I'm a Postdoctoral fellow at Max-Planck Institute for Astronomy (MPIA, Heidelberg). My current research is mostly oriented towards understanding star clusters in galaxies, and primarily focusing on low-mass clusters to eventually constrain the dissolution theory of star clusters. However, I also contribute to many data modeling, especially using probabilistic inferences in astronomy but also other domains of physics.
My current main project is the construction of Gaia hyrid catalogs, a project that will combine all ancillary data with the most up to date Gaia measurements to infer the most precise information of objects in the Gaia Mission Survey and ultimately the to understand our own galaxy.
A small selection of current projects and research.
The vast majority of clusters in the Universe are small, and it is well known that the integrated fluxes and colors of all but the most massive ones have broad probability distributions, owing to small numbers of bright stars.
Systematics and errors arising from the use of standard analysis methods, which are based on continuous population synthesis models are large. Systematic errors on ages and random errors on masses are large, while systematic errors on masses tend to be smaller.
We find that the cluster age distribution is consistent with being uniform over the past 100 Myr, which suggests a weak effect of cluster disruption within M31.Fouesneau et al., 2014
We find that the mass distribution of the whole sample can be well-described by a single power-law with a spectral index of −1.9 ± 0.1 However, we find that the present day mass spectral index varies significantly with environment with the shallower slope in the highest star formation intensity regions.Fouesneau et al., 2014
We currently work on infering the disruption processes and initial mass function with respect to environment and star formation intensity in M31
We currently work on infering the disruption processes and initial mass function with respect to environment and star formation intensity in M31 from the PHAT survey.
PyPegase is a stellar population synthesis suite in python that aims at generating integrated spectra of discrete populations. It may be easily used by anyone, familiar or not with spectral evolution questions. Further improvements will also be available as PEGASE is still under active development.
This code upgrades the latest version of the spectro-photometric model of galaxy evolution PEGASE.2 (Fioc and Rocca-Volmerange 1997) with a new initial mass function considerations which explicitly account for stochastic fluctuations due to finite and small number of stars in populations of stellar cluster scales.
The code sources, including input data, and stellar libraries are currently available on demand .
the Bayesian Extinction and Stellar Tool (BEAST) is a probabilistic approach to infering stellar parameters and line of sight attenuation from the dust attenuated photometric spectral energy distribution of an individual star, which accounts for observational uncertainties common to large resolved star surveys. Specifically, the inference model accounts for measurement uncertainties and any correlation between them due to photon noise, stellar crowding (systematic biases and uncertainties in the bias), and absolute flux calibration.
(check github for more) All codes should work with python 2.6+ and 3.
Fast algorithms to do statistics
This package is my current exploration on how to make fast statistics on big data. Functions are typically several orders of magnitude faster, or so they claim.
direct interface to the PADOVA/PARSEC isochrone
It compiles the URL needed to query the website and
retrives the data into a python table variable easy to
Works with all their available models.
SAMPy Client package providing a very small VO interactivity using the SAMP protocol. This allows anyone to easily send and receive data to VO applications such as Aladin, Topcat or DS9.EZMAP
make python parallel mapping even simpler Everything goes through the same command: map! which also includes a progress indicator.
No-U-Turn Sampler (NUTS) as in Hoffman and Gelman, (2011)Differential Evolution
Differential evolution optimization method by Storn and Price (1997)BGLS
Bayesian Generalized Lomb-Scargle periodogram as in described in Mortier et al. (2014).MPL contour add-on
a low-level contour computation for Matplotlib that could give me the polygones without the need to make an actual figure and extract the curves from the contour object.
