Research
The interstellar medium (ISM)
The ISM is a complex, multiphase environment whose structure and chemistry are shaped by processes influenced by the local radiation field, gas composition, and dust properties. It hosts the baryon cycle, where atomic gas assembles into cold molecular clouds that collapse to form stars. These stars exert feedback through radiation, winds, and supernovae, producing heavy elements and recycling material into the ISM.
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I recently joined the PHANGS collaboration. The survey has been obtaining high angular resolution images of nearby galaxies across the electromagnetic spectrum to constrain the properties of the ISM and the baryon cycle. My research focuses on studying the properties of the small carbonaceous dust grains, PAHs, and how they relate to the properties of the ionized gas.
Figure by the PHANGS collaboration showing NGC 1566 color composites using HST observations (lower left) and JWST (upper right). The same structures that show up as dark brown and obscure the stellar emission in optical in the HST image, show up in emission (shown with bright orange) in the JWST image. This emission originates from PAHs.
Post starburst E+A galaxies
Post starburst E+A galaxies are believed to be a short phase in galaxy evolution, connecting gas-rich major mergers (observed as ULIRGs) with quiescent ellipticals. Simulations suggest that during a major merger, a starburst is triggered and gas is funneled to the vicinity of the supermassive black hole, triggering an AGN. The AGN then launches powerful outflows that quench the starburst abruptly. This results in a post starburst galaxy that will later evolve to a quiescent elliptical.
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In my research, I use multi-wavelength observations to study the star formation properties and AGN properties in post starburst E+A galaxies. I use dedicated observations in optical and mm (KCWI, MUSE, NEOMA, and ALMA) to study the properties of the ionized, neutral, and molecular gas in these systems, and to look for gas outflows.
Figure from Baron et al. (2021), where we used far infrared observations from IRAS to study the star formation properties of post starburst E+A galaxies. This diagram shows the star formation rate (SFR) versus the stellar mass, where empty symbols represent upper limits. It suggests that, contrary to the common belief, Many E+A galaxies are not fully-quenched, with some of them hosting starbursts.
The impact of AGN on the evolution of their host galaxies
Active Galactic Nuclei (AGN) are supermassive black holes located in the center of galaxies, which accrete gas through an accretion disk. The radiation originating in the accretion disk is believed to produce gas outflows. These winds can reach galactic scales, and may have a significant impact on the evolution of their host galaxy.
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In my research, I use large public surveys (e.g., the SDSS) and conduct dedicated observations using various instruments (KCWI, MUSE, NOEMA, and ALMA) to study these outflows. Using photoionization models I construct (with CLOUDY), I estimate the properties of these winds, such as the mass and momentum that they carry. My goal is to examine whether these winds can have a dramatic impact on their hosts.
Figure from Baron et al. (2018), where we used KCWI/Keck to map the ionized outflows in a system of two merging galaxies. The stellar emission is color-coded with black-white, and the gas emission with yellow-purple. The wind extends to 17 kpc, and most of it had left the host galaxy.
Facilitating new discoveries in astronomy with Machine Learning
Astronomy is going through a revolution. As astronomical surveys become larger and deeper, we face unprecedented data volumes which challenge the classical methods with which we extract information and make new discoveries. The challenge is not only due to the data volumes, but also due to their complexity. How can we extract novel information and detect new physical phenomena in these large and complex datasets?
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In my research, I develop novel techniques to perform unsupervised anomaly detection and dimensionality reduction of large and complex datasets. The application of these tools has already led to several new discoveries in astronomy (see here and here) and geology (see here).
Figure from Baron & Ménard (2020), where we presented the Sequencer algorithm. This algorithm is designed to automatically detect sequences in datasets. If a sequence exists, the Sequencer reorders the objects in the sample according to the detected sequence.
The left panel of the figure shows an input dataset, where each row represents a one-dimensional object, and each pixel is color-coded according to the intensity in this dimension. The right panel shows the Sequencer output. The Sequencer detected a significant one-dimensional sequence (Einstein's face) and reordered the data accordingly.
Recorded Talks
Below you can find my recorded talks.
Finding simple structures in complex astronomical datasets.
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Astroinformatics 2018.
Heidelberg.
Extracting the Main Trend in a Data Set: The Sequencer Algorithm.
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AAS Journal Author Series @ YouTube.
November, 2021.