam, H.T. Lambertz, B. Lane, M. Langley, E. Lascas Neto, E. Łaszyńska, K.D. Lawson, A. Lazaros, E. Lazzaro, G. Learoyd, C. Lee, K. Lee, S. Leerink, T. Leeson, X. Lefebvre, H.J. Leggate, J. Lehmann, M. Lehnen, D. Leichtle, F. Leipold, I. Lengar, M. Lennholm, E. Leon Gutierrez, L.A. Leppin, E. Lerche, A. Lescinskis, S. Lesnoj, L. Lewin, J. Lewis, J. Likonen, Ch. Linsmeier, X. Litaudon, E. Litherland-Smith, F. Liu, T. Loarer, A. Loarte, R. Lobel, B. Lomanowski, P.J. Lomas, J. Lombardo, R. Lorenzini, S. Loreti, V.P. Loschiavo, M. Loughlin, T. Lowe, C. Lowry, T. Luce, R. Lucock, T. Luda Di Cortemiglia, M. Lungaroni, C.P. Lungu, T. Lunt, V. Lutsenko, B. Lyons, J. Macdonald, E. Macusova, R. Mäenpää, H. Maier, J. Mailloux, S. Makarov, P. Manas, A. Manning, P. Mantica, M.J. Mantsinen, J. Manyer, A. Manzanares, Ph. Maquet, M. Maraschek, G. Marceca, G. Marcer, C. Marchetto, O. Marchuk, A. Mariani, G. Mariano, M. Marin, A. Marin Roldan, M. Marinelli, T. Markovič, L. Marot, C. Marren, S. Marsden, S. Marsen, J. Marsh, R. Marshall, L. Martellucci, A.J. Martin, C. Martin, R. Martone, S. Maruyama, M. Maslov, M. Mattei, G.F. Matthews, D. Matveev, E. Matveeva, A. Mauriya, F. Maviglia, M. Mayer, M.-L. Mayoral, S. Mazzi, C. Mazzotta, R. McAdams, P.J. McCarthy, P. McCullen, R. McDermott, D.C. McDonald, D. McGuckin, V. McKay, L. McNamee, A. McShee, D. Mederick, M. Medland, S. Medley, K. Meghani, A.G. Meigs, S. Meitner, S. Menmuir, K. Mergia, S. Mianowski, P. Middleton, J. Mietelski, K. Mikszuta-Michalik, D. Milanesio, E. Milani, E. Militello-Asp, F. Militello, J. Milnes, A. Milocco, S. Minucci, I. Miron, J. Mitchell, J. Mlynář, V. Moiseenko, P. Monaghan, I. Monakhov, A. Montisci, S. Moon, R. Mooney, S. Moradi, R.B. Morales, L. Morgan, F. Moro, J. Morris, T. Mrowetz, L. Msero, S. Munot, A. Muñoz-Perez, M. Muraglia, A. Murari, A. Muraro, B. N’Konga, Y.S. Na, F. Nabais, R. Naish, F. Napoli, E. Nardon, V. Naulin, M.F.F. Nave, R. Neu, S. Ng, M. Nicassio, D. Nicolai, A.H. Nielsen, S.K. Nielsen, D. Nina, C. Noble, C.R. Nobs, M. Nocente, H. Nordman, S. Nowak, H. Nyström, J. O’Callaghan, M. O’Mullane, C. O’Neill, C. Olde, H.J.C. Oliver, R. Olney, J. Ongena, G.P. Orsitto, A. Osipov, R. Otin, N. Pace, L.W. Packer, E. Pajuste, D. Palade, J. Palgrave, O. Pan, N. Panadero, T. Pandya, E. Panontin, A. Papadopoulos, G. Papadopoulos, G. Papp, V.V. Parail, A. Parsloe, K. Paschalidis, M. Passeri, A. Patel, A. Pau, G. Pautasso, R. Pavlichenko, A. Pavone, E. Pawelec, C. Paz-Soldan, A. Peacock, M. Pearce, I.J. Pearson, E. Peluso, C. Penot, K. Pepperell, A. Perdas, T. Pereira, E. Perelli Cippo, C. Perez von Thun, D. Perry, P. Petersson, G. Petravich, N. Petrella, M. Peyman, L. Pigatto, M. Pillon, S. Pinches, G. Pintsuk, C. Piron, A. Pironti, F. Pisano, R. Pitts, U. Planck, N. Platt, V. Plyusnin, M. Podesta, G. Pokol, F.M. Poli, O.G. Pompilian, M. Poradzinski, M. Porkolab, C. Porosnicu, G. Poulipoulis, A.S. Poulsen, I. Predebon, A. Previti, D. Primetzhofer, G. Provatas, G. Pucella, P. Puglia, K. Purahoo, O. Putignano, T. Pütterich, A. Quercia, G. Radulescu, V. Radulovic, R. Ragona, M. Rainford, P. Raj, M. Rasinski, D. Rasmussen, J. Rasmussen, J.J. Rasmussen, A. Raso, G. Rattá, S. Ratynskaia, R. Rayaprolu, M. Rebai, A. Redl, D. Rees, D. Réfy, R. Reichle, H. Reimerdes, B.C.G. Reman, C. Reux, S. Reynolds, D. Rigamonti, E. Righi, F.G. Rimini, J. Risner, J.F. Rivero-Rodriguez, C.M. Roach, J. Roberts, R. Robins, S. Robinson, D. Robson, S. Rode, P. Rodrigues, P. Rodriguez-Fernandez, S. Romanelli, J. Romazanov, E. Rose, C. Rose-Innes, R. Rossi, S. Rowe, D. Rowlands, C. Rowley, M. Rubel, G. Rubinacci, G. Rubino, M. Rud, J. Ruiz Ruiz, F. Ryter, S. Saarelma, A. Sahlberg, M. Salewski, A. Salmi, R. Salmon, F. Salzedas, F. Sanchez, I. Sanders, D. Sandiford, F. Sanni, O. Sauter, P. Sauvan, G. Schettini, A. Shevelev, A.A. Schekochihin, K. Schmid, B.S. Schmidt, S. Schmuck, M. Schneider, P.A. Schneider, N. Schoonheere, R. Schramm, D. Scoon, S. Scully, M. Segato, J. Seidl, L. Senni, J. Seo, G. Sergienko, M. Sertoli, S.E. Sharapov, R. Sharma, A. Shaw, R. Shaw, H. Sheikh, U. Sheikh, N. Shi, P. Shigin, D. Shiraki, G. Sias, M. Siccinio, B. Sieglin, S.A. Silburn, A. Silva, C. Silva, J. Silva, D. Silvagni, D. Simfukwe, J. Simpson, P. Sirén, A. Sirinelli, H. Sjöstrand, N. Skinner, J. Slater, T. Smart, R.D. Smirnov, N. Smith, P. Smith, T. Smith, J. Snell, L. Snoj, E.R. Solano, V. Solokha, C. Sommariva, K. Soni, M. Sos, J. Sousa, C. Sozzi, T. Spelzini, F. Spineanu, L. Spolladore, D. Spong, C. Srinivasan, G. Staebler, A. Stagni, I. Stamatelatos, M.F. Stamp, Ž. Štancar, P.A. Staniec, G. Stankūnas, M. Stead, B. Stein-Lubrano, A. Stephen, J. Stephens, P. Stevenson, C. Steventon, M. Stojanov, D.A. St-Onge, P. Strand, S. Strikwerda, C.I. Stuart, S. Sturgeon, H.J. Sun, S. Surendran, W. Suttrop, J. Svensson, J. Svoboda, R. Sweeney, G. Szepesi, M. Szoke, T. Tadić, B. Tal, T. Tala, P. Tamain, K. Tanaka, W. Tang, G. Tardini, M. Tardocchi, D. Taylor, A.S. Teimane, G. Telesca, A. Teplukhina, A. Terra, D. Terranova, N. Terranova, D. Testa, B. Thomas, V.K. Thompson, A. Thorman, A.S. Thrysoe, W. Tierens, R.A. Tinguely, A. Tipton, H. Todd, M. Tomeš, A. Tookey, P. Tsavalas, D. Tskhakaya, L.-P. Turică, A. Turner, I. Turner, M. Turner, M.M. Turner, G. Tvalashvili, A. Tykhyy, S. Tyrrell, A. Uccello, V. Udintsev, A. Vadgama, D.F. Valcarcel, A. Valentini, M. Valisa, M. Vallar, M. Valovic, M. Van Berkel, K.L. van de Plassche, M. van Rossem, D. Van Eester, J. Varela, J. Varje, T. Vasilopoulou, G. Vayakis, M. Vecsei, J. Vega, M. Veis, P. Veis, S. Ventre, M. Veranda, G. Verdoolaege, C. Verona, G. Verona Rinati, E. Veshchev, N. Vianello, E. Viezzer, L. Vignitchouk, R. Vila, R. Villari, F. Villone, P. Vincenzi, A. Vitins, Z. Vizvary, M. Vlad, I. Voldiner, U. Von Toussaint, P. Vondráček, B. Wakeling, M. Walker, R. Walker, M. Walsh, R. Walton, E. Wang, F. Warren, R. Warren, J. Waterhouse, C. Watts, T. Webster, M. Weiland, H. Weisen, M. Weiszflog, N. Wendler, A. West, M. Wheatley, S. Whetham, A. Whitehead, D. Whittaker, A. Widdowson, S. Wiesen, M. Willensdorfer, J. Williams, I. Wilson, T. Wilson, M. Wischmeier, A. Withycombe, D. Witts, A. Wojcik-Gargula, E. Wolfrum, R. Wood, R. Woodley, R. Worrall, I. Wyss, T. Xu, D. Yadykin, Y. Yakovenko, Y. Yang, V. Yanovskiy, R. Yi, I. Young, R. Young, B. Zaar, R.J. Zabolockis, L. Zakharov, P. Zanca, A. Zarins, D. Zarzoso Fernandez, K.-D. Zastrow, Y. Zayachuk, M. Zerbini, W. Zhang, B. Zimmermann, M. Zlobinski, A. Zocco, V.K. Zotta, M. Zuin, W. Zwingmann, and I. Zychor
Subjects: magnetic fusion, JET-ILW, D–T, tritium, alpha particles, fusion prediction, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract: In 2021 JET exploited its unique capabilities to operate with T and D–T fuel with an ITER-like Be/W wall (JET-ILW). This second major JET D–T campaign (DTE2), after DTE1 in 1997, represented the culmination of a series of JET enhancements—new fusion diagnostics, new T injection capabilities, refurbishment of the T plant, increased auxiliary heating, in-vessel calibration of 14 MeV neutron yield monitors—as well as significant advances in plasma theory and modelling in the fusion community. DTE2 was complemented by a sequence of isotope physics campaigns encompassing operation in pure tritium at high T-NBI power. Carefully conducted for safe operation with tritium, the new T and D–T experiments used 1 kg of T (vs 100 g in DTE1), yielding the most fusion reactor relevant D–T plasmas to date and expanding our understanding of isotopes and D–T mixture physics. Furthermore, since the JET T and DTE2 campaigns occurred almost 25 years after the last major D–T tokamak experiment, it was also a strategic goal of the European fusion programme to refresh operational experience of a nuclear tokamak to prepare staff for ITER operation. The key physics results of the JET T and DTE2 experiments, carried out within the EUROfusion JET1 work package, are reported in this paper. Progress in the technological exploitation of JET D–T operations, development and validation of nuclear codes, neutronic tools and techniques for ITER operations carried out by EUROfusion (started within the Horizon 2020 Framework Programme and continuing under the Horizon Europe FP) are reported in (Litaudon et al Nucl. Fusion accepted), while JET experience on T and D–T operations is presented in (King et al Nucl. Fusion submitted).
Published: 2024
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11. JET machine operations in T&D-T