|2014 - present||Postdoctoral at Max-Planck Institute for Astronomy (Heidelberg, Germany)|
|Gaia enabling science products - Hybrid catalogs / CU8 Validation|
|2011 - 2014||Postdoctoral at University of Washington (Seattle, WA)|
|The Panchromatic Hubble Andromeda Treasury (PHAT)|
|2007 - 2011||Ph.D. in Astronomy at University of Strasbourg (Strasbourg, France)|
|”Study of stellar cluster populations in galaxies: a Bayesian approach” under the supervision of Ariane Lancon.|
|2006 - 2007||Master degree at University of Strasbourg (Strasbourg, France)|
|Compact objects and high energy, inverse methods, galactic evolution, and ”big data”.|
|2004 - 2007||Engineer degree (ENSPS/Superior and National School of Physics of Strasbourg)|
|Fundamental physics, signal processing, data processing and databases.|
|Gaia Hybrid Catalogs|
|As part of the Gaia collaboration (DPAC-CU9), the hybrid catalog aim to provide specific catalogs of Gaia sources combined with other wavelengths and information available.|
|Automated detection of star cluster in surveys|
|Algorithm combining of photometric and velocity measurements with density profiles. (Fouesneau et al, in prep)|
|Star cluster disruption from the photometric end-point|
|Statistical modeling and measurement of cluster disruption effect on present day cluster populations. (Fouesneau et al, in prep)|
|Inferring Dust Distribution on 20pc scale from resolved stellar populations in M31|
|Statistical modeling and measurement of dust distribution from CMD fitting of resolved stars in M31 through PHAT survey. (Dalcanton et al, submitted)|
|Stellar object characterization from spectro-photometric measurements|
|Calibrating spectra with photometry in order to derive more reliable stellar parameters (Weisz et al, in prep)|
|Bayesian extinction and stellar parameters fitting code (BEAST)|
|Determination of stellar parameters and dust properties using individual stars (Gordon et al, in prep)|
|Characterization of Nuclear Deformations with AGATA|
|Statistical analysis of gamma detections and probabilistic modeling of uncertainties. Collaboration with Damian Ralet (PhD student, GSI)|
|Cross-correlation of inhomogeneous catalogs|
|Combining catalogs using positions and spectro-photometric measurements, Collaboration with Dr. François-Xavier Pinot (CDS, Observatoire de Strasbourg)|
|Machine learning to detect artifacts in astronomical images|
|Detecting effect of charge-transfer efficiency and cosmic rays in Hubble images Collaboration with Ms. Martina Unutzer and Prof. Magdalena Balazinska (University of Washington) conference poster|
|Percolation in Carbon Nanotube Networks|
|Statistical analysis of Carbon Nanotube Networks in the percolation context. Collaboration with Yann Leroy (ICube). paper|
|Photometry and Temporal analysis in the IRAC-CF|
|Master project at Harvard Smithonian Center for Astrophysics (Cambridge, MA) Photometry and temporal analysis in the photometric campaign of the Spitzer’s IRAC dark calibration field (IRACCF). Collaboration with Matthew Ashby (CfA) and Joseph Hora (CfA).|
|Automation of synoptic maps construction of the solar activity|
|Detecting and tracking solar features (spots, filaments, faculae) using multiwavelength imaging. Collaboration with Dr. Isabelle Scholl (ISU) and Dr. Jean Aboudarham, (LESIA/Paris Observatory) 2008AnGeo..26..243A|
|"Introduction to Bayesian statistics"|
|Lectures to Master student level, introducting the basics of statistical analysis in the most pragmatic way (if possible). online materials|
|"Introduction to MCMC and Nested sampling"|
|Improvised Blackboard lecture during the Gaia Challenge in 2014, covering the basis of Bayesian statistics and sampling for model fitting|
|"Introduction to python and object oriented programming"|
|Part of a workshop introducing python to astronomers and in particular basics of object oriented programming for science applications online materials|
|"Python for Astronomers and Curious"|
|Workshop introducing python to engineers, scientists from the observatory and university online materials|
|"Introduction to data reduction"|
|lectures to master of astrophysics students Cheat Sheet - Slides (french)|
|"Spectra of Stars to Stellar evolution in Layman's term"|
|Public talk reviewing our knowledge of stellar physics based on observations|
GESF2014 - Marseille, France, September - "Relation between environment and star cluster formation and evolution"
Aspen Center for Physics, June - "Observational constraints on star and cluster formation"
Strasbourg, France, March - "Uncertainties when inferring the stellar mass function"
STScI, Baltimore, BA, January - "Clues in Environmental influence in cluster disruption in PHAT"
AAS, Long Beach, CA, January - "Star Clusters in PHAT, Early Science Review", M31 Special Session
Sesto, Italy, July - "Star Clusters Populations in M31"
MPIA, Heidelberg, Germany, July - "Clusters Disruption from early science in PHAT"
AAS, Austin, TX, January - "Characterizing Cluster Populations in a Stochastic Regime"
Strasbourg, France, December - "PHAT an HST large survey of M31"
Strasbourg, France, March - "Python for Astronomers" tutorial
|2010||STScI, Baltimore, BA, May - "Estimating Cluster Properties in a Stochastic Context"|
Stockholm, November - "Unresolved Star Clusters Properties"
Next Generation Virgo Cluster Survey (NGVS), Paris, France, October - "Star cluster ages and masses analysis"
Johnson City, TN, July - "Bayesian Approach for Star cluster property determinations"
AAS, Passadena, CA, May - "Infrared-variable Objects in the IRAC-CF"
KITP, Santa Barbara, CA, January - "Stochastic fluctuations in cluster property analysis"
Sheffield, United Kingdom, July "Stochastic Population Synthesis"
Illkirch, France, April Stellar evolution in Layman’s term
Geneva, Switzerland, February Spectra of populations dominated by supergiants