gsoc 26: add lhcb proposal on ml calo reconstruction

_gsocorgs/2026/ub.md

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+---
+title: "Universitat de Barcelona"
+author: "Maciej Szymanski"
+layout: default
+organization: UB
+logo: ub-logo.png
+description: |
+    [The University of Barcelona](https://web.ub.edu/en) is a public academic institution, a national leader in teaching, research, and innovation.
+---
+
+{% include gsoc_proposal.ext %}

_gsocprojects/2026/project_LHCb.md

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+---
+project: LHCb
+layout: default
+logo: lhcb_logo.png
+description: |
+  [LHCb](http://lhcb.web.cern.ch/) is one of the four major experiments at the Large Hadron Collider at CERN, dedicated to precision studies of heavy-flavor hadrons in order to investigate CP violation and rare decays, and to search for physics beyond the Standard Model. The experiment employs a highly specialized forward spectrometer optimized for high-precision measurements of particles produced in proton-proton collisions. In the context of the LHCb Upgrade II, the experiment will operate in a high-luminosity environment, placing stringent demands on detector performance, real-time reconstruction, and advanced software-based reconstruction techniques.
+
+---
+
+{% include gsoc_project.ext %}
+

_gsocproposals/2026/proposal_LHCb_Calo.md

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+---
+title: Transformer-based Reconstruction for Electromagnetic Calorimeters in Future LHC Upgrade Experiments
+layout: gsoc_proposal
+project: LHCb
+year: 2026
+organization:
+  - UB
+  - CERN
+difficulty: medium
+duration: 175
+mentor_avail: June-October
+project_mentors:
+  - email: felipe.luan@cern.ch
+    first_name: Felipe Luan
+    last_name: Souza de Almeida
+    organization: UB
+    is_preferred_contact: yes
+  - email: carla.marin.benito@cern.ch
+    first_name: Carla
+    last_name: Marin Benito
+    organization: UB
+    is_preferred_contact: no
+---
+
+## Description
+
+ Electromagnetic calorimeter reconstruction is a critical component of precision measurements involving neutral particles such as photons and neutral pions (π⁰). The achievable energy resolution directly impacts the sensitivity of physics analyses relying on these final states, including rare decays and CP violation measurements.
+
+In the context of future LHC upgrades, calorimeter reconstruction must satisfy increasingly stringent real-time constraints, making both reconstruction quality and inference performance essential. Transformer-based machine learning models have recently emerged as a promising technology for modeling complex detector responses and long-range correlations, with potential advantages in reconstruction accuracy and scalability.
+
+The goal of this project is to design, implement, and benchmark a Transformer-based reconstruction pipeline for electromagnetic calorimeters, focusing on energy resolution and inference performance. The developed approach will be quantitatively compared to existing standard reconstruction algorithms and GNN-based methods. The project emphasizes software implementation, validation, and benchmarking, rather than open-ended machine learning research, making it well suited for the GSoC timeline.
+
+## Task Ideas
+
+- Design, implementation, and benchmarking of a Transformer-based reconstruction pipeline for electromagnetic calorimeters
+
+## Expected Results and Milestones
+
+### Core deliverables
+
+- A working, documented end-to-end Transformer-based reconstruction pipeline for electromagnetic calorimeter energy reconstruction.
+
+- Energy response and resolution studies using single-photon simulated samples.
+
+- Quantitative comparison with standard reconstruction algorithms and existing GNN-based approaches.
+
+- Benchmarking of inference performance (e.g. latency and throughput) relevant for real-time reconstruction constraints.
+
+### Stretch goals (depending on progress)
+
+- Performance studies under high-luminosity conditions using single-photon events overlaid with minimum-bias background.
+
+- Extended benchmarking studies across different model configurations and detector conditions.
+
+## Requirements
+
+* Intermediate-level Python programming skills
+* Fundamentals of machine learning
+* Familiarity with PyTorch or a similar ML framework
+* Basic knowledge of particle physics or detector concepts is beneficial but not required
+
+## AI Policy
+
+AI assistance is allowed for this contribution. The applicant takes full responsibility for all code and results, disclosing AI use for non-routine tasks (algorithm design, architecture, complex problem-solving). Routine tasks (grammar, formatting, style) do not require disclosure.
+
+## How to Apply
+
+Email mentors with a brief background and interest in ML/particle physics. Please include "gsoc26" in the subject line. Mentors will provide an evaluation task after submission.
+
+## Resources
+
+*   *A Survey on Transformers* (<https://arxiv.org/abs/2106.04554>)
+*   *Transformers are Graph Neural Networks* (<https://arxiv.org/abs/2506.22084>)
+*   PyTorch documentation: [https://docs.pytorch.org/docs/stable/index.html](https://docs.pytorch.org/docs/stable/index.html)
+*   [LHCb experiment](https://lhcb.web.cern.ch/)
+*   *Calibration and performance of the LHCb calorimeters in Run 1 and 2 at the LHC* (<https://arxiv.org/abs/2008.11556>)
+*   *Graph Clustering: a graph-based clustering algorithm for the electromagnetic calorimeter in LHCb* (<https://arxiv.org/abs/2212.11061>)