Author: The JET Operations Team (presented by D.B. King), E. Abdelrahman, A. Abdul Hamid, N. Abid, K. Abraham, O. Adabonyan, C. Adlam, M. Afzal, M. Akhtar, V. Aldred, S. Aldworth, S. Aleiferis, M. Ali, R. Alie, R. Allan, H. Allen, E. Alli, M. Allinson, P. Almond, J. Angus, K. Antcliffe, I. Antoniou, L. Appel, C. Appelbee, C. Aramunde, N. Archer, S. Aria, H. Arkuszynski, M. Arshad, G. Artaserse, A. Ash, C. Ashe, T. Aue, D. Auld, B. Austin, Y. Austin, C. Ayres, R.B. Morales, S. Baker, S. Bakes, I. Balboa, C. Balshaw, N. Balshaw, J. Banks, J. Banner, A. Barnard, M. Barnard, M. Baruzzo, C. Basagiannis, S. Bathe Hariyanandan, P. Batistoni, R. Baughan, P. Beaumont, D. Beckett, A. Begolli, M. Beldishevski, K. Bell, E. Belonohy, J. Bentley, J. Bernardo, M. Berry, J. Bhatt, S. Bickerton, J. Bielecki, W. Bird, D. Blackett, K. Blackman, S. Blake, P. Blatchford, A. Bleasdale, A. Boboc, J. Booth, P. Boulting, M. Bowden, C. Boyd, K. Boyd, R. Bracey, D. Brennan, A. Brett, M. Bright, M. Brix, I. Brooks, B. Brown, P. Brown, M. Brown, P. Brummitt, B. Viola, A. Buckingham, M. Buckley, J. Bumpass, M. Burford, A. Burgess, J. Burton-Sweeten, A. Busse, D. Butcher, P. Cahill, P. Camp, I. Campbell, R. Canavan, J. Cane, M. Cannon, N. Canterbury, A. Carberry, P. Card, M. Carlick, M. Carlo, P. Carman, A. Carruthers, S. Carter, I.S. Carvalho, P. Carvalho, F. Casson, D. Chalk, B. Chamberlain, P. Chauhan, A. Chow, A. Churchman, D. Ciric, M. Clark, J. Clarkson, R. Clarkson, T. Clayton, M. Cleverly, P. Coates, I. Coffey, J. Collins, S. Conroy, N.J. Conway, R. Conway, J. Cook, M. Cooke, D. Coombs, P. Cooper, S. Cooper, G. Corrigan, R. Cotterell, A. Coulson, M. Cox, S. Cox, S. Cramp, D. Craven, R. Craven, M. Crick, D. Croft, T. Cronin, Z. Cui, A. Cullen, R. Cumming, C. Cummings, A. Dal Molin, P. Dalgliesh, S. Dalley, A. Danquah, S. Davies, G. Davis, H. Dawson, K. Dawson, S. Dawson, I. Day, L. de Caires, E. de la Luna, K. Deakin, J. Deane, M. Dearing, A. Dennett, T. Dickson, J. Dobrashian, T. Dochnal, S. Dorling, D. Douai, S. Dowson, J. Drewitt, G. Drummond, P. Dumortier, R. Eade, R. Eastham, K. Eden, J. Edmond, J. Edwards, P. Edwards, H. Elamin, S. Elford, H. El-Haroun, P. Ellis, C. Elsmore, S. Emery, G. Evans, S. Evans, D. Fagan, T. Farmer, I. Farquhar, R. Felton, F. Ferner, J. Fessey, P. Finburg, G. Fishpool, L. Fittill, J. Flanagan, K. Flinders, S. Foley, M. Fontana, M. Fortune, J. Foster, C. Fowler, P. Fox, O. Franklin, R. Franklin, R. Fraser, S. French, M. Furseman, A. Gabbidon, L. Garcia, J. Garcia, M. Gardener, D. Gear, T. Gedling, S. Gee, P. Gell, R. George, S. Gerasimov, M. Gethins, Z. Ghani, L. Giacomelli, C. Gibson, V. Gilsenan, C. Giroud, R. Glen, J. Goff, C. Goodman, A. Goodyear, A. Gordon, S. Gore, S. Gosden, N. Gotts, E. Gow, W. Graham, G. Graham, M. Green, R. Gregory, R. Griffiths, T. Griffiths, F. Griph, C. Grundy, T. Grundy, D. Guard, D. Guest, C. Gurl, S. Hacquin, A. Hakola, K. Hammond, H. Harmer, P. Harper, S. Harris, D. Hart, D. Hattan, A. Haupt, J. Hawes, N. Hawkes, J. Hawkins, P. Hawkins, S. Hayes, S. Hazael, D. Heads, P. Heesterman, O. Hemming, R.B. Henriques, G. Hermon, G. Hewson, T. Hibberd, M. Hill, J. Hillesheim, I. Hirb, K. Ho, C. Hogben, A. Hollingsworth, S. Hollis, M. Hook, D. Hopley, N. Horsten, A. Horton, L.D. Horton, L. Horvath, S. Hotchin, Z. Huang, E. Hubenov, V. Huber, A. Huber, C. Huddart, T. Huddleston, T. Hunter, Y. Husain, A. Hynes, J. Ingleby, S. Ives, E. Ivings, S. Jackson, T. Jackson, P. Jacquet, N. Jayasekera, I. Jepu, D. Jezzard, E. Joffrin, R. Johnson, J. Johnston, C. Jones, E. Jones, G. Jones, L. Jones, S. Jones, T. Jones, M. Jones, A. Joyce, M. Juvonen, A. Kantor, A. Kappatou, G. Karajgikar, J. Karhunen, I. Karnowska-Paterski, E. Karsakos, G. Kaveney, G. Kay, D. Keeling, T. Keenan, R. Kelly, W. Kelly, D. Kennedy, R. Kennedy, O. Kent, K. Khan, D. King, D. Kinna, V. Kiptily, K. Kirov, G. Kneale, M. Knight, P. Knight, J. Knipe, R. Knipe, S. Knipe, P. Kochanski, D. Kos, M. Kovari, E. Kowalska-Strzęciwilk, N. Kraus, M. Kresina, B. Labit, A. Laing, V. Laksharam, N. Lam, B. Lane, C. Lane, T. Lavender, A. Lawson, K. Lawson, G. Learoyd, T. Leeson, X. Lefebvre, J. Lehmann, M. Lennholm, K. Lennon, E. Lerche, S. Lesnoj, E. Letellier, L. Lewin, J. Lewis, J. Li, G. Liddiard, E. Litherland-Smith, F. Liu, R. Lobel, J. Logan, P. Lomas, C. Long, U. Losada, C. Loveridge, T. Lowe, C. Lowry, R. Lucock, G. Lyons, J. Macdonald, P. Macheta, T. Madden, J. Maddock, C.F. Maggi, J. Mailloux, A. Manning, C. Manning, N. Mantel, A. Manzanares, S. Marsden, J. Marsh, R. Marshall, A. Martin, M. Maslov, G. Matthews, N. Mayfield, M. Mayoral, R. McAdams, L. McCafferty, P. McCullen, D. McDonald, A. McDonnell, D. McGuckin, T. McIver, V. McKay, R. McKean, L. McNamee, A. McShee, R. Meadows, D. Mederick, M. Medland, K. Meghani, A. Meigs, S. Menmuir, I. Merrigan, S. Mianowski, P. Middleton, C. Miles, J. Milnes, A. Milocco, J. Mitchell, P. Mitchell, P. Monaghan, I. Monakhov, P. Moody, R. Mooney, C. Moore, N. Mooring, L. Morgan, R. Morgan, J. Morris, O. Morton, S. Morton, P. Mulvana, S. Munot, R. Munro-Smith, K. Musgrave, R. Naish, N. Neethiraj, J. Neilson, A. Newman, S. Ng, M. Nicassio, K. Nicholls, M. Nightingale, C. Noble, R. Normington, C. Nygaard, J. O’Callaghan, R. Olney, B. O’Meara, M. O’Mullane, C. O’Neill, C. Opara, K. O’Rourke, J. Ottley, K. Otu, A. Owen, N. Pace, K. Palamartchouk, D. Paley, J. Palgrave, G. Papadopoulos, V. Parail, A. Parrott, A. Parsloe, L. Parsons, R. Parsons, A. Patel, J. Patel, A. Peacock, M. Pearce, T. Pearce, I. Pearson, J. Penzo, A. Perdas, T. Pereira, C. Perez Von Thun, D. Perry, N. Petrella, M. Peyman, N. Platt, M. Poradzinski, M. Porter, M. Porton, C. Powell, J. Pozzi, M. Price, L. Price, P. Puglia, D. Pulley, K. Purahoo, M. Rainford, A. Raj, S. Randhawa, S. Rapa, K. Ravisankar, C. Rayner, A. Read, C. Reux, S. Reynolds, V. Riccardo, L. Richiusa, D. Rigamonti, F. Rimini, J. Roberts, R. Robins, S. Robinson, T. Robinson, D. Robson, S. Romanelli, F. Rose, C. Rose-Innes, D. Rouse, S. Rowe, N. Rowland, D. Rowlands, M. Rubel, K. Sabin, R. Salmon, H. Salter, A. Sanders, E. Sanders, I. Sanders, D. Sandiford, F. Sanni, R. Sarwar, R. Sayles, C. Scaysbrook, G. Scott, D. Scraggs, S. Scully, R. Sealey, E. Searle, M. Segato, M. Sertoli, C. Shanks, R. Sharma, A. Shaw, K. Sheahan, H. Sheikh, D. Shrestha, R. Siddiqui, S. Silburn, J. Silva, D. Simfukwe, J. Simpson, M. Sinclair, A. Sips, P. Sirén, S. Skeats, N. Skinner, B. Slade, J. Slater, T. Smart, G. Smith, J. Smith, N. Smith, P. Smith, T. Smith, F. P. Smith, J. Snell, K. Snelling, K. Soare, E. Solano, A. Spelzini, C. Srinivasan, Z. Stancar, P.A. Staniec, M. Stead, R. Steadman, L. Steel, D. Steele, A. Stephen, J. Stephens, L. Stevenson, P. Stevenson, C. Steventon, L. Sticklen, M. Stojanov, S. Strikwerda, C. Stuart, G. Stubbs, N. Studd, W. Studholme, H. Sun, S. Surendran, G. Szepesi, M. Szoke, H. Tan, A. Taylor, D. Taylor, K. Taylor, A. Thingore, B. Thomas, J. Thomas, A. Thorman, A. Tilley, A. Tipton, N. Tipton, H. Todd, P. Tonner, A. Tookey, M. Towndrow, M. Tsang, E. Tsitrone, I. Turner, M. Turner, G. Tvalashvili, S. Tyrrell, A. Vadgama, D. Valcarcel, Q. Van Der Westhuizen, J. Verdon, N. Vianello, A. Vittal, Z. Vizvary, B. Wakeling, M. Walker, R. Walker, T. Wall, M. Walsh, T. Walsh, J. Walters, J. Walton, R. Walton, S. Warder, F. Warren, R. Warren, J. Waterhouse, T. Webster, G. Wells, C. Wellstood, A. West, M. Wheatley, S. Whiffin, A. Whitehead, C. Whitehead, D. Whittaker, A. Widdowson, J. Wilcox, D. Wilkins, R. Wilkins, J. Williams, M. Williams, D. Willoughby, A. Wilson, I. Wilson, T. Wilson, M. Wischmeier, P. Wise, G. Withenshaw, A. Withycombe, D. Witts, J. Witts, R. Wood, L. Woodham, C. Woodley, J. Woodley, R. Woodley, B. Woods, S. Wray, T. Xu, I. Young, R. Young, K-D. Zastrow, Y. Zayachuk, and M. Zerbini
Subjects: JET, tritium, operations, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract: JET, the world’s largest operating tokamak with unique Be/W wall and tritium handling capability, completed a Deuterium-Tritium (D-T) campaign in 2021 (Maggi et al 29th Fusion Energy Conf. ) following a decade of preparatory experiments, dedicated enhancements, technical rehearsals and training (Horton et al 2016 Fusion Eng. Des. 109–111 925). Operation with tritium raises significant technical, safety and scientific challenges not encountered in standard protium or deuterium operation. This contribution describes the tritium operational requirements, pulses and technical preparations, new operating procedures, lessons learned and details on the achieved operational availability and performance. The preparation and execution of the recent JET tritium experiments benefitted from the previous experience in 1991 (Preliminary Tritium Experiment), 1997 (DTE1 campaign) and 2003 (Trace Tritium Campaigns) and consisted of the following five phases: technical rehearsals and scenario preparation, tritium commissioning, 100% tritium campaign, D-T campaign (DTE2), tritium clean-up. Following the clean-up JET resumed normal operation and is currently undertaking a further D-T campaign (DTE3).
Published: 2024
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12. A measurement of the K + → π + μ + μ − decay

