Jindra Gensior

Jindra Gensior

Astrophysics PhD student


I am a fourth year PhD student at Heidelberg University working with Dr. Diederik Kruijssen in the MUSTANG group. I study the influence of galactic dynamics on star formation from a numerical perspective, with a particular focus on the interplay between galactic dynamics, star formation, and quenching: trying to understand whether star formation can be a rate limiting step in the baryon cycle of galaxies and how empirically motivated sub-grid models for star formation impact galaxy formation and evolution.

If you would like to get in touch, send me an email: j.gensior (at) uni-heidelberg.de

Emmy Noether
Universität Heidelberg
ZAH
MUSTANG

Research interests


In keywords, my research interests can be summarised as:
star formation | galaxy formation and evolution | interstellar medium | numerical modelling | supermassive black holes/active galactic nuclei

Below is a more in depth description of my past and current research. Each of the images can be clicked on for more information.

What regulates star formation?

Stars form in collapsing clouds of cold gas, so in the most simplistic way, how much of this gas is available in a galaxy sets how many stars are formed. This is supported by observations of galaxies, where the star formation rate (SFR) is well correlated with the surface density of gas. However, there are galaxies in where the SFR is low despite having plenty of cold gas. This raises the question what, besides the presence of gas, regulates the star formation rate?

Dynamical suppression

One of the things that could influence the SFR is the galactic environment, i.e. the specific conditions of the cold gas. Most of the galaxies with a suppressed SFR are early-type galaxies, which have the majority of their stellar component in a central spheroid. This spheroid induces shear in the gas, especially close to the galactic centre, which could be sufficient to stop the gas from collapsing and forming stars. This is known as dynamical suppression (or morphological quenching).
For my first paper, I investigated the effect of dynamical suppression with a suite of 10 isolated galaxy simulations. These galaxies all had the same stellar mass and low gas fraction initially, the only difference being the distribution of the stars ranging from completely disc- to spheroid-dominated.

A new sub-grid model for star formation

Unfortunately, simulations of entire galaxies (kpc-scales) cannot resolve the formation of stars individually, and instead require a sub-grid model to determine which gas forms stars instead. Traditionally, following the observations, the SFR is related to the gas density via a constant efficiency factor, SFR = SFE x gas density. However, this doesn't work for galaxies which have a suppressed SFR despite having the same gas densitites as star-forming galaxies. Instead a sub-grid model that takes into account the galactic environment, and specifically the dynamics of the gas (as these will be affected by shear) is required.

Schematic representation of the sub-grid algorithm for identifying overdensities in the simulation. First a number of nearest neighbours around the central gas cell are found (panel a), this region is then extended (b). We then iterate until the length scale on which the density changes (cyan line) is matched by the length scale on which it is calculated (purple line). Once the purple and cyan lines converge, the overdensity is successfully identified. (see <a href='https://ui.adsabs.harvard.edu/abs/2020MNRAS.495..199G/abstract' class = 'jg-hl-white'>Gensior, Kruijssen & Keller 2020a</a>.)

Turbulent star formation theory predicts that the SFE depends on the virial parameter of the gas (how bound is a gas cloud, or alternatively, what is the ratio of gravitational potential energy to turbulent kinetic energy of the cloud). This is suitable to test the effect of dynamical suppression, but requires a way to calculate the virial parameter of the gas on the cloud scale. For this purpose, I developed an algorithm that self-consistently identifies overdensities in the gas.

A couple of the key results from simulations with the virial parameter-dependent SFE are shown in the figures below. We found that the shear induced by the spheroidal component suppresses the fragmentation of the interstellar medium in the centre of bulge-dominated galaxies, which in turn leads to a strong suppression of star formation.

Gas surface density maps for each of the 10 different galaxies in <a href='https://ui.adsabs.harvard.edu/abs/2020MNRAS.495..199G/abstract' class = 'jg-hl-white'>Gensior, Kruijssen & Keller 2020a</a>. The galaxies with no or a weak bulge component have a very substructured central interstellar medium, but with increasing bulge strength (going diagonally from the top left to the bottom right), galaxies host smooth central gas reservoirs. This is a direct consequence of the suppression of fragmentation through the shear induced by the bulge component of the galaxy.
Plots showing the <a href='https://ui.adsabs.harvard.edu/abs/2020MNRAS.495..199G/abstract' class = 'jg-hl-white'>Gensior, Kruijssen & Keller 2020a</a> simulation data overplotted on the <a href='https://ui.adsabs.harvard.edu/abs/2017ApJS..233...22S/abstract' class = 'jg-hl-white'>xCOLDGASS</a> and <a href='https://ui.adsabs.harvard.edu/abs/2018MNRAS.476..875C/abstract' class = 'jg-hl-white'>xGASS</a> survey data. In the specific star formation rate (sSFR, star formation rate normalised by the stellar mass) - galaxy stellar mass plane on the left, and as offset from the star forming main sequence as a function of central stellar surface density on the right. The left panel demonstrates that dynamical suppression can cause an SFR suppression of an order of magnitude for a spheroid-dominated galaxy. The right panel shows that the amount of star formation suppression is correlated with the central stellar surface density, i.e. bulge strength. The observations follow the same qualitative trend.

How does dynamical suppression influence the galaxy population?

