Investigation of a protracted neutrino mystery

Neutrinos are one of the most mysterious members of the Standard Model, a framework for describing fundamental forces and particles in nature. While among the most widely known particles in the universe, they very rarely interact with matter, making their detection a challenging experimental feat. One of the long-standing puzzles in neutrino physics comes from the Mini Booster Neutrino Experiment (MiniBooNE), which ran from 2002 to 2017 at the Fermi National Accelerator Laboratory, or Fermilab, in Illinois. MiniBooNE observed significantly more neutrino interactions that produce electrons than one would expect given our best knowledge of the standard model – and physicists are trying to understand why.

MicroBooNE Neutrino Interaction Neural Network

A neural network predicts real life: Actual data from a neutrino interaction in MicroBooNE LArTPC is shown on the left, where an electron neutrino enters from the left and interacts with a neutron in an argon nucleus, producing a proton (p) and an electron (s). ). A cosmic ray muon is seen at the bottom. SparseSSNet, a deep learning algorithm that labels each pixel by the type of particle it suspects, is seen on the right. The proton is correctly identified as a high-ionizing particle (HIP), the electron is correctly identified as an electromagnetic shower, and the muon is correctly identified as a minimum ionizing particle (MIP). Credit: MicroBooNE Collaboration

In 2007, researchers developed the idea for a follow-up experiment, MicroBooNE, which recently completed data collection at Fermilab. MicroBooNE is an ideal test of the MiniBooNE surplus thanks to its use of a new detector technology known as the liquid argon time projection chamber (LArTPC), which provides high-resolution images of the particles created in neutrino interactions.

Physics students Nicholas Kamp and Lauren Yates, along with Professor Janet Conrad, all of the MIT Laboratory for Nuclear Science, have played a leading role in MicroBooNE’s in-depth learning-based quest for a surplus of neutrinos in the Fermilab Booster Neutrino Beam. In this interview, Kamp discusses the future of the MiniBooNE anomaly in the context of MicroBooNE’s recent results.

Lauren Yates

Lauren Yates, an MIT graduate student in physics, is monitoring the MicroBooNE detector in the Remote Operation Center West control room in Fermilab, Illinois. Credit: Reidar Hahn / Fermilab

Q: Why is the MiniBooNE anomaly a big deal?

ONE: One of the big open questions in neutrinophysics concerns the possible existence of a hypothetical particle called the “sterile neutrino.” Finding a new particle would be a very big deal because it can give us clues to the larger theory that explains the many particles we see. The most common explanation for the MiniBooNE surplus involves the addition of such a sterile neutrino to the standard model. Due to the effects of neutrinosillations, this sterile neutrino would manifest itself as an enhancement of electron neutrinos in MiniBooNE.

There are many additional anomalies seen in neutrinophysics that indicate that this particle may exist. However, it is difficult to explain these anomalies with MiniBooNE through a single sterile neutrino – the full picture does not quite fit. Our group at MIT is interested in new physics models that could potentially explain this full picture.

Q: What is our current understanding of the MiniBooNE profits?

ONE: Our understanding has developed significantly recently thanks to developments in both the experimental and theoretical areas.

Our group has worked with physicists from Harvard, Columbia and Cambridge universities to explore new sources of photons that can appear in a theoretical model that also has a 20 percent electron signature. We developed a “mixed model” that involves two types of exotic neutrinos – one that transforms into electron flavors and one that decays into a photon. This work is on its way in Physical Review D.

On the experimental end, recent MicroBooNE results – including a deep-learning-based analysis in which our MIT group played an important role – observed no excess of neutrinos producing electrons in the MicroBooNE detector. Keeping in mind the level at which MicroBooNE can make the measurement, this suggests that the MiniBooNE surplus cannot be entirely attributed to extra neutrino interactions. If it is not electrons, then it must be photons, for it is the only particle that can produce a similar signature in MiniBooNE. But we are sure that it is not photons produced by interactions that we know of because they are limited to a low level. So they must come from something new, such as the exotic neutrino decay in the mixed model. Next, MicroBooNE is working on a search that could isolate and identify these additional photons. Stick around!

Q: You mentioned that your group is involved in deep-learning-based MicroBooNE analysis. Why use deep learning in neutrino physics?

ONE: When people look at pictures of cats, they can easily distinguish between species. Similarly, when physicists look at images coming from an LArTPC, they can tell the difference between the particles produced in neutrino interactions without much difficulty. However, due to the nuances of the differences, both tasks prove to be difficult for conventional algorithms.

MIT is a connection between deep-learning ideas. Recently, for example, it became the site of the National Science Foundation AI Institute for Artificial Intelligence and Fundamental Interactions. It made sense for our group to build on the extensive local expertise in the field. We have also had the opportunity to work with fantastic groups at SLAC, Tufts University, Columbia University, and IIT, each with a strong knowledge base in the connection between deep learning and neutrino physics.