Author: The NA62 collaboration, E. Cortina Gil, A. Kleimenova, E. Minucci, S. Padolski, P. Petrov, A. Shaikhiev, R. Volpe, T. Numao, Y. Petrov, B. Velghe, V. W. S. Wong, D. Bryman, J. Fu, T. Husek, J. Jerhot, K. Kampf, M. Zamkovsky, R. Aliberti, G. Khoriauli, J. Kunze, D. Lomidze, L. Peruzzo, M. Vormstein, R. Wanke, P. Dalpiaz, M. Fiorini, I. Neri, A. Norton, F. Petrucci, H. Wahl, A. Cotta Ramusino, A. Gianoli, E. Iacopini, G. Latino, M. Lenti, A. Parenti, A. Bizzeti, F. Bucci, A. Antonelli, G. Georgiev, V. Kozhuharov, G. Lanfranchi, S. Martellotti, M. Moulson, T. Spadaro, G. Tinti, F. Ambrosino, T. Capussela, M. Corvino, D. Di Filippo, R. Fiorenza, P. Massarotti, M. Mirra, M. Napolitano, G. Saracino, G. Anzivino, F. Brizioli, E. Imbergamo, R. Lollini, R. Piandani, C. Santoni, M. Barbanera, P. Cenci, B. Checcucci, P. Lubrano, M. Lupi, M. Pepe, M. Piccini, F. Costantini, L. Di Lella, N. Doble, M. Giorgi, S. Giudici, G. Lamanna, E. Lari, E. Pedreschi, M. Sozzi, C. Cerri, R. Fantechi, L. Pontisso, F. Spinella, I. Mannelli, G. D’Agostini, M. Raggi, A. Biagioni, P. Cretaro, O. Frezza, E. Leonardi, A. Lonardo, M. Turisini, P. Valente, P. Vicini, R. Ammendola, V. Bonaiuto, A. Fucci, A. Salamon, F. Sargeni, R. Arcidiacono, B. Bloch-Devaux, M. Boretto, E. Menichetti, E. Migliore, D. Soldi, C. Biino, A. Filippi, F. Marchetto, J. Engelfried, N. Estrada-Tristan, A. M. Bragadireanu, S. A. Ghinescu, O. E. Hutanu, A. Baeva, D. Baigarashev, D. Emelyanov, T. Enik, V. Falaleev, V. Kekelidze, A. Korotkova, L. Litov, D. Madigozhin, M. Misheva, N. Molokanova, S. Movchan, I. Polenkevich, Yu. Potrebenikov, S. Shkarovskiy, A. Zinchenko, S. Fedotov, E. Gushchin, A. Khotyantsev, Y. Kudenko, V. Kurochka, M. Medvedeva, A. Mefodev, S. Kholodenko, V. Kurshetsov, V. Obraztsov, A. Ostankov, V. Semenov, V. Sugonyaev, O. Yushchenko, L. Bician, T. Blazek, V. Cerny, Z. Kucerova, J. Bernhard, A. Ceccucci, H. Danielsson, N. De Simone, F. Duval, B. Döbrich, L. Federici, E. Gamberini, L. Gatignon, R. Guida, F. Hahn, E. B. Holzer, B. Jenninger, M. Koval, P. Laycock, G. Lehmann Miotto, P. Lichard, A. Mapelli, R. Marchevski, K. Massri, M. Noy, V. Palladino, M. Perrin-Terrin, J. Pinzino, V. Ryjov, S. Schuchmann, S. Venditti, T. Bache, M. B. Brunetti, V. Duk, V. Fascianelli, J. R. Fry, F. Gonnella, E. Goudzovski, J. Henshaw, L. Iacobuzio, C. Lazzeroni, N. Lurkin, F. Newson, C. Parkinson, A. Romano, A. Sergi, A. Sturgess, J. Swallow, A. Tomczak, H. Heath, R. Page, S. Trilov, B. Angelucci, D. Britton, C. Graham, D. Protopopescu, J. Carmignani, J. B. Dainton, R. W. L. Jones, G. Ruggiero, L. Fulton, D. Hutchcroft, E. Maurice, B. Wrona, A. Conovaloff, P. Cooper, D. Coward, and P. Rubin
Subjects: Branching fraction, Fixed Target Experiments, Flavour Changing Neutral Currents, Rare Decay, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract: Abstract A sample of 2.8 × 104 K + → π + μ + μ − candidates with negligible background was collected by the NA62 experiment at the CERN SPS in 2017–2018. The model-independent branching fraction is measured to be (9.15 ± 0.08) × 10 −8, a factor three more precise than previous measurements. The decay form factor is presented as a function of the squared dimuon mass. A measurement of the form factor parameters and their uncertainties is performed using a description based on Chiral Perturbation Theory at O $$ \mathcal{O} $$ (p 6).
Published: 2022
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13. A search for the K+ → μ−νe+e+ decay

Author: E. Cortina Gil, A. Kleimenova, E. Minucci, S. Padolski, P. Petrov, A. Shaikhiev, R. Volpe, T. Numao, Y. Petrov, B. Velghe, V.W.S. Wong, D. Bryman, J. Fu, T. Husek, J. Jerhot, K. Kampf, M. Zamkovsky, R. Aliberti, G. Khoriauli, J. Kunze, D. Lomidze, L. Peruzzo, M. Vormstein, R. Wanke, P. Dalpiaz, M. Fiorini, I. Neri, A. Norton, F. Petrucci, H. Wahl, A. Cotta Ramusino, A. Gianoli, E. Iacopini, G. Latino, M. Lenti, A. Parenti, A. Bizzeti, F. Bucci, A. Antonelli, G. Georgiev, V. Kozhuharov, G. Lanfranchi, S. Martellotti, M. Moulson, T. Spadaro, G. Tinti, F. Ambrosino, T. Capussela, M. Corvino, D. Di Filippo, R. Fiorenza, P. Massarotti, M. Mirra, M. Napolitano, G. Saracino, G. Anzivino, F. Brizioli, E. Imbergamo, R. Lollini, R. Piandani, C. Santoni, M. Barbanera, P. Cenci, B. Checcucci, P. Lubrano, M. Lupi, M. Pepe, M. Piccini, F. Costantini, L. Di Lella, N. Doble, M. Giorgi, S. Giudici, G. Lamanna, E. Lari, E. Pedreschi, M. Sozzi, C. Cerri, R. Fantechi, L. Pontisso, F. Spinella, I. Mannelli, G. D'Agostini, M. Raggi, A. Biagioni, P. Cretaro, O. Frezza, E. Leonardi, A. Lonardo, M. Turisini, P. Valente, P. Vicini, R. Ammendola, V. Bonaiuto, A. Fucci, A. Salamon, F. Sargeni, R. Arcidiacono, B. Bloch-Devaux, M. Boretto, E. Menichetti, E. Migliore, D. Soldi, C. Biino, A. Filippi, F. Marchetto, J. Engelfried, N. Estrada-Tristan, A.M. Bragadireanu, S.A. Ghinescu, O.E. Hutanu, A. Baeva, D. Baigarashev, D. Emelyanov, T. Enik, V. Falaleev, V. Kekelidze, A. Korotkova, L. Litov, D. Madigozhin, M. Misheva, N. Molokanova, S. Movchan, I. Polenkevich, Yu. Potrebenikov, S. Shkarovskiy, A. Zinchenko, S. Fedotov, E. Gushchin, A. Khotyantsev, Y. Kudenko, V. Kurochka, M. Medvedeva, A. Mefodev, S. Kholodenko, V. Kurshetsov, V. Obraztsov, A. Ostankov, V. Semenov, V. Sugonyaev, O. Yushchenko, L. Bician, T. Blazek, V. Cerny, Z. Kucerova, J. Bernhard, A. Ceccucci, H. Danielsson, N. De Simone, F. Duval, B. Döbrich, L. Federici, E. Gamberini, L. Gatignon, R. Guida, F. Hahn, E.B. Holzer, B. Jenninger, M. Koval, P. Laycock, G. Lehmann Miotto, P. Lichard, A. Mapelli, R. Marchevski, K. Massri, M. Noy, V. Palladino, M. Perrin-Terrin, J. Pinzino, V. Ryjov, S. Schuchmann, S. Venditti, T. Bache, M.B. Brunetti, V. Duk, V. Fascianelli, J.R. Fry, F. Gonnella, E. Goudzovski, J. Henshaw, L. Iacobuzio, C. Lazzeroni, N. Lurkin, F. Newson, C. Parkinson, A. Romano, A. Sergi, A. Sturgess, J. Swallow, A. Tomczak, H. Heath, R. Page, S. Trilov, B. Angelucci, D. Britton, C. Graham, D. Protopopescu, J. Carmignani, J.B. Dainton, R.W.L. Jones, G. Ruggiero, L. Fulton, D. Hutchcroft, E. Maurice, B. Wrona, A. Conovaloff, P. Cooper, D. Coward, and P. Rubin
Subjects: Kaon decays, Lepton number violation, Lepton flavour violation, Physics, QC1-999
Abstract: A search for the K+→μ−νe+e+ decay, forbidden within the Standard Model by either lepton number or lepton flavour conservation depending on the flavour of the emitted neutrino, has been performed using the dataset collected by the NA62 experiment at CERN in 2016–2018. An upper limit of 8.1×10−11 is obtained for the decay branching fraction at 90% CL, improving by a factor of 250 over the previous search.
Published: 2023
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14. Noncredit Workforce Training, Industry Credentials, and Labor Market Outcomes. EdWorkingPaper No. 24-959

Author: Annenberg Institute for School Reform at Brown University, Di Xu, Kelli A. Bird, Michael Cooper, and Benjamin L. Castleman
Abstract: Many public workforce training programs lead to industry-recognized, third-party awarded credentials, but little research has been conducted on the economic benefits of these credentials in the labor market. This paper provides quasi-experimental evidence on the labor market returns to industry-recognized credentials connected to community college workforce noncredit training programs. Based on novel data that includes approximately 24,000 working-age adults enrolled in noncredit workforce training programs at the Virginia Community College System, we employ a comparative individual-level fixed effects model to estimate earnings premia net of fixed attributes and earnings time-trends. Our results indicate that earning an industry-recognized credential, on average, increases quarterly earnings by approximately $1,000 and the probability of being employed by 2.4 percentage points, although there is substantial heterogeneity in economic return across different program fields. Back-of-the-envelope calculations suggest that the earnings gains associated with the industry credential obtained through the noncredit workforce training would exceed program costs in just over half a year on average.
Published: 2024

