Aldric Parent

TYPE

Academic Project

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CONTEXT

Paris Observatory

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YEAR

2025

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DURATION

3 months

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LOCATION

Meudon, FRANCE

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LINK

ProjeCT DETAILS

Context & Objectives

This project formed part of an advanced astrophysics unit focused on visible‑light observations and the end‑to‑end processing of scientific data. Nightly observing sessions (January–March, weather permitting) were conducted at the Meudon Observatory using 0.3–1.0 m class telescopes equipped with semi‑professional instruments. The principal objective was to acquire real observational experience and to master the complete workflow from planning measurements to producing calibrated data ready for scientific analysis.

Specifically, the project aimed to: (1) operate telescopes and instruments for imaging and spectroscopy, (2) implement standard calibration procedures for CCD and spectrograph data, and (3) develop reproducible Python pipelines to reduce and assess data quality.

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PROJECT DETAILS

Methodology & Observational Setup

I participated in observation planning (target selection, pointing, and scheduling) and in the night operations at Meudon. Instruments used included CCD cameras and a slit spectrograph; when available, speckle imaging setups were also explored. Practical tasks covered telescope pointing and celestial coordinates, focal and optical setup verification, and familiarization with instrument control software.

Observing campaigns were organized into multi‑night runs. For each target we recorded acquisition parameters (exposure times, filters, slit widths, binning), environmental metadata (seeing, airmass), and calibration frames (bias, darks, flats, arc lamps) required for robust reduction.

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PROJECT DETAILS

Data Reduction & Analysis (Python)

All reduction was performed in Python using standard scientific libraries. Key reduction steps implemented in reproducible scripts:

CCD imaging: bias subtraction, dark correction, flat‑fielding, cosmic ray removal, astrometric registration, and stacking.
Photometric considerations: estimation of the point spread function (PSF), background subtraction, and measurement of spatial resolution and SNR.
Spectroscopy: preprocessing (bias/dark/flat), spectral extraction, wavelength calibration using arc lamp exposures, and flux normalization.
Optional speckle processing: short‑exposure stack analysis, speckle reconstruction and estimation of high‑resolution PSF.

Output products were FITS files and analysis notebooks documenting each processing step, QA plots (histograms, PSF profiles, line profiles), and versioned Python scripts to ensure reproducibility.

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PROJECT DETAILS

Deliverables & Contributions

Deliverables produced for the portfolio:

Observation logs and acquisition protocols summarizing commands, instrument settings and safety notes.
A set of calibrated imaging and spectroscopic data products (bias‑subtracted, flat‑fielded, wavelength‑calibrated) ready for scientific use.
Python reduction pipelines and Jupyter notebooks with modular functions for common tasks (FITS I/O, calibration, extraction, plotting).
Quality assessment reports including PSF analyses, SNR estimates, and recommendations to improve future campaigns.

I contributed to designing the observing sequences, writing the reduction scripts, and producing the documentation and QA reports.

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PROJECT DETAILS

Skills Developed & Added Value

This project consolidated practical and methodological skills critical for observational astrophysics and instrumentation: telescope operation and pointing, CCD and spectrograph calibration, handling of FITS data, and development of reproducible Python pipelines (numpy, astropy, matplotlib and affiliated tools). I gained hands‑on experience in observational planning, night‑time operations, and diagnosing instrumental effects (bias, dark current, readout noise, PSF/seeing impacts).

The work demonstrates an ability to translate raw instrument outputs into scientifically usable data products, bridging instrumentation knowledge and software‑based data analysis — a valuable asset for ground‑based observing campaigns and space‑instrument validation alike.