One of the key ideas in deep learning is a “neutral network”, which is an algorithm that makes decisions (such as identifying particles in an LArTPC) based on previous exposure to a range of training data. Our group produced the first paper on particle identification using deep learning in neutrino physics, which proved to be a powerful technique. This is a major reason why the recently released results of MicroBooNE’s deep learning-based analysis place strong constraints on an electron neutrino interpretation of the MiniBooNE surplus.

All in all, it is very fortunate that much of the basis for this analysis was done in the AI-rich environment at MIT.

For more on this research, read MicroBooNE experiment showing no hint of sterile neutrino.

References:

“Search for Neutrino-Induced Neutral Current Δ Radiative Decay in MicroBooNE and a First Test of MiniBooNE Low Energy Excess Under a Single-Photon Hypothesis” by MicroBooNE Collaboration: P. Abratenko, R. An, J. Anthony, L. Arellano, J Asaadi, A. Ashkenazi, S. Balasubramanian, B. Baller, C. Barnes, G. Barr, V. Basque, L. Bathe-Peters, O. Benevides Rodrigues, S. Berkman, A. Bhanderi, A. Bhat, M. Bishai, A. Blake, T. Bolton, JY Book, L. Camilleri, D. Caratelli, I. Caro Terrazas, R. Castillo Fernandez, F. Cavanna, G. Cerati, Y. Chen, D. Cianci, JM Conrad, M. Convery, L. Cooper-Troendle, JI Crespo-Anadon, M. Del Tutto, SR Dennis, P. Detje, A. Devitt, R. Diurba, R. Dorrill, K. Duffy, S. Dytman, B Eberly, A. Ereditato, JJ Evans, R. Fine, GA Fiorentini Aguirre, RS Fitzpatrick, BT Fleming, N. Foppiani, D. Franco, AP Furmanski, D. Garcia-Gamez, S. Gardiner, G. Ge, S Gollapinni, O. Goodwin, E. Gramellini, P. Green, H. Greenlee, W. Gu, R. Guenette, P. Guzowski, L. Hagaman, O. Hen, C. Hilge nberg, GA Horton-Smith, A. Hourlier, R. Itay, C. James, X. Ji, L. Jiang, JH Jo, RA Johnson, YJ Jwa, D. Kalra, N. Kamp, N. Kaneshige, G. Karagiorgi, W. Ketchum, M. Kirby, T. Kobilarcik, I. Kreslo, R. LaZur, I. Lepetic, K. Li, Y. Li, K. Lin, BR Littlejohn, WC Louis, X. Luo, K. Manivannan, C. Mariani, D. Marsden, J. Marshall, DA Martinez Caicedo, K. Mason, A. Mastbaum, N. McConkey, V. Meddage, T. Mettler, K. Miller, J. Mills, K. Mistry, T. Mohayai, A. Mogan, J. Moon, M. Mooney, AF Moor, CD Moore, L. Mora Lepin, J. Mousseau, M. Murphy, D. Naples, A. Navrer-Agasson, M. Nebot-Guinot, RK Neely, DA Newmark, J. Nowak, M. Nunes, O. Palamara, V. Paolone, A. Papadopoulou, V. Papavassiliou, SF Pate, N. Patel, A. Paudel, Z. Pavlovic, E. Piasetzky, I. Ponce-Pinto, S. Prince, X. Qian, JL Raaf, V. Radeka, A. Rafique, M. Reggiani-Guzzo, L. Ren, LCJ Rice, L. Rochester, J. Rodriguez Rondon, M. Rosenberg, M Ross-Lonergan, G. Scanavini, DW Schmitz, A. Schukraft, W. Seligman, MH Shaevitz, R. Sharankova, J. Shi, J. Sinclair, A. Smith, EL Snider, M. Soderberg, S. Soldner-Rembold, P. Spentzouris, J. Spitz, M. Stancari, J. St. John, T. Strauss, K. Sutton, S. Sword-Fehlberg, AM Szelc, W. Tang, K. Terao, C. Thorpe, D. Totani, M. Toups, Y.-T. Tsai, MA Uchida, T. Usher, W. Van De Pontseele, B. Viren, M. Weber, H. Wei, Z. Williams, S. Wolbers, T. Wongjirad, M. Wospakrik, K. Wresilo, N. Wright , W. Wu, E. Yandel, T. Yang, G. Yarbrough, LE Yates, HW Yu, GP Zeller, J. Zennamo and C. Zhang, Submitted, Physical review letters.
arXiv: 2110.00409

“Search for an abnormal surplus of charged-current quasi-elastic νe interactions with the MicroBooNE experiment using Deep-Learning-based reconstruction” of MicroBooNE collaboration, submitted, Physical review D.
arXiv: 2110.14080

“Search for an abnormal surplus of charged-current νe interactions without pioneering in the final state of the MicroBooNE experiment” by MicroBooNE Collaboration, submitted, Physical review D.
arXiv: 2110.14065

“Search for an abnormal excess of inclusive charge-current νe interactions in the MicroBooNE experiment using Wire-Cell reconstruction” by MicroBooNE collaboration, submitted, Physical review D.
arXiv: 2110.13978

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