15. MAGAZ3NE: Evidence for Galactic Conformity in $z\gtrsim3$ Protoclusters

Author: McConachie, Ian, Wilson, Gillian, Forrest, Ben, Marsan, Z. Cemile, Muzzin, Adam, Cooper, M. C., Annunziatella, Marianna, Marchesini, Danilo, Gomez, Percy, Chang, Wenjun, Stawinski, Stephanie M. Urbano, McDonald, Michael, Webb, Tracy, Noble, Allison, Lemaux, Brian C., Shah, Ekta A., Staab, Priti, Lubin, Lori M., and Gal, Roy R.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: We examine the quiescent fractions of massive galaxies in six $z\gtrsim3$ spectroscopically-confirmed protoclusters in the COSMOS field, one of which is newly confirmed and presented here. We report the spectroscopic confirmation of MAGAZ3NE~J100143+023021 at $z=3.122^{+0.007}_{-0.004}$ by the Massive Ancient Galaxies At $z>3$ NEar-infrared (MAGAZ3NE) survey. MAGAZ3NE~J100143+023021 contains a total of 79 protocluster members (28 spectroscopic and 51 photometric). Three spectroscopically-confirmed members are star-forming ultra-massive galaxies ($\log(M_{\star}/{\rm M}_\odot)>11$; UMGs), the most massive of which has $\log(M_{\star}/{\rm M}_\odot)=11.15^{+0.05}_{-0.06}$. Combining Keck/MOSFIRE spectroscopy and the COSMOS2020 photometric catalog, we use a weighted Gaussian kernel density estimator to map the protocluster and measure its total mass $2.25^{+1.55}_{-0.65}\times10^{14}~{\rm M}_{\odot}$ in the dense ``core'' region. For each of the six COSMOS protoclusters, we compare the quiescent fraction to the status of the central UMG as star-forming or quiescent. We observe that galaxies in these protoclusters appear to obey galactic conformity: elevated quiescent fractions are found in protoclusters with $UVJ$ quiescent UMGs and low quiescent fractions are found in protoclusters containing $UVJ$ star-forming UMGs. This correlation of star-formation/quiescence in UMGs and the massive galaxies nearby in these protoclusters is the first evidence for the existence of galactic conformity at $z>3$. Despite disagreements over mechanisms behind conformity at low redshifts, its presence at these early cosmic times would provide strong constraints on the physics proposed to drive galactic conformity., Comment: 6 figures, 5 tables. Accepted for publication in the Astrophysical Journal
Published: 2024

16. Machine learning interatomic potential for modeling uranium mononitride

Author: Alzate-Vargas, Lorena, Subedi, Kashi N., Lubbers, Nicholas, Cooper, Michael W. D, Tutchton, Roxanne M., Gibson, Tammie, and Messerly, Richard A.
Subjects: Condensed Matter - Materials Science
Abstract: Uranium mononitride (UN) is a promising accident tolerant fuel due to its high fissile density and high thermal conductivity. In this study, we developed the first machine learning interatomic potentials for reliable atomic-scale modeling of UN at finite temperatures. We constructed a training set using density functional theory (DFT) calculations that was enriched through an active learning procedure and two neural network potentials were generated. We found that both potentials can reproduce some thermophysical properties of interest such as temperature dependent heat capacity. We also evaluated the energy of stoichiometric defect reactions and defect migration barriers and found close agreement with DFT values demonstrating that our potentials can be used for a description of defects in UN.
Published: 2024

17. Does Unlearning Truly Unlearn? A Black Box Evaluation of LLM Unlearning Methods

Author: Doshi, Jai and Stickland, Asa Cooper
Subjects: Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract: Large language model unlearning aims to remove harmful information that LLMs have learnt to prevent their use for malicious purposes. LLMU and RMU have been proposed as two methods for LLM unlearning, achieving impressive results on unlearning benchmarks. We study in detail the efficacy of these methods by evaluating their impact on general model capabilities on the WMDP benchmark as well as a biology benchmark we create. Our experiments show that RMU generally leads to better preservation of model capabilities, for similar or better unlearning. We further test the robustness of these methods and find that doing 5-shot prompting or rephrasing the question in simple ways can lead to an over ten-fold increase in accuracy on unlearning benchmarks. Finally, we show that training on unrelated data can almost completely recover pre-unlearning performance, demonstrating that these methods fail at truly unlearning. The code is available at: https://github.com/JaiDoshi/Knowledge-Erasure., Comment: 9 pages, 2 figures
Published: 2024

18. Evaluating Generative AI Systems is a Social Science Measurement Challenge

Author: Wallach, Hanna, Desai, Meera, Pangakis, Nicholas, Cooper, A. Feder, Wang, Angelina, Barocas, Solon, Chouldechova, Alexandra, Atalla, Chad, Blodgett, Su Lin, Corvi, Emily, Dow, P. Alex, Garcia-Gathright, Jean, Olteanu, Alexandra, Reed, Stefanie, Sheng, Emily, Vann, Dan, Vaughan, Jennifer Wortman, Vogel, Matthew, Washington, Hannah, and Jacobs, Abigail Z.
Subjects: Computer Science - Computers and Society
Abstract: Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult. We argue that these measurement tasks are highly reminiscent of measurement tasks found throughout the social sciences. With this in mind, we present a framework, grounded in measurement theory from the social sciences, for measuring concepts related to the capabilities, impacts, opportunities, and risks of GenAI systems. The framework distinguishes between four levels: the background concept, the systematized concept, the measurement instrument(s), and the instance-level measurements themselves. This four-level approach differs from the way measurement is typically done in ML, where researchers and practitioners appear to jump straight from background concepts to measurement instruments, with little to no explicit systematization in between. As well as surfacing assumptions, thereby making it easier to understand exactly what the resulting measurements do and do not mean, this framework has two important implications for evaluating evaluations: First, it can enable stakeholders from different worlds to participate in conceptual debates, broadening the expertise involved in evaluating GenAI systems. Second, it brings rigor to operational debates by offering a set of lenses for interrogating the validity of measurement instruments and their resulting measurements., Comment: NeurIPS 2024 Workshop on Evaluating Evaluations (EvalEval)
Published: 2024

19. A dataset of questions on decision-theoretic reasoning in Newcomb-like problems

Author: Oesterheld, Caspar, Cooper, Emery, Kodama, Miles, Nguyen, Linh Chi, and Perez, Ethan
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, I.2.7
Abstract: We introduce a dataset of natural-language questions in the decision theory of so-called Newcomb-like problems. Newcomb-like problems include, for instance, decision problems in which an agent interacts with a similar other agent, and thus has to reason about the fact that the other agent will likely reason in similar ways. Evaluating LLM reasoning about Newcomb-like problems is important because interactions between foundation-model-based agents will often be Newcomb-like. Some ways of reasoning about Newcomb-like problems may allow for greater cooperation between models. Our dataset contains both capabilities questions (i.e., questions with a unique, uncontroversially correct answer) and attitude questions (i.e., questions about which decision theorists would disagree). We use our dataset for an investigation of decision-theoretical capabilities and expressed attitudes and their interplay in existing models (different models by OpenAI, Anthropic, Meta, GDM, Reka, etc.), as well as models under simple prompt-based interventions. We find, among other things, that attitudes vary significantly between existing models; that high capabilities are associated with attitudes more favorable toward so-called evidential decision theory; and that attitudes are consistent across different types of questions., Comment: 48 pages, 15 figures; code and data at https://github.com/casparoe/newcomblike_questions_dataset
Published: 2024

20. Deep Learning for Fetal Inflammatory Response Diagnosis in the Umbilical Cord

Author: Ayad, Marina A., Nateghi, Ramin, Sharma, Abhishek, Chillrud, Lawrence, Seesillapachai, Tilly, Cooper, Lee A. D., and Goldstein, Jeffery A.
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract: Inflammation of the umbilical cord can be seen as a result of ascending intrauterine infection or other inflammatory stimuli. Acute fetal inflammatory response (FIR) is characterized by infiltration of the umbilical cord by fetal neutrophils, and can be associated with neonatal sepsis or fetal inflammatory response syndrome. Recent advances in deep learning in digital pathology have demonstrated favorable performance across a wide range of clinical tasks, such as diagnosis and prognosis. In this study we classified FIR from whole slide images (WSI). We digitized 4100 histological slides of umbilical cord stained with hematoxylin and eosin(H&E) and extracted placental diagnoses from the electronic health record. We build models using attention-based whole slide learning models. We compared strategies between features extracted by a model (ConvNeXtXLarge) pretrained on non-medical images (ImageNet), and one pretrained using histopathology images (UNI). We trained multiple iterations of each model and combined them into an ensemble. The predictions from the ensemble of models trained using UNI achieved an overall balanced accuracy of 0.836 on the test dataset. In comparison, the ensembled predictions using ConvNeXtXLarge had a lower balanced accuracy of 0.7209. Heatmaps generated from top accuracy model appropriately highlighted arteritis in cases of FIR 2. In FIR 1, the highest performing model assigned high attention to areas of activated-appearing stroma in Wharton's Jelly. However, other high-performing models assigned attention to umbilical vessels. We developed models for diagnosis of FIR from placental histology images, helping reduce interobserver variability among pathologists. Future work may examine the utility of these models for identifying infants at risk of systemic inflammatory response or early onset neonatal sepsis.
Published: 2024

21. HETDEX-LOFAR Spectroscopic Redshift Catalog

Author: Debski, Maya H., Zeimann, Gregory R., Hill, Gary J., Schneider, Donald P., Morabito, Leah, Dalton, Gavin, Jarvis, Matt J., Cooper, Erin Mentuch, Ciardullo, Robin, Gawiser, Eric, and Jurlin, Nika
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: We combine the power of blind integral field spectroscopy from the Hobby-Eberly Telescope (HET) Dark Energy Experiment (HETDEX) with sources detected by the Low Frequency Array (LOFAR) to construct the HETDEX-LOFAR Spectroscopic Redshift Catalog. Starting from the first data release of the LOFAR Two-metre Sky Survey (LoTSS), including a value-added catalog with photometric redshifts, we extracted 28,705 HETDEX spectra. Using an automatic classifying algorithm, we assigned each object a star, galaxy, or quasar label along with a velocity/redshift, with supplemental classifications coming from the continuum and emission line catalogs of the internal, fourth data release from HETDEX (HDR4). We measured 9,087 new redshifts; in combination with the value-added catalog, our final spectroscopic redshift sample is 9,710 sources. This new catalog contains the highest substantial fraction of LOFAR galaxies with spectroscopic redshift information; it improves archival spectroscopic redshifts, and facilitates research to determine the [O II] emission properties of radio galaxies from $0.0 < z < 0.5$, and the Ly$\alpha$ emission characteristics of both radio galaxies and quasars from $1.9 < z < 3.5$. Additionally, by combining the unique properties of LOFAR and HETDEX, we are able to measure star formation rates (SFR) and stellar masses. Using the Visible Integral-field Replicable Unit Spectrograph (VIRUS), we measure the emission lines of [O III], [Ne III], and [O II] and evaluate line-ratio diagnostics to determine whether the emission from these galaxies is dominated by AGN or star formation and fit a new SFR-L$_{150MHz}$ relationship., Comment: 21 pages, 17 figures, submitted to ApJ
Published: 2024

