Research
- ACT DR6 non-gaussian simulations
2024/02/19 - Results (maps, spectra and MCMC) of non-gaussian simulation for ACT DR6
CMB maps spectra likelihood cobaya python - Variations of fundamental constants such as \(m_e\) or \(\alpha_\text{EM}\)
2023/10/03 - Studying variations of fundamental constants in the context of CMB analysis
https://gitlab.in2p3.fr/xgarrido/class_varconst
CMB likelihood class cobaya python - SPT-SZ results
2022/10/03 - Checking & trying to reproduce Reichardt et al. results
https://gitlab.in2p3.fr/xgarrido/sptsz
CMB likelihood cobaya python
Teaching
- Particle & Nuclear physics
2023/10/20 - French classes of particle and nuclear physics given at the Master 1 of Fundamental Physics in the University of Paris-Saclay
https://gitlab.dsi.universite-paris-saclay.fr/xavier.garrido/master_npp_teaching
particle physics nuclear physics magistère master - Special relativity
2023/10/19 - French classes of special relativity given at the Licence 3 of Fundamental Physics in the University of Paris-Saclay
https://gitlab.dsi.universite-paris-saclay.fr/xavier.garrido/licence_relativity_teaching
special relativity magistère licence - Electromagnetism
2023/10/18 - French classes of classic field theory given at the Licence 3 of Fundamental Physics in the University of Paris-Saclay
https://gitlab.dsi.universite-paris-saclay.fr/xavier.garrido/licence_em_teaching
electromagnetism magistère licence - ecandidat
2023/08/28 - An interactive visualization of the number of candidates for Magistère of Fundamental Physics of Paris-Saclay
https://gitlab.com/xgarrido1/mag-ecandidat
magistère ecandidat jupyter
Misc.
- Mail activity chart
2023/10/30 - An analysis of mail sent & received over time
jupyter visualization plotly - Garmin watches report
2023/09/07 - An analysis of sports activities and wellness reports from my Garmin watches
https://gitlab.com/xgarrido1/garmin_data
jupyter garmin wellness - noisy planet
2021/01/15 - A python implementation of noisy planet. I recently came across this interesting post and I wanted to replicate the whole thing in
pythonwithin ajupyter notebook.
jupyter perlin noise