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grapako/README.md

Juan Ignacio Peralta (Juani) — Astrophysicist · Researcher · Teacher

PhD in Astrophysics, Instituto de Astronomía y Física del Espacio (IAFE), UBA-CONICET.
Teaching Associate (full-time) in Physics Laboratory at Universidad Nacional de Tres de Febrero (UNTREF).

TL;DR

Astrophysicist working on spectral diagnostics of stellar atmospheres, currently with a focus on lines sensitive to chromospheric activity. I develop reproducible Python tools for spectral I/O, preprocessing and analysis, and I apply unsupervised clustering methods to classify spectral-line responses to activity. My short-term objective is to transition these skills toward AI applications in astrophysics and pursue a postdoctoral position that combines machine learning and stellar/exoplanetary spectroscopy.

Current research & technical profile

  • Research focus: spectral line formation in semi-empirical stellar atmospheres; classification of spectral lines sensitive to chromospheric activity.

  • Methods: deterministic radiative-transfer modelling, custom spectral pipelines (I/O, convolution, remapping, normalization), and unsupervised clustering (k-means, hierarchical clustering, DBSCAN) applied to spectral-feature vectors.

  • Programming & tools:

    Python PyTorch TensorFlow scikit-learn NumPy Pandas AstroPy SQL

    (Environments: Anaconda / Spyder / VSCode)

  • Goal: apply this combined expertise in fundamental stellar physics and data analysis to the field of exoplanetary science. I'm currently deepening my knowledge in that field, and IA tools, to define a focused research project.

Selected works (recent → older)

Thesis: Nuevos modelos atómicos para modelos atmosféricos de estrellas con planetas — (PhD, IAFE-UBA). PDF + abstract (pag. 4 in english): https://bibliotecadigital.exactas.uba.ar/download/tesis/tesis_n7534_Peralta.pdf

What you will (soon) find here

I plan to publish a small, focused set of repositories that demonstrate reproducible pipelines and analysis:

  • jip-tools — compact Python utilities for spectral I/O and preprocessing (I/O, convolution, remapping, export/import).
  • spectral-clustering — scripts and notebooks showing clustering pipelines (k-means, hierarchical, DBSCAN) applied to spectral-feature vectors and the resulting group interpretation.
  • paper-figure-repro — minimal repo to reproduce one figure from a selected paper (script + small example data / synthetic generator).

These will be added progressively. If you are interested in a specific item, contact me (email above) and I will prioritize that repo.

Short note on license & citation (for future repos)

I typically use a permissive license (MIT) for code so others can reuse it easily; datasets may have separate restrictions. For academic reproducibility I add CITATION.cff when a release is published (optional for now).

Last updated: 2025-08-20

Pinned Loading

  1. grapako-grotrian-mgI-fig4-repro grapako-grotrian-mgI-fig4-repro Public

    Generate a Grotrian diagram for Mg I (Figure 4 from Peralta et al., 2023, A&A 676, A18).

    Python 1