22. Towards a criteria-based approach to selecting human-AI interaction mode

Author: Irons, Jessica, Cooper, Patrick, McGrath, Melanie, Tariq, Shahroz, and Duenser, Andreas
Subjects: Computer Science - Human-Computer Interaction
Abstract: Artificial intelligence (AI) tools are now prevalent in many knowledge work industries. As AI becomes more capable and interactive, there is a growing need for guidance on how to employ AI most effectively. The A2C framework (Tariq, Chhetri, Nepal & Paris, 2024) distinguishes three decision-making modes for engaging AI: automation (AI completes a task, including decision/action), augmentation (AI supports human to decide) and collaboration (iterative interaction between human and AI). However, selecting the appropriate mode for a specific application is not always straightforward. The goal of the present study was to compile and trial a simple set of criteria to support recommendations about appropriate A2C mode for a given application. Drawing on human factors and computer science literature, we identified key criteria related to elements of the task, impacts on worker and support needs. From these criteria we built a scoring rubric with recommendation for A2C mode. As a preliminary test of this approach, we applied the criteria to cognitive task analysis (CTA) outputs from three tasks in the science domain - genome annotation, biological collections curation and protein crystallization - which provided insights into worker decision points, challenges and expert strategies. This paper describes the method for connecting CTA to A2C, reflecting on the challenges and future directions., Comment: 27 pages
Published: 2024

23. Can CDT rationalise the ex ante optimal policy via modified anthropics?

Author: Cooper, Emery, Oesterheld, Caspar, and Conitzer, Vincent
Subjects: Computer Science - Artificial Intelligence, Computer Science - Computer Science and Game Theory
Abstract: In Newcomb's problem, causal decision theory (CDT) recommends two-boxing and thus comes apart from evidential decision theory (EDT) and ex ante policy optimisation (which prescribe one-boxing). However, in Newcomb's problem, you should perhaps believe that with some probability you are in a simulation run by the predictor to determine whether to put a million dollars into the opaque box. If so, then causal decision theory might recommend one-boxing in order to cause the predictor to fill the opaque box. In this paper, we study generalisations of this approach. That is, we consider general Newcomblike problems and try to form reasonable self-locating beliefs under which CDT's recommendations align with an EDT-like notion of ex ante policy optimisation. We consider approaches in which we model the world as running simulations of the agent, and an approach not based on such models (which we call 'Generalised Generalised Thirding', or GGT). For each approach, we characterise the resulting CDT policies, and prove that under certain conditions, these include the ex ante optimal policies.
Published: 2024

24. Vacuum Amplification of Chiral Gravitational Waves and the Stochastic Gravitational Wave Background

Author: Alexander, Stephon, Bernardo, Heliudson, Li, Yiya Selina, and Niu, Cooper
Subjects: General Relativity and Quantum Cosmology, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Phenomenology, High Energy Physics - Theory
Abstract: We investigate cosmological vacuum amplification of gravitational waves in dynamical Chern-Simons gravity. We develop a comprehensive framework to compute graviton production induced by the parity violating Pontryagin coupling and study its imprint on the stochastic gravitational wave background energy power spectrum. We explore gravitational vacuum amplification in four concrete scenarios for the evolution of the Chern-Simons pseudoscalar. We show that a parity-violating contribution dominates over an initially flat spectrum when the velocity of the pseudoscalar quickly interpolates between two asymptotically constant values or when it is nonvanishing and constant through a finite period of time. This is also the case when we parametrize the pseudoscalar evolution by a perfect fluid with radiation- and dust-like equations of state for large enough values of its energy density. The resulting spectra are compared with the sensitivity curves of current and future gravitational wave observational searches., Comment: 17 pages, 4 figures
Published: 2024

25. MOS-Bench: Benchmarking Generalization Abilities of Subjective Speech Quality Assessment Models

Author: Huang, Wen-Chin, Cooper, Erica, and Toda, Tomoki
Subjects: Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: Subjective speech quality assessment (SSQA) is critical for evaluating speech samples as perceived by human listeners. While model-based SSQA has enjoyed great success thanks to the development of deep neural networks (DNNs), generalization remains a key challenge, especially for unseen, out-of-domain data. To benchmark the generalization abilities of SSQA models, we present MOS-Bench, a diverse collection of datasets. In addition, we also introduce SHEET, an open-source toolkit containing complete recipes to conduct SSQA experiments. We provided benchmark results for MOS-Bench, and we also explored multi-dataset training to enhance generalization. Additionally, we proposed a new performance metric, best score difference/ratio, and used latent space visualizations to explain model behavior, offering valuable insights for future research., Comment: Submitted to Transactions on Audio, Speech and Language Processing. This work has been submitted to the IEEE for possible publication
Published: 2024

26. Characteristic Polynomials and Hypergraph Generating Functions via Heaps of Pieces

Author: Cooper, Joshua, Guo, Krystal, and Okur, Utku
Subjects: Mathematics - Combinatorics, 05C65 (Primary) 05C50, 05A15, 15A69 (Secondary)
Abstract: It is a classical result due to Jacobi in algebraic combinatorics that the generating function of closed walks at a vertex $u$ in a graph $G$ is determined by the rational function \[ \frac{\phi_{G-u}(t)}{\phi_G(t)} \] where $\phi_G(t)$ is the characteristic polynomial of $G$. In this paper, we show that the corresponding rational function for a hypergraph is also a generating function for some combinatorial objects in the hypergraph. We make use of the Heaps of Pieces framework, developed by Viennot, demonstrating its use on graphs, digraphs, and multigraphs before using it on hypergraphs. In the case of a graph $G$, the pieces are cycles and the concurrence relation is sharing a vertex. The pyramids with maximal piece containing a vertex $u \in V(G)$ are in one-to-one correspondence with closed walks at $u$. In the case of a hypergraph $\mathcal{H}$, connected "infragraphs" can be defined as the set of pieces, with the same concurrence relation: sharing a vertex. Our main results are established by analyzing multivariate resultants of polynomial systems associated to adjacency hypermatrices., Comment: 37 pages, 8 figures
Published: 2024

27. Data-driven model validation for neutrino-nucleus cross section measurements

Author: MicroBooNE collaboration, Abratenko, P., Alterkait, O., Aldana, D. Andrade, Arellano, L., Asaadi, J., Ashkenazi, A., Balasubramanian, S., Baller, B., Barnard, A., Barr, G., Barrow, D., Barrow, J., Basque, V., Bateman, J., Rodrigues, O. Benevides, Berkman, S., Bhanderi, A., Bhat, A., Bhattacharya, M., Bishai, M., Blake, A., Bogart, B., Bolton, T., Brunetti, M. B., Camilleri, L., Cao, Y., Caratelli, D., Cavanna, F., Cerati, G., Chappell, A., Chen, Y., Conrad, J. M., Convery, M., Cooper-Troendle, L., Crespo-Anadon, J. I., Cross, R., Del Tutto, M., Dennis, S. R., Detje, P., Diurba, R., Djurcic, Z., Duffy, K., Dytman, S., Eberly, B., Englezos, P., Ereditato, A., Evans, J. J., Fang, C., Fleming, B. T., Foreman, W., Franco, D., Furmanski, A. P., Gao, F., Garcia-Gamez, D., Gardiner, S., Ge, G., Gollapinni, S., Gramellini, E., Green, P., Greenlee, H., Gu, L., Gu, W., Guenette, R., Guzowski, P., Hagaman, L., Handley, M. D., Hen, O., Hilgenberg, C., Horton-Smith, G. A., Imani, Z., Irwin, B., Ismail, M. S., James, C., Ji, X., Jo, J. H., Johnson, R. A., Jwa, Y. J., Kalra, D., Karagiorgi, G., Ketchum, W., Kirby, M., Kobilarcik, T., Lane, N., Li, J. -Y., Li, Y., Lin, K., Littlejohn, B. R., Liu, L., Louis, W. C., Luo, X., Mahmud, T., Mariani, C., Marsden, D., Marshall, J., Martinez, N., Caicedo, D. A. Martinez, Martynenko, S., Mastbaum, A., Mawby, I., McConkey, N., Meddage, V., Mellet, L., Mendez, J., Micallef, J., Miller, K., Mistry, K., Mohayai, T., Mogan, A., Mooney, M., Moor, A. F., Moore, C. D., Lepin, L. Mora, Moudgalya, M. M., Babu, S. Mulleria, Naples, D., Navrer-Agasson, A., Nayak, N., Nebot-Guinot, M., Nguyen, C., Nowak, J., Oza, N., Palamara, O., Pallat, N., Paolone, V., Papadopoulou, A., Papavassiliou, V., Parkinson, H., Pate, S. F., Patel, N., Pavlovic, Z., Piasetzky, E., Pletcher, K., Pophale, I., Qian, X., Raaf, J. L., Radeka, V., Rafique, A., Reggiani-Guzzo, M., Ren, L., Rochester, L., Rondon, J. Rodriguez, Rosenberg, M., Ross-Lonergan, M., Safa, I., Schmitz, D. W., Schukraft, A., Seligman, W., Shaevitz, M. H., Sharankova, R., Shi, J., Snider, E. L., Soderberg, M., Soldner-Rembold, S., Spitz, J., Stancari, M., John, J. St., Strauss, T., Szelc, A. M., Taniuchi, N., Terao, K., Thorpe, C., Torbunov, D., Totani, D., Toups, M., Trettin, A., Tsai, Y. -T., Tyler, J., Uchida, M. A., Usher, T., Viren, B., Wang, J., Weber, M., Wei, H., White, A. J., Wolbers, S., Wongjirad, T., Wospakrik, M., Wresilo, K., Wu, W., Yandel, E., Yang, T., Yates, L. E., Yu, H. W., Zeller, G. P., Zennamo, J., and Zhang, C.
Subjects: High Energy Physics - Experiment
Abstract: Neutrino-nucleus cross section measurements are needed to improve interaction modeling to meet the precision needs of neutrino experiments in efforts to measure oscillation parameters and search for physics beyond the Standard Model. We review the difficulties associated with modeling neutrino-nucleus interactions that lead to a dependence on event generators in oscillation analyses and cross section measurements alike. We then describe data-driven model validation techniques intended to address this model dependence. The method relies on utilizing various goodness-of-fit tests and the correlations between different observables and channels to probe the model for defects in the phase space relevant for the desired analysis. These techniques shed light on relevant mis-modeling, allowing it to be detected before it begins to bias the cross section results. We compare more commonly used model validation methods which directly validate the model against alternative ones to these data-driven techniques and show their efficacy with fake data studies. These studies demonstrate that employing data-driven model validation in cross section measurements represents a reliable strategy to produce robust results that will stimulate the desired improvements to interaction modeling.
Published: 2024

28. Machine learning identification of maternal inflammatory response and histologic choroamnionitis from placental membrane whole slide images

Author: Sharma, Abhishek, Nateghi, Ramin, Ayad, Marina, Cooper, Lee A. D., and Goldstein, Jeffery A.
Subjects: Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Quantitative Methods
Abstract: The placenta forms a critical barrier to infection through pregnancy, labor and, delivery. Inflammatory processes in the placenta have short-term, and long-term consequences for offspring health. Digital pathology and machine learning can play an important role in understanding placental inflammation, and there have been very few investigations into methods for predicting and understanding Maternal Inflammatory Response (MIR). This work intends to investigate the potential of using machine learning to understand MIR based on whole slide images (WSI), and establish early benchmarks. To that end, we use Multiple Instance Learning framework with 3 feature extractors: ImageNet-based EfficientNet-v2s, and 2 histopathology foundation models, UNI and Phikon to investigate predictability of MIR stage from histopathology WSIs. We also interpret predictions from these models using the learned attention maps from these models. We also use the MIL framework for predicting white blood cells count (WBC) and maximum fever temperature ($T_{max}$). Attention-based MIL models are able to classify MIR with a balanced accuracy of up to 88.5% with a Cohen's Kappa ($\kappa$) of up to 0.772. Furthermore, we found that the pathology foundation models (UNI and Phikon) are both able to achieve higher performance with balanced accuracy and $\kappa$, compared to ImageNet-based feature extractor (EfficientNet-v2s). For WBC and $T_{max}$ prediction, we found mild correlation between actual values and those predicted from histopathology WSIs. We used MIL framework for predicting MIR stage from WSIs, and compared effectiveness of foundation models as feature extractors, with that of an ImageNet-based model. We further investigated model failure cases and found them to be either edge cases prone to interobserver variability, examples of pathologist's overreach, or mislabeled due to processing errors.
Published: 2024