Whether dynamical suppression is effective depends on what dominates the local gravitational potential. Therefore, the gas fraction of galaxies must play an important role, too, next to the central stellar surface density. To explore this in Gensior & Kruijssen 2020b, we simulated 3 isolated galaxies covering the parameter space of disc-dominated to spheroidal at different gas fractions between 1-20%.
We found that the lower the gas fraction, the stronger the dynamical suppression of star formation. However, there is a critical gas fraction, above which dynamical suppression becomes ineffective. Combining a fit to the simulations with observational scaling relations for the evolution of the gas fraction, stellar mass and SFR over cosmic time, we predict that dynamical suppression could be a dominant effect at high galaxy stellar masses and low redshifts, as shown below.

Plots showing the <a href='https://ui.adsabs.harvard.edu/abs/2020MNRAS.500.2000G/abstract' class = 'jg-hl-white'>Gensior & Kruijssen 2020b</a> simulation data, colour-coded by their gas fraction, overplotted on the <a href='https://ui.adsabs.harvard.edu/abs/2017ApJS..233...22S/abstract' class = 'jg-hl-white'>xCOLDGASS</a> and <a href='https://ui.adsabs.harvard.edu/abs/2018MNRAS.476..875C/abstract' class = 'jg-hl-white'>xGASS</a> survey data. In the specific star formation rate (sSFR, star formation rate normalised by the stellar mass) - galaxy stellar mass plane on the left, and as offset from the star forming main sequence as a function of central stellar surface density on the right. At high gas fractions, the star formation rate is unaffected by differences in the gravitational potential. The lower the gas fraction, the stronger the effect of dynamical suppression.
Redshift and stellar mass range for which galaxies are predicted to be affected by the dynamical suppression of star formation: At a given stellar mass, the black line indicates the redshift where the <a href='https://ui.adsabs.harvard.edu/abs/2018ApJ...853..179T/abstract' class = 'jg-hl-white'>typical observed gas fraction</a> and the gas fraction required for dynamical suppression are equal. Galaxies below the line (grey-shaded area) are predicted to experience dynamical suppression, i.e. there the cloud-scale physics of star formation are predicted to regulate the baryon cycle and drive galaxies off the star formation main sequence. Coloured lines show the stellar mass growth histories of galaxies as predicted by <a href='https://ui.adsabs.harvard.edu/abs/2018MNRAS.477.1822M/abstract' class = 'jg-hl-white'>Moster et al. 2018</a> and <a href='https://ui.adsabs.harvard.edu/abs/2019MNRAS.488.3143B/abstract' class = 'jg-hl-white'>Behroozi et al. 2019</a> (see the legend). Plot from <a href='https://ui.adsabs.harvard.edu/abs/2020MNRAS.500.2000G/abstract' class = 'jg-hl-white'>Gensior & Kruijssen 2020b</a>.

The WISDOM of power spectra

The galaxies in the Gensior, Kruijssen & Keller 2020a simulations not only manage to reproduce the observed suppression of star formation in spheroid-dominated galaxies, but also the smooth central gas reservoir. In my current project I'm comparing the structure of this central gas reservoir my simulations to galaxies from the WISDOM (mm-Wave Interferometric Survey of Dark Object Masses) project using power spectra.

1D radial power spectrum slope of the <a href='https://ui.adsabs.harvard.edu/abs/2020MNRAS.495..199G/abstract' class = 'jg-hl-white'>Gensior, Kruijssen & Keller 2020a</a> galaxies' central gas reservoir as a function of central stellar surface density. The effect of the central stellar surface density on suppressing fragmentation in the gas is evident in its correlation with the power spectrum slope. The more fragmentation is suppressed, the steeper the power spectrum. (Gensior et al. 2020c, in prep.)
Virial parameter and gas densities for stars that formed in one of the cosmological zoom-in simulations of the EMP-Pathfinder (Reina-Campos, ..., Gensior et al. in prep.) suite. Stars form under very different conditions depending on the sub-grid model. When the star formation efficiency (SFE) is constant stars form in less dense and much more unbound gas. (Gensior et al., in prep.)

How does an environmentally-dependent SFE affect galaxy formation and evolution across cosmic time?

Excellent question! This is something that can be explored with the EMP-Pathfinder (Reina-Campos,...,Gensior et al. in prep.) cosmological zoom-in simulations. Each of the 10 galaxies in the sample is simulated once with a constant SFE and once using my dynamics-dependent sub-grid model for star formation. A sneak peak at the difference the star formation model causes in the gas that formed stars is shown above.

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Curriculum Vitae


Below is a shortened version of my CV, please contact me for the full one.

Academic positions and education

2017 - present: PhD Student in the IMPRS programme at Heidelberg University, Germany 2013-2017: MPhys with First Class Honours Astrophysics, University of Edinburgh, UK Summer 2016: Research internship at the Max Planck Institute for Extraterrestrial Physics, Garching, Germany Summer 2015: Research internship at the Max Planck Institute for Radio Astronomy, Bonn, Germany

Prizes

Talks

This is a list of my contributions to conferences and seminars:

Publications


An up-to-date list of my refereed and published publications can be found on ADS.
Refereed publications:

Publications close to submission:

Outreach


I sometimes combine my two favourite pastimes, astrophysics and dancing, to explain complex astrophysical processes by breaking them down into simple geometrical patterns and movements. For example, I have written Stardust, a dance about the life-cycle of stars, in collaboration with Science Ceilidh, and Dustborne about the work I did for my masters project (star formation and some interstellar medium chemistry).
Since I also really enjoy talking about all things astro, I have given a few outreach talks, e.g. at the Big Bang Bash of the Edinburgh International Science Festival in 2016.

Contact me


You can contact me at: j.gensior (at) uni-heidelberg.de

Astronomisches Rechen-Institut
Mönchhofstraße 12-14
69120 Heidelberg, Germany