About Me
Researcher on the intersection of astronomy, statistics and computer science.
Astrophysicist, Statistician, Computer Scientist
In Astrophysics, I research growing black holes, their demographics and their immediate environments. Before matter falls into a black hole, it swirls around it and heats up, creating radiation. This makes distant galaxies shine bright as Active Galactic Nuclei (AGN). I observe this radiation because it tells us how much the black hole is currently growing.
My research focuses on the time when this growth occured most (z=0.5-3). For my PhD project I reconstructed the total growth of black holes over cosmic time using a large sample of distant AGN (2000, CDFS, COSMOS, AEGIS, XMM-XXL). This also requires a good understanding of the observations (astro-statistics) and the obscuration of AGN.
In most AGN, much of the radiation is swallowed by thick columns of gas and dust near the black hole. In my research I try to understand these gas clouds, in particular their location, extent/covering and relation to the black hole. During my PhD I investigated different obscurer geometries. I could also place the best constraints to date on the intrinsic covering fraction of the obscurer (77% Compton-thin, 38% Compton-thick obscured). To understand the obscurer is crucial to correctly infer the intrinsic emission and therefore the black hole growth. Also, the mechanisms making these clouds is currently unknown.
I have published in Statistics, where I focus on nested sampling Monte Carlo algorithms and their performance. Population studies (hierarchical Bayesian inference) interests me, as well as helping others with statistics problems.
I write a lot of software for various purposes (>100 github repos), many of them are also used by others: I am the author of the PyMultiNest package, and the Bayesian X-ray Astronomy (BXA) code. I think daily about new algorithms and solutions.
Since I begun publishing in 2014 my papers have received more than 80 citations. You can find a full list on ADS. Here I mention some aspects for each of the papers:
Year, Title, Authors | Astronomy aspects | Statistics aspects | Link |
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Buchner et al. (2015): Obscuration-dependent evolution of Active Galactic Nuclei | This luminosity-function type study reconstructs the distribution and evolution of AGN in obscuration and luminosity using a novel robust non-parametric approach. We constrain the importance of Compton-thick AGN to the accretion history of the Universe, and the evolution of the obscured fraction.
Supplimentary material: Video explanation • XLF as table for download: Space density of AGN as f(Lx, z, NH) • Total Space density of AGN as f(Lx, z) • Plot for comparison. | Cencored inference about the properties of a population. Bayesian field inference. Reconstructs a 3d smooth function under selection effects without assuming a shape but only smoothness. Uses Stan to reconstruct the growth of black holes over cosmic time. | |
Buchner et al. (2014): X-ray spectral modelling of the AGN obscuring region in the CDFS: Bayesian model selection and catalogue | Comparison of various models for the obscurer in AGN. Through model comparison between a disk, sphere and toroidal geometry, with the latter preferred, the obscurer was found to be extended but not fully covering, even for the Compton-thick sub-population.
Supplimentary material: Vizier CDFS catalogue • BXA documentation | Presents several advancement in X-ray spectral analysis methods: Bayesian parametric analysis, comparison of models, Goodness-of-Fit, nested sampling vs. MCMC, vs likelihood contour error estimation | |
Buchner (2014): A statistical test for Nested Sampling algorithms | Statistics paper: Evaluation of MultiNest and similar algorithms
Supplimentary material: Code for RadFriends and UltraNest on Github. | Analyses several nested sampling algorithms (e.g. MultiNest) for flaws using a new statistical test. |
I am an active member on the Astrostatistics Facebook group, where we answer Astronomers questions about statistics, data mining, machine learning, programming, etc. I regularly answer questions, review papers on their use of statistics and write mini-tutorials. Similarly, I help out my colleagues with statistics questions in our astronomy institute.
I have written a minimal statistics checklist to help you identify and fix common errors/misinterpretation in your analysis, or of a paper you are refereeing.
You can find my statistics software and papers in the previous and next sections.
I write software to make my life and the life of my colleagues easier. Perhaps you can take advantage of it too:
Name | Description | Link |
---|---|---|
PyMultiNest** | Pythonic Bayesian inference and visualization for the MultiNest Nested Sampling Algorithm or MCMC. See also the tutorial, RMultiNest. | |
BXA** | Bayesian X-ray analysis (nested sampling for Xspec and Sherpa) | |
UltraNest | Pythonic Nested Sampling Development Framework & UltraNest | |
simbad2kstars | Simbad to KStars import | |
SysCorr | Bayesian correlation swiss army knife | |
jbopt | A interface definition that lets you plug and play many algorithms against your likelihood function, including optimisation algorithms in scipy, OpenOpt, MultiNest, emcee, etc. | |
LightRayRider | Ray tracing of hydrodynamic simulations to compute column densities | |
languagecheck | Improve the language of your paper before submission | |
test-calculator | Online Scientific computations (with javascript) | |
athena-point-source-simulator | Simulating Compton-thick AGN for Athena | |
intersection | Ray tracing / Line intersection formulas for various 2d and 3d objects | |
spuren | A Desktop search engine kept fast and simple | |
DHCProbe | Send a DHCP request to DHCP server to check its configuration | |
imagehash** | A Python Perceptual Image Hashing Module | |
zwicky-morphological-analysis | Zwickys Morphological Analysis implemented in Python |
** very popular