29. Search for a Hidden Sector Scalar from Kaon Decay in the Di-Muon Final State at ICARUS

Author: ICARUS Collaboration, Alrahman, F. Abd, Abratenko, P., Abrego-Martinez, N., Aduszkiewicz, A., Akbar, F., Soplin, L. Aliaga, Garrote, R. Alvarez, Pons, M. Artero, Asaadi, J., Badgett, W. F., Baibussinov, B., Behera, B., Bellini, V., Benocci, R., Berger, J., Berkman, S., Bertolucci, S., Betancourt, M., Bonesini, M., Boone, T., Bottino, B., Braggiotti, A., Brailsford, D., Brice, S. J., Brio, V., Brizzolari, C., Budd, H. S., Campani, A., Campos, A., Carber, D., Carneiro, M., Terrazas, I. Caro, Carranza, H., Fernandez, F. Castillo, Castro, A., Centro, S., Cerati, G., Chatterjee, A., Cherdack, D., Cherubini, S., Chithirasreemadam, N., Cicerchia, M., Coan, T. E., Cocco, A., Convery, M. R., Cooper-Troendle, L., Copello, S., Da Motta, H., Dallolio, M., Dange, A. A., de Roeck, A., Di Domizio, S., Di Noto, L., Di Stefano, C., Di Ferdinando, D., Diwan, M., Dolan, S., Domine, L., Donati, S., Drielsma, F., Dyer, J., Dytman, S., Falcone, A., Farnese, C., Fava, A., Ferrari, A., Gallice, N., Garcia, F. G., Gatto, C., Gibin, D., Gioiosa, A., Gu, W., Guglielmi, A., Gurung, G., Hassinin, K., Hausner, H., Heggestuen, A., Howard, B., Howell, R., Ingratta, I., James, C., Jang, W., Jung, M., Jwa, Y. -J., Kashur, L., Ketchum, W., Kim, J. S., Koh, D. -H., Larkin, J., Li, Y., Mariani, C., Marshall, C. M., Martynenko, S., Mauri, N., McFarland, K. S., Mé9ndez, D. P., Menegolli, A., Meng, G., Miranda, O. G., Mogan, A., Moggi, N., Montagna, E., Montanari, C., Montanari, A., Mooney, M., Moreno-Granados, G., Mueller, J., Murphy, M., Naples, D., Nguyen, V. C. L, Palestini, S., Pallavicini, M., Paolone, V., Papaleo, R., Pasqualini, L., Patrizii, L., Paudel, L., Pelegrina-Gutiérrez, L., Petrillo, G., Petta, C., Pia, V., Pietropaolo, F., Poppi, F., Pozzato, M., Putnam, G., Qian, X., Rappoldi, A., Raselli, G. L., Repetto, S., Resnati, F., Ricci, A. M., Riccobene, G., Richards, E., Rosenberg, M., Rossella, M., Rowe, N., Roy, P., Rubbia, C., Saad, M., Safa, I., Saha, S., Sala, P., Salmoria, G., Samanta, S., Sapienza, P., Scaramelli, A., Scarpelli, A., Schmitz, D., Schukraft, A., Senadheera, D., Seo, S-H., Sergiampietri, F., Sirri, G., Smedley, J. S., Smith, J., Stanco, L., Stewart, J., Tanaka, H. A., Tapia, F., Tenti, M., Terao, K., Terranova, F., Togo, V., Torretta, D., Torti, M., Tortorici, F., Triozzi, R., Tsai, Y. -T., Tufanli, S., Usher, T., Varanini, F., Ventura, S., Vicenzi, M., Vignoli, C., Viren, B., Wieler, F. A., Williams, Z., Wilson, R. J., Wilson, P., Wolfs, J., Wongjirad, T., Wood, A., Worcester, E., Worcester, M., Wospakrik, M., Yadav, S., Yu, H., Yu, J., Zani, A., Zennamo, J., Zettlemoyer, J., Zhang, C., and Zucchelli, S.
Subjects: High Energy Physics - Experiment
Abstract: We present a search for long-lived particles (LLPs) produced from kaon decay that decay to two muons inside the ICARUS neutrino detector. This channel would be a signal of hidden sector models that can address outstanding issues in particle physics such as the strong CP problem and the microphysical origin of dark matter. The search is performed with data collected in the Neutrinos at the Main Injector (NuMI) beam at Fermilab corresponding to $2.41\times 10^{20}$ protons-on-target. No new physics signal is observed, and we set world-leading limits on heavy QCD axions, as well as for the Higgs portal scalar among dedicated searches. Limits are also presented in a model-independent way applicable to any new physics model predicting the process $K\to \pi+S(\to\mu\mu)$, for a long-lived particle S. This result is the first search for new physics performed with the ICARUS detector at Fermilab. It paves the way for the future program of long-lived particle searches at ICARUS.
Published: 2024

30. Novel Clinical-Grade Prostate Cancer Detection and Grading Model: Development and Prospective Validation Using Real World Data, with Performance Assessment on IHC Requested Cases

Author: Nateghi, Ramin, Zhou, Ruoji, Saft, Madeline, Schnauss, Marina, Neill, Clayton, Alam, Ridwan, Handa, Nicole, Huang, Mitchell, Li, Eric V, Goldstein, Jeffery A, Schaeffer, Edward M, Nadim, Menatalla, Pourakpour, Fattaneh, Isaila, Bogdan, Felicelli, Christopher, Mehta, Vikas, Nezami, Behtash G, Ross, Ashley, Yang, Ximing, and Cooper, Lee AD
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract: Artificial intelligence may assist healthcare systems in meeting increasing demand for pathology services while maintaining diagnostic quality and reducing turnaround time and costs. We aimed to investigate the performance of an institutionally developed system for prostate cancer detection, grading, and workflow optimization and to contrast this with commercial alternatives. From August 2021 to March 2023, we scanned 21,396 slides from 1,147 patients with positive biopsies. We developed models for cancer detection, grading, and screening of equivocal cases for IHC ordering. We compared a task-specific model trained using the PANDA dataset of prostate cancer biopsies with one built using features extracted by the general-purpose histology foundation model, UNI and compare their performance in an unfiltered prospectively collected dataset that reflects our patient population (1737 slides,95 patients). We evaluated the contributions of a bespoke model designed to improve sensitivity in detecting small cancer foci and scoring of broader patterns observed at lower resolution. We found high concordance between the developed systems and pathologist reference in detection (AUC 98.5, sensitivity 95.0, and specificity 97.8), ISUP grading (quadratic Cohen's kappa 0.869), grade group 3 or higher (AUC 97.5, sensitivity 94.9, specificity 96.6) and comparable to published data from commercial systems. Screening could reduce IHC ordering for equivocal cases by 44.5% with an overall error rate of 1.8% (1.4% false positive, 0.4% false negative rates). Institutions like academic medical centers that have high scanning volumes and report abstraction capabilities can develop accurate computational pathology models for internal use. These models have the potential to aid in quality control role and to improve workflow in the pathology lab to help meet future challenges in prostate cancer diagnosis.
Published: 2024

31. Burstiness in low stellar-mass Ha emitters at z~2 and z~4-6 from JWST medium band photometry in GOODS-S

Author: Navarro-Carrera, R., Rinaldi, P., Caputi, K. I., Iani, E., Kokorev, V., Kerutt, J., and Cooper, R.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: We analyze a sample of 4500 photometrically-selected Ha emitter galaxies at redshifts z~2 and z~4-6 selected from James Webb Space Telescope (JWST) Near-Infrared Camera (NIRCam) medium-band images in the Great Observatories Origins Deep Survey South (GOODS-S). The bulk (80%) of the galaxies in our sample have stellar masses lower than 10^8 Msun, with a median stellar mass of ~10^7.3 Msun. We derive Ha rest-frame equivalent widths ( EW0(Ha) ), line fluxes, and star formation rates using a robust photometric excess technique tested against spectroscopic measurements, being sensitive to EW0(Ha) > 75 A. Both EW0(Ha) and sSFR(Ha) anti-correlate with stellar mass, and at fixed stellar mass, show a steep increasing trend with redshift sSFR(Ha) ~ (1+z)^2.55. By comparing the Ha and rest-frame UV-derived SFRs, we probe the star formation histories (SFHs) of our galaxies in the past 100 Myr. The fraction of low-mass galaxies (M < 10^8 Msun) with signs of bursty star formation from their SFR(Ha)/SFR(UV) is ~ 50 %. It quickly drops to ~ 25 % for M > 10^8 Msun. This is consistent with the results from sSFR(Ha), showing 80% and 17%, respectively. SFR(Ha)/SFR(UV) is a stricter criterion than those based on the galaxy sSFR(Ha), as it only identifies the strongest starbursts, the ones at the initial phases of a bursty star-formation episode., Comment: 20 pages, 10 figures. Submitted to ApJ, comments are welcome
Published: 2024

32. Controllable Game Level Generation: Assessing the Effect of Negative Examples in GAN Models

Author: Bazzaz, Mahsa and Cooper, Seth
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract: Generative Adversarial Networks (GANs) are unsupervised models designed to learn and replicate a target distribution. The vanilla versions of these models can be extended to more controllable models. Conditional Generative Adversarial Networks (CGANs) extend vanilla GANs by conditioning both the generator and discriminator on some additional information (labels). Controllable models based on complementary learning, such as Rumi-GAN, have been introduced. Rumi-GANs leverage negative examples to enhance the generator's ability to learn positive examples. We evaluate the performance of two controllable GAN variants, CGAN and Rumi-GAN, in generating game levels targeting specific constraints of interest: playability and controllability. This evaluation is conducted under two scenarios: with and without the inclusion of negative examples. The goal is to determine whether incorporating negative examples helps the GAN models avoid generating undesirable outputs. Our findings highlight the strengths and weaknesses of each method in enforcing the generation of specific conditions when generating outputs based on given positive and negative examples.
Published: 2024

33. Guided Game Level Repair via Explainable AI

Author: Bazzaz, Mahsa and Cooper, Seth
Subjects: Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: Procedurally generated levels created by machine learning models can be unsolvable without further editing. Various methods have been developed to automatically repair these levels by enforcing hard constraints during the post-processing step. However, as levels increase in size, these constraint-based repairs become increasingly slow. This paper proposes using explainability methods to identify specific regions of a level that contribute to its unsolvability. By assigning higher weights to these regions, constraint-based solvers can prioritize these problematic areas, enabling more efficient repairs. Our results, tested across three games, demonstrate that this approach can help to repair procedurally generated levels faster.
Published: 2024

34. Troubling Taxonomies in GenAI Evaluation

Author: Berman, Glen, Cooper, Ned, Deng, Wesley Hanwen, and Hutchinson, Ben
Subjects: Computer Science - Human-Computer Interaction
Abstract: To evaluate the societal impacts of GenAI requires a model of how social harms emerge from interactions between GenAI, people, and societal structures. Yet a model is rarely explicitly defined in societal impact evaluations, or in the taxonomies of societal impacts that support them. In this provocation, we argue that societal impacts should be conceptualised as application- and context-specific, incommensurable, and shaped by questions of social power. Doing so leads us to conclude that societal impact evaluations using existing taxonomies are inherently limited, in terms of their potential to reveal how GenAI systems may interact with people when introduced into specific social contexts. We therefore propose a governance-first approach to managing societal harms attended by GenAI technologies., Comment: 3 pages
Published: 2024

