| Presentations |
Alex Blocker (Harvard U)
6 Sep 2011 |
- A taste of astrostatistics: problems