Songnan QI

About me

I am a second-year Master's student in Astrophysics at the University of Geneva, specializing in "From Stars to the Universe". At the same time, driven by a strong curiosity and eagerness to explore diverse topics, I also attended all courses in the Exoplanets specialization. This interdisciplinary exposure has significantly enriched my academic perspective.

My research journey has spanned a wide range of topics, from stellar systems and exoplanetary dynamics to stellar evolution simulations and the observational analysis of distant galaxies. Engaging with such diverse projects has helped me develop both theoretical and data-driven approaches to research. (Explore my research projects to learn more about my work)
These experiences have also allowed me to refine my academic focus. Over time, I have come to recognize that my true passion lies in exploring the high-redshift universe, particularly the early formation of massive galaxies and the physical conditions that shaped the cosmos during its first billion years.

My research interests:

• Formation and evolution of massive galaxies at high redshift
• Molecular gas content and star formation histories in early galaxies
• Cosmic reionization processes
• Multi-wavelength spectroscopic analysis

Research experience

Master's thesis

Studying the Efficient Formation of the Most Massive Galaxies in the Early Universe with JWST and ALMA

09.2025 - present

Under the supervision of Dr. Mengyuan Xiao, Dr Miroslava Dessauges and Prof. Pascal Oesch

Abstract:
This project aims at studying the rapid assembly of massive galaxies in the early Universe (z > 4) through multi-wavelength analysis of JWST spectroscopy and ALMA FIR observations. By deriving physical properties (stellar masses, gas and dust content, SFRs and gas depletion timescales), I shall be able to characterize star formation efficiency and quenching mechanisms in dust-obscured, star-forming, and quiescent massive systems.

Previous research projects

Searching for broad-line emitting galaxies with JWST FRESCO data

02.2025 - 06.2025

Under the supervision of Ph.D candidate Alba Covelo Paz and Prof. Pascal Oesch

• Developed a Python pipeline to identify broad-line emitting galaxies from JWST FRESCO NIRCam slitless spectroscopic data by multi-component Gaussian fit;
• Queried MAST to match selected candidates with HST observations, verifying the presence of broadened emission lines;
• Analyzed the physical properties of the identified galaxies, including black hole masses and stellar masses;
• Compared the BH-to-stellar mass ratios with relations established by published papers and explained the offsets.

Evection resonance in the hierarchical restricted 3-body problem

09.2024 - 12.2024

Under the supervision of Dr. Jérémy Couturier and Prof. Adrien Leleu

• Simplified the Hamiltonian of the massless particle in a hierarchical restricted problem to an one-degree-of-freedom, autonomous, single-parameter and dimensionless one;
• Searched for the theoretical evection resonance by analysing the bifurcations of the phase space topology;
• Simulated the Sun-Earth-Moon system and the exoplanetary system of TOI-6303 b to confirm the existence of evection resonance with the N-body software NcorpiON.

MCMC algorithm application: Estimating the mass of exoplanets

10.2024 - 12.2024

As part of the course ‘Astrophysics & Data Science’

• Computed the maximum likelihood estimates of the physical parameters of a planet;
• Coded the Metropolis-Hastings algorithm;
• Computed the posterior distributions of the orbital parameters of a planet and the interval estimates of its mass.

Neural Network from scratch

10.2024 - 12.2024

As part of the course ‘Astrophysics & Data Science’

• Implemented all the functions needed to create a neural network without using any package besides numpy and matplotlib.pyplot;
• Trained the network with two datasets: a 2-class problem and a 3-class problem;
• Adjusted the number of neurons per hidden layer and the number of hidden layers to optimise the accuracy of the classification.

Population synthesis of wind-fed BH-HMXBs

11.2023 - 03.2024

Under the supervision of Dr. Zepei Xing and Prof. Marc Audard

• Simulated 500000 binaries by using the binary population synthesis code POSYDON;
• Selected wind-fed high-mass X-ray binaries with a black hole (BH-HMXBs) among the generated dataset;
• Analysed the main features of the binary population that evolved into wind-fed BH-HMXBs and compared them with observational data;
• Studied the impact of metallicity on the simulation outcome.

Curriculum Vitae

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Contact

You can reach me via email at Songnan.Qi@etu.unige.ch & qisongnan@outlook.com
Or by telephone at +41786339109

Department of Astronomy
University of Geneva
Chemin Pegasi 51, CH-1290 Versoix, Switzerland

Elements

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