35. Elliptic Functions

Author: Cooper, Shaun
Subjects: Mathematics - History and Overview, Mathematics - Number Theory, 33E05
Abstract: This note discusses elliptic functions in Ramanujan's work.
Published: 2024

36. Rephrasing natural text data with different languages and quality levels for Large Language Model pre-training

Author: Pieler, Michael, Bellagente, Marco, Teufel, Hannah, Phung, Duy, Cooper, Nathan, Tow, Jonathan, Rocha, Paulo, Adithyan, Reshinth, Alyafeai, Zaid, Pinnaparaju, Nikhil, Zhuravinskyi, Maksym, and Riquelme, Carlos
Subjects: Computer Science - Computation and Language
Abstract: Recently published work on rephrasing natural text data for pre-training LLMs has shown promising results when combining the original dataset with the synthetically rephrased data. We build upon previous work by replicating existing results on C4 and extending them with our optimized rephrasing pipeline to the English, German, Italian, and Spanish Oscar subsets of CulturaX. Our pipeline leads to increased performance on standard evaluation benchmarks in both the mono- and multilingual setup. In addition, we provide a detailed study of our pipeline, investigating the choice of the base dataset and LLM for the rephrasing, as well as the relationship between the model size and the performance after pre-training. By exploring data with different perceived quality levels, we show that gains decrease with higher quality. Furthermore, we find the difference in performance between model families to be bigger than between different model sizes. This highlights the necessity for detailed tests before choosing an LLM to rephrase large amounts of data. Moreover, we investigate the effect of pre-training with synthetic data on supervised fine-tuning. Here, we find increasing but inconclusive results that highly depend on the used benchmark. These results (again) highlight the need for better benchmarking setups. In summary, we show that rephrasing multilingual and low-quality data is a very promising direction to extend LLM pre-training data., Comment: 21 pages, 4 figures, 12 tables
Published: 2024

37. Prediction of microstructural representativity from a single image

Author: Dahari, Amir, Docherty, Ronan, Kench, Steve, and Cooper, Samuel J.
Subjects: Statistics - Computation, Computer Science - Computer Vision and Pattern Recognition, Statistics - Applications
Abstract: In this study, we present a method for predicting the representativity of the phase fraction observed in a single image (2D or 3D) of a material. Traditional approaches often require large datasets and extensive statistical analysis to estimate the Integral Range, a key factor in determining the variance of microstructural properties. Our method leverages the Two-Point Correlation function to directly estimate the variance from a single image (2D or 3D), thereby enabling phase fraction prediction with associated confidence levels. We validate our approach using open-source datasets, demonstrating its efficacy across diverse microstructures. This technique significantly reduces the data requirements for representativity analysis, providing a practical tool for material scientists and engineers working with limited microstructural data. To make the method easily accessible, we have created a web-application, \url{www.imagerep.io}, for quick, simple and informative use of the method.
Published: 2024

38. Electronic structure of norbornadiene and quadricyclane

Author: Cooper, Joseph C. and Kirrander, Adam
Subjects: Physics - Chemical Physics
Abstract: The ground and excited state electronic structure of the molecular photoswitches quadricyclane and norbornadiene is examined qualitatively and quantitatively. A new custom basis set is introduced, optimised for efficient yet accurate calculations. A number of advanced multi-configurational and multi-reference electronic structure methods are evaluated, identifying those sufficiently accurate and efficient to be used in {\it{on-the-fly}} simulations of photoexcited dynamics. The key valence states participating in the isomerisation reaction are investigated, specifically mapping the important S$_1$/S$_0$ conical intersection that governs the non-radiative decay of the excited system. The powerful yet simple three-state valence model introduced here provides a suitable base for future computational exploration of the photodynamics of the substituted molecules suitable for \textit{e.g}.\ energy-storage applications.
Published: 2024

39. Demonstration of new MeV-scale capabilities in large neutrino LArTPCs using ambient radiogenic and cosmogenic activity in MicroBooNE

Author: MicroBooNE collaboration, Abratenko, P., Alterkait, O., Aldana, D. Andrade, Arellano, L., Asaadi, J., Ashkenazi, A., Balasubramanian, S., Baller, B., Barnard, A., Barr, G., Barrow, D., Barrow, J., Basque, V., Bateman, J., Rodrigues, O. Benevides, Berkman, S., Bhanderi, A., Bhat, A., Bhattacharya, M., Bishai, M., Blake, A., Bogart, B., Bolton, T., Brunetti, M. B., Camilleri, L., Cao, Y., Caratelli, D., Cavanna, F., Cerati, G., Chappell, A., Chen, Y., Conrad, J. M., Convery, M., Cooper-Troendle, L., Crespo-Anadon, J. I., Cross, R., Del Tutto, M., Dennis, S. R., Detje, P., Diurba, R., Djurcic, Z., Duffy, K., Dytman, S., Eberly, B., Englezos, P., Ereditato, A., Evans, J. J., Fang, C., Fleming, B. T., Foreman, W., Franco, D., Furmanski, A. P., Gao, F., Garcia-Gamez, D., Gardiner, S., Ge, G., Gollapinni, S., Gramellini, E., Green, P., Greenlee, H., Gu, L., Gu, W., Guenette, R., Guzowski, P., Hagaman, L., Handley, M. D., Hen, O., Hilgenberg, C., Horton-Smith, G. A., Imani, Z., Irwin, B., Ismail, M. S., James, C., Ji, X., Jo, J. H., Johnson, R. A., Jwa, Y. J., Kalra, D., Karagiorgi, G., Ketchum, W., Kirby, M., Kobilarcik, T., Lane, N., Li, J. -Y., Li, Y., Lin, K., Littlejohn, B. R., Liu, L., Louis, W. C., Luo, X., Mahmud, T., Mariani, C., Marsden, D., Marshall, J., Martinez, N., Caicedo, D. A. Martinez, Martynenko, S., Mastbaum, A., Mawby, I., McConkey, N., Meddage, V., Mellet, L., Mendez, J., Micallef, J., Miller, K., Mistry, K., Mohayai, T., Mogan, A., Mooney, M., Moor, A. F., Moore, C. D., Lepin, L. Mora, Moudgalya, M. M., Babu, S. Mulleria, Naples, D., Navrer-Agasson, A., Nayak, N., Nebot-Guinot, M., Nguyen, C., Nowak, J., Oza, N., Palamara, O., Pallat, N., Paolone, V., Papadopoulou, A., Papavassiliou, V., Parkinson, H., Pate, S. F., Patel, N., Pavlovic, Z., Piasetzky, E., Pletcher, K., Pophale, I., Qian, X., Raaf, J. L., Radeka, V., Rafique, A., Reggiani-Guzzo, M., Ren, L., Rochester, L., Rondon, J. Rodriguez, Rosenberg, M., Ross-Lonergan, M., Safa, I., Schmitz, D. W., Schukraft, A., Seligman, W., Shaevitz, M. H., Sharankova, R., Shi, J., Snider, E. L., Soderberg, M., Soldner-Rembold, S., Spitz, J., Stancari, M., John, J. St., Strauss, T., Szelc, A. M., Taniuchi, N., Terao, K., Thorpe, C., Torbunov, D., Totani, D., Toups, M., Trettin, A., Tsai, Y. -T., Tyler, J., Uchida, M. A., Usher, T., Viren, B., Wang, J., Weber, M., Wei, H., White, A. J., Wolbers, S., Wongjirad, T., Wospakrik, M., Wresilo, K., Wu, W., Yandel, E., Yang, T., Yates, L. E., Yu, H. W., Zeller, G. P., Zennamo, J., and Zhang, C.
Subjects: High Energy Physics - Experiment
Abstract: Large neutrino liquid argon time projection chamber (LArTPC) experiments can broaden their physics reach by reconstructing and interpreting MeV-scale energy depositions, or blips, present in their data. We demonstrate new calorimetric and particle discrimination capabilities at the MeV energy scale using reconstructed blips in data from the MicroBooNE LArTPC at Fermilab. We observe a concentration of low energy ($<$3 MeV) blips around fiberglass mechanical support struts along the TPC edges with energy spectrum features consistent with the Compton edge of 2.614 MeV $^{208}$Tl decay $\gamma$ rays. These features are used to verify proper calibration of electron energy scales in MicroBooNE's data to few percent precision and to measure the specific activity of $^{208}$Tl in the fiberglass composing these struts, $(11.7 \pm 0.2 ~\text{(stat)} \pm 2.8~\text{(syst)})~\text{Bq/kg}$. Cosmogenically-produced blips above 3 MeV in reconstructed energy are used to showcase the ability of large LArTPCs to distinguish between low-energy proton and electron energy depositions. An enriched sample of low-energy protons selected using this new particle discrimination technique is found to be smaller in data than in dedicated CORSIKA cosmic ray simulations, suggesting either incorrect CORSIKA modeling of incident cosmic fluxes or particle transport modeling issues in Geant4., Comment: 19 pages, 15 figures total including the supplementary material section, 1 table. CC BY license
Published: 2024

40. Ramanujan--Fine integrals for level 10

Author: Cooper, Shaun, Huber, Timothy, and Opoku, Jeffery
Subjects: Mathematics - Number Theory, 11F11, 33E05
Abstract: We investigate the question of when an eta quotient is a derivative of a formal power series with integer coefficients and present an analysis in the case of level 10. As a consequence, we establish and classify an infinite number of integral evaluations such as $$ \int_0^{e^{-2\pi/\sqrt{10}}} q\prod_{j=1}^\infty \frac{(1-q^j)^3(1-q^{10j})^8}{(1-q^{5j})^7} \text{d} q = \frac14\left(\sqrt{10-4\sqrt{5}}-1\right). $$ We describe how the results were found and give reasons for why it is reasonable to conjecture that the list is complete for level 10.
Published: 2024

41. A probe of the maximum energetics of fast radio bursts through a prolific repeating source

Author: Ould-Boukattine, O. S., Chawla, P., Hessels, J. W. T., Cooper, A. J., Gawroński, M. P., Herrmann, W., Kirsten, F., Hewitt, D. M., Konijn, D. C., Nimmo, K., Pleunis, Z., Puchalska, W., and Snelders, M. P.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: Fast radio bursts (FRBs) are sufficiently energetic to be detectable from luminosity distances up to at least seven billion parsecs (redshift $z > 1$). Probing the maximum energies and luminosities of FRBs constrains their emission mechanism and cosmological population. Here we investigate the maximum energetics of a highly active repeater, FRB 20220912A, using 1,500h of observations. We detect $130$ high-energy bursts and find a break in the burst energy distribution, with a flattening of the power-law slope at higher energy. This is consistent with the behaviour of another highly active repeater, FRB 20201124A. Furthermore, we model the rate of the highest-energy bursts and find a turnover at a characteristic spectral energy density of $E^{\textrm{char}}_{\nu} = 2.09^{+3.78}_{-1.04}\times10^{32}$ erg/Hz. This characteristic maximum energy agrees well with observations of apparently one-off FRBs, suggesting a common physical mechanism for their emission. The extreme burst energies push radiation and source models to their limit., Comment: Comments welcome
Published: 2024

42. Observing the evolution of the Sun's global coronal magnetic field over eight months

Author: Yang, Zihao, Tian, Hui, Tomczyk, Steven, Liu, Xianyu, Gibson, Sarah, Morton, Richard J., and Downs, Cooper
Subjects: Astrophysics - Solar and Stellar Astrophysics
Abstract: The magnetic field in the Sun's corona stores energy that can be released to heat the coronal plasma and drive solar eruptions. Measurements of the global coronal magnetic field have been limited to a few snapshots. We present observations using the Upgraded Coronal Multi-channel Polarimeter, which provided 114 magnetograms of the global corona above the solar limb spanning approximately eight months. We determined the magnetic field distributions at different solar radii in the corona, and monitored the evolution at different latitudes over multiple solar rotations. We found varying field strengths from <1 to 20 Gauss within 1.05-1.6 solar radii and a signature of active longitude in the coronal magnetic field. Coronal models are generally consistent with the observational data, with larger discrepancies in high-latitude regions., Comment: This is the author's version of the work. It is posted here by permission of the AAAS for personal use, not for redistribution. The definitive version was published in Science on October 4, 2024, doi: 10.1126/science.ado2993 (https://www.science.org/doi/10.1126/science.ado2993). Cite this paper as Yang et al. 2024, Science, 386, 76-82
Published: 2024
Full Text: View/download PDF

43. AUTALIC: A Dataset for Anti-AUTistic Ableist Language In Context

Author: Rizvi, Naba, Strickland, Harper, Gitelman, Daniel, Cooper, Tristan, Morales-Flores, Alexis, Golden, Michael, Kallepalli, Aekta, Alurkar, Akshat, Owens, Haaset, Ahmedi, Saleha, Khirwadkar, Isha, Munyaka, Imani, and Ousidhoum, Nedjma
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: As our understanding of autism and ableism continues to increase, so does our understanding of ableist language towards autistic people. Such language poses a significant challenge in NLP research due to its subtle and context-dependent nature. Yet, detecting anti-autistic ableist language remains underexplored, with existing NLP tools often failing to capture its nuanced expressions. We present AUTALIC, the first benchmark dataset dedicated to the detection of anti-autistic ableist language in context, addressing a significant gap in the field. The dataset comprises 2,400 autism-related sentences collected from Reddit, accompanied by surrounding context, and is annotated by trained experts with backgrounds in neurodiversity. Our comprehensive evaluation reveals that current language models, including state-of-the-art LLMs, struggle to reliably identify anti-autistic ableism and align with human judgments, underscoring their limitations in this domain. We publicly release AUTALIC along with the individual annotations which serve as a valuable resource to researchers working on ableism, neurodiversity, and also studying disagreements in annotation tasks. This dataset serves as a crucial step towards developing more inclusive and context-aware NLP systems that better reflect diverse perspectives., Comment: 9 pages, 5 figures, 7 tables
Published: 2024

44. Upsampling DINOv2 features for unsupervised vision tasks and weakly supervised materials segmentation

Author: Docherty, Ronan, Vamvakeros, Antonis, and Cooper, Samuel J.
Subjects: Computer Science - Computer Vision and Pattern Recognition, Condensed Matter - Materials Science, Electrical Engineering and Systems Science - Image and Video Processing
Abstract: The features of self-supervised vision transformers (ViTs) contain strong semantic and positional information relevant to downstream tasks like object localization and segmentation. Recent works combine these features with traditional methods like clustering, graph partitioning or region correlations to achieve impressive baselines without finetuning or training additional networks. We leverage upsampled features from ViT networks (e.g DINOv2) in two workflows: in a clustering based approach for object localization and segmentation, and paired with standard classifiers in weakly supervised materials segmentation. Both show strong performance on benchmarks, especially in weakly supervised segmentation where the ViT features capture complex relationships inaccessible to classical approaches. We expect the flexibility and generalizability of these features will both speed up and strengthen materials characterization, from segmentation to property-prediction.
Published: 2024

45. Progenitor diversity in the accreted stellar halos of Milky Way-like galaxies

Author: Pu, Sy-Yun, Cooper, Andrew P., Grand, Robert J. J., Gómez, Facundo A., and Monachesi, Antonela
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: Ongoing large stellar spectroscopic surveys of the Milky Way seek to reconstruct the major events in the assembly history of the Galaxy. Chemical and kinematic observations can be used to separate the contributions of different progenitor galaxies to the present-day stellar halo. Here we compute the number of progenitors that contribute to the accreted stellar halos of simulated Milky Way-like galaxies as a function of radius (the radial diversity) in three suites of models: Bullock & Johnston, Aquarius and Auriga. We show that there are significant differences between the predictions of these three models, beyond the halo-to-halo scatter expected in $\Lambda$CDM. Predictions of diversity from numerical simulations are sensitive to model-dependent assumptions regarding the efficiency of star formation in dwarf galaxies. We compare, at face value, to current constraints on the radial diversity of the Milky Way's accreted halo. These constraints imply that the halo of our Galaxy is dominated by $\sim2$ progenitors in the range $8-45\,\mathrm{kpc}$, in contrast to averages of $7$ progenitors in the Bullock & Johnston models, $3.5$ in Aquarius and $4.2$ in Auriga over the same region. We additionally find that the models with radial diversity most similar to that of the Milky Way are predominantly those with ongoing merger events. The Milky Way therefore appears unusual in having an accreted stellar halo dominated by a small number of progenitors accreted at very early times., Comment: 20 pages, 10 figures, submitted to ApJ. This paper describes the first public release of the particle-tagging stellar halo data from Cooper et al. (2010), see https://github.com/nthu-ga/aquarius-halos
Published: 2024

46. Black-box Uncertainty Quantification Method for LLM-as-a-Judge

Author: Wagner, Nico, Desmond, Michael, Nair, Rahul, Ashktorab, Zahra, Daly, Elizabeth M., Pan, Qian, Cooper, Martín Santillán, Johnson, James M., and Geyer, Werner
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract: LLM-as-a-Judge is a widely used method for evaluating the performance of Large Language Models (LLMs) across various tasks. We address the challenge of quantifying the uncertainty of LLM-as-a-Judge evaluations. While uncertainty quantification has been well-studied in other domains, applying it effectively to LLMs poses unique challenges due to their complex decision-making capabilities and computational demands. In this paper, we introduce a novel method for quantifying uncertainty designed to enhance the trustworthiness of LLM-as-a-Judge evaluations. The method quantifies uncertainty by analyzing the relationships between generated assessments and possible ratings. By cross-evaluating these relationships and constructing a confusion matrix based on token probabilities, the method derives labels of high or low uncertainty. We evaluate our method across multiple benchmarks, demonstrating a strong correlation between the accuracy of LLM evaluations and the derived uncertainty scores. Our findings suggest that this method can significantly improve the reliability and consistency of LLM-as-a-Judge evaluations.
Published: 2024

47. Asynchronous 3-Majority Dynamics with Many Opinions

Author: Cooper, Colin, Mallmann-Trenn, Frederik, Radzik, Tomasz, Shimizu, Nobutaka, and Shiraga, Takeharu
Subjects: Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Discrete Mathematics
Abstract: We consider 3-Majority, a probabilistic consensus dynamics on a complete graph with $n$ vertices, each vertex starting with one of $k$ initial opinions. At each discrete time step, a vertex $u$ is chosen uniformly at random. The selected vertex $u$ chooses three neighbors $v_1,v_2,v_3$ uniformly at random with replacement and takes the majority opinion held by the three, where ties are broken in favor of the opinion of $v_3$. The main quantity of interest is the consensus time, the number of steps required for all vertices to hold the same opinion. This asynchronous version turns out to be considerably harder to analyze than the synchronous version and so far results have only been obtained for $k=2$. Even in the synchronous version the results for large $k$ are far from tight. In this paper we prove that the consensus time is $\tilde{\Theta}( \min(nk,n^{1.5}) )$ for all $k$. These are the first bounds for all $k$ that are tight up to a polylogarithmic factor., Comment: Symposium on Discrete Algorithms (SODA25)
Published: 2024

48. Five-fold precision enhancement in a cold atom experiment via adaptive symmetry-informed Bayesian strategies

Author: Overton, Matt, Rubio, Jesús, Cooper, Nathan, Baldolini, Daniele, Johnson, David, Anders, Janet, and Hackermüller, Lucia
Subjects: Quantum Physics, Physics - Atomic Physics, Physics - Data Analysis, Statistics and Probability
Abstract: Bayesian methods promise enhanced device performance and accelerated data collection. We demonstrate an adaptive Bayesian measurement strategy for atom number estimation in a quantum technology experiment, utilising a symmetry-informed loss function. Compared to a standard unoptimised strategy, our method yields a five-fold reduction in the fractional variance of the atom number estimate. Equivalently, it achieves the target precision with a third of the data points previously required. We provide general expressions for the optimal estimator and error for any quantity amenable to symmetry-informed strategies, facilitating the application of these strategies in quantum computing, communication, metrology, and the wider quantum technology sector., Comment: 6 + 5 pages, 3 + 2 figures, 1 + 1 tables. Updated references and minor revisions
Published: 2024

49. Comparison of deep learning and conventional methods for disease onset prediction

Author: John, Luis H., Kim, Chungsoo, Kors, Jan A., Chang, Junhyuk, Morgan-Cooper, Hannah, Desai, Priya, Pang, Chao, Rijnbeek, Peter R., Reps, Jenna M., and Fridgeirsson, Egill A.
Subjects: Computer Science - Machine Learning
Abstract: Background: Conventional prediction methods such as logistic regression and gradient boosting have been widely utilized for disease onset prediction for their reliability and interpretability. Deep learning methods promise enhanced prediction performance by extracting complex patterns from clinical data, but face challenges like data sparsity and high dimensionality. Methods: This study compares conventional and deep learning approaches to predict lung cancer, dementia, and bipolar disorder using observational data from eleven databases from North America, Europe, and Asia. Models were developed using logistic regression, gradient boosting, ResNet, and Transformer, and validated both internally and externally across the data sources. Discrimination performance was assessed using AUROC, and calibration was evaluated using Eavg. Findings: Across 11 datasets, conventional methods generally outperformed deep learning methods in terms of discrimination performance, particularly during external validation, highlighting their better transportability. Learning curves suggest that deep learning models require substantially larger datasets to reach the same performance levels as conventional methods. Calibration performance was also better for conventional methods, with ResNet showing the poorest calibration. Interpretation: Despite the potential of deep learning models to capture complex patterns in structured observational healthcare data, conventional models remain highly competitive for disease onset prediction, especially in scenarios involving smaller datasets and if lengthy training times need to be avoided. The study underscores the need for future research focused on optimizing deep learning models to handle the sparsity, high dimensionality, and heterogeneity inherent in healthcare datasets, and find new strategies to exploit the full capabilities of deep learning methods.
Published: 2024

50. Impeding LLM-assisted Cheating in Introductory Programming Assignments via Adversarial Perturbation

Author: Salim, Saiful Islam, Yang, Rubin Yuchan, Cooper, Alexander, Ray, Suryashree, Debray, Saumya, and Rahaman, Sazzadur
Subjects: Computer Science - Computation and Language, Computer Science - Computers and Society, Computer Science - Software Engineering
Abstract: While Large language model (LLM)-based programming assistants such as CoPilot and ChatGPT can help improve the productivity of professional software developers, they can also facilitate cheating in introductory computer programming courses. Assuming instructors have limited control over the industrial-strength models, this paper investigates the baseline performance of 5 widely used LLMs on a collection of introductory programming problems, examines adversarial perturbations to degrade their performance, and describes the results of a user study aimed at understanding the efficacy of such perturbations in hindering actual code generation for introductory programming assignments. The user study suggests that i) perturbations combinedly reduced the average correctness score by 77%, ii) the drop in correctness caused by these perturbations was affected based on their detectability.
Published: 2024

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