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1. MLPF: efficient machine-learned particle-flow reconstruction using graph neural networks

   https://doi.org/10.1140/epjc/s10052-021-09158-w  

   Pata, Joosep; Duarte, Javier; Vlimant, Jean-Roch; Pierini, Maurizio; Spiropulu, Maria (May 2021, The European Physical Journal C)

   null (Ed.)

   Abstract In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the calorimeters and the trackers, significantly improving the detector resolution for jets and the missing transverse momentum. In view of the planned high-luminosity upgrade of the CERN Large Hadron Collider (LHC), it is necessary to revisit existing reconstruction algorithms and ensure that both the physics and computational performance are sufficient in an environment with many simultaneous proton–proton interactions (pileup). Machine learning may offer a prospect for computationally efficient event reconstruction that is well-suited to heterogeneous computing platforms, while significantly improving the reconstruction quality over rule-based algorithms for granular detectors. We introduce MLPF, a novel, end-to-end trainable, machine-learned particle-flow algorithm based on parallelizable, computationally efficient, and scalable graph neural network optimized using a multi-task objective on simulated events. We report the physics and computational performance of the MLPF algorithm on a Monte Carlo dataset of top quark–antiquark pairs produced in proton–proton collisions in conditions similar to those expected for the high-luminosity LHC. The MLPF algorithm improves the physics response with respect to a rule-based benchmark algorithm and demonstrates computationally scalable particle-flow reconstruction in a high-pileup environment. 

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2. An improved infrastructure for the IceCube realtime system

   https://doi.org/10.22323/1.444.1106  

   Lincetto, Massimiliano; Eric_M, Evans-Jacquez; Riedel, Benedikt; David, Schultz; Yuan, Tianlu; Abbasi, Rasha; Ackermann, Markus; Adams, Jenni; Agarwalla, Sanjib; Aguilar, Juanan; et al (August 2023, Pos proceedings of science)

   The IceCube realtime alert system has been operating since 2016. It provides prompt alerts on high-energy neutrino events to the astroparticle physics community. The localization regions for the incoming direction of neutrinos are published through NASA's Gamma-ray Coordinate Network (GCN). The IceCube realtime system consists of infrastructure dedicated to the selection of alert events, the reconstruction of their topology and arrival direction, the calculation of directional uncertainty contours and the distribution of the event information through public alert networks. Using a message-based workflow management system, a dedicated software (SkyDriver) provides a representational state transfer (REST) interface to parallelized reconstruction algorithms. In this contribution, we outline the improvements of the internal infrastructure of the IceCube realtime system that aims to streamline the internal handling of neutrino events, their distribution to the SkyDriver interface, the collection of the reconstruction results as well as their conversion into human- and machine-readable alerts to be publicly distributed through different alert networks. An approach for the long-term storage and cataloging of alert events according to findability, accessibility, interoperability and reusability (FAIR) principles is outlined. 

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3. Bayesian reconstruction and differential testing of excised introns

   https://doi.org/10.1093/bioinformatics/btaf646  

   Hosseini, Marjan; McConnell, Devin; Aguiar, Derek (December 2025, Bioinformatics)

   Kendziorski, Christina (Ed.)

   Abstract MotivationCharacterizing the differential excision of introns is critical for understanding the functional complexity of a cell or tissue, from normal developmental processes to disease pathogenesis. Most transcript reconstruction methods infer full-length transcripts from high-throughput sequencing data. However, this is a challenging task due to incomplete annotations and the heterogeneous expression of transcripts across cell-types, tissues, and experimental conditions. Several recent methods circumvent these difficulties by considering local splicing events, but these methods lose transcript-level splicing information and may conflate similar, but distinct transcripts. ResultsIn this work, we formalize a new transcript reconstruction problem that interpolates between the full-length and local splicing perspectives by considering sequences of exon–exon junctions (SEEJs) that co-occur in transcripts. We then present a hierarchical Bayesian admixture model and posterior inference algorithms for computing SEEJs (BSEEJ), and a generalized linear model for characterizing differential SEEJ usage based on model parameter estimates. We show that BSEEJ achieves high F1 score for reconstruction tasks and improved accuracy and sensitivity in differential splicing when compared with six transcript and local splicing methods on simulated data. Lastly, we evaluate BSEEJ on experimental data based on transcript reconstruction, novelty of transcripts produced, model sensitivity to hyperparameters, and a functional analysis of differentially expressed SEEJs. Availability and implementationBSEEJ is freely available at https://github.com/bayesomicslab/BSEEJ. 

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4. IceCat-2 Preliminary Data Release: Seven Selected Events

   https://doi.org/10.7910/dvn/rx28yt  

   Collaboration, IceCube; Collaboration, IceCube (January 2025, Harvard Dataverse)

   This preliminary data release contains alerts previously presented at the 39th International Cosmic Ray Conference (ICRC 2025), see PoS(ICRC2025)1224. IC-170922A — in spatial coincidence with the blazar TXS 0506+056, representing the first association between a neutrino alert and an astrophysical source. IC-220424A and IC-230416A — in spatial coincidence with NGC 7469. IC-200530A, IC-191119A, and IC-191001A — previously identified in spatial coincidence with several Tidal Disruption Events. In addition to these events, we also include IC-230724A in this preliminary release. Its inclusion is motivated by the fact that it was a promising Gold alert, with an average probability of astrophysical origin of 53%. An issue in the automatic processing pipeline responsible for the offline reconstruction delayed the distribution of its updated position (https://gcn.nasa.gov/circulars/34265). To ensure timely availability at the time, an alternative reconstruction algorithm was applied. Following the update of the reconstruction algorithms for high-energy track alerts in September 2024 (https://roc.icecube.wisc.edu/public/docs/IceCube_Update_Muon_Alert_Reco.pdf), the new position and the alert-related parameters have now been recalculated. In the following months, the full catalog IceCat-2 will be publish, together with a related publication. 

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5. Neutrino interaction vertex reconstruction in DUNE with Pandora deep learning

   https://doi.org/10.1140/epjc/s10052-025-14313-8  

   Abud, A Abed; Acciarri, R; Acero, M A; Adames, M R; Adamov, G; Adamowski, M; Adams, D; Adinolfi, M; Adriano, C; Aduszkiewicz, A; et al (June 2025, The European Physical Journal C)

   Abstract The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20% increase in the efficiency of sub-1 cm vertex reconstruction across all neutrino flavours. 

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