Reconstructing hadronically decaying tau leptons with a jet foundation model

  • This paper investigates adapting the OmniJet-a jet foundation model, originally pretrained on a different dataset and task, to reconstruct hadronically decaying tau leptons, demonstrating that fine-tuning the pretrained model significantly improves performance, particularly momentum resolution, compared to training from scratch.
  • Submitted to SciPost Physics
  • https://doi.org/10.48550/arXiv.2503.19165
    A figure with two diagrams illustrating workflows for jet foundation models. The left diagram shows the typical workflow: a large jet dataset trains a foundation model, which produces a jet embedding used by a task-specific head for jet tagging. The right diagram shows the paper's approach: the same foundation model is adapted using a smaller hadronic tau dataset for new tasks (tau tagging, kinematic reconstruction, decay mode), with options to fine-tune the model (screwdriver icon) or train task-specific heads from scratch (fire icon).
    A histogram comparing the performance of kinematic reconstruction for hadronically decaying tau leptons using two training methods: fine-tuning a pre-existing model (green distribution) and training a model from scratch (red distribution). The x-axis represents the relative difference between predicted and true transverse momentum (pT_pred - pT_true) / pT_true, and the y-axis shows the normalized bin content. The fine-tuned model's distribution is narrower and centered closer to zero, indicating better performance.
    (Left) We contrast the typical training workflow for jet foundation models with the generalized approach used in this study to adapt an existing model to new datasets and tasks, specifically for hadronic tau lepton reconstruction. (Right) Fine-tuning improves the pT reconstruction resolution by approximately 50% compared to training from scratch on small datasets.

On the detection of stellar wakes in the Milky Way: a deep learning approach

  • This paper assesses the viability of using deep learning trained on simulations to detect stellar wakes induced by dark matter subhalos in the Milky Way’s stellar halo, finding the method can infer subhalo presence down to masses of 5x10⁷ M☉.
  • Astronomy and Astrophysics 693, A227 (2025)
  • https://doi.org/10.1051/0004-6361/202451480
    X-Y plot showing stellar overdensity (color scale) caused by a simulated subhalo (black circle at center). An overdense wake region extends to the left (-X direction), with a dashed contour indicating the half-max density area.
    Plot comparing the performance (ROC curves) of machine learning models in detecting three different subhalo masses. The x-axis shows the true positive rate, and the y-axis shows the false positive rate. Each colored line represents a model trained with a different subhalo mass. A dashed black line indicates random chance performance.
    (Left) Simulated stellar overdensity wake induced by a 5x10⁸ M☉ subhalo moving through the stellar halo, projected onto the X-Y plane. (Right) This chart shows how well a machine learning model can find stellar wakes of different mass. A line closer to the bottom-right corner means the model is better at finding the wakes without mistakenly identifying random patterns as wakes.

A unified machine learning approach for reconstructing hadronically decaying tau leptons

Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors

  • Nature Communications Physics 7, 124 (2024)
  • https://doi.org/10.1038/s42005-024-01599-5
  • We show that a scalable and portable graph neural network algorithm can efficiently reconstruct stable particles, resulting in more accurate event reconstruction.

Tau lepton identification and reconstruction: A new frontier for jet-tagging ML algorithms

  • Computer Physics Communications 298 (2024)
  • https://doi.org/10.1016/j.cpc.2024.109095
  • We demonstrate that the transformer-based architecture can be used for tau lepton identification, and that it outperforms alternative approaches based on heuristic algorithms and convolutional nets.

A Bayesian estimation of the Milky Way’s circular velocity curve using Gaia DR3

Sensitivity Estimation for Dark Matter Subhalos in Synthetic Gaia DR2 using Deep Learning

MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks

Data Analysis with GPU-Accelerated Kernels

  • Proceedings of Science, Volume 390, ICHEP (2020)
  • https://doi.org/10.22323/1.390.0908
  • I developed a novel approach for large-scale high-energy physics data analysis based on GPUs, accelerating the time-to-insight by ~10x.

Observation of ttH production

Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV

  • Journal of Instrumentation, Volume 13 (2018)
  • https://doi.org/10.1088/1748-0221/13/05/P05011
  • I developed a novel b-quark identification model (cMVAv2) based on xgboost, thus introducing industry-standard tools to the CMS b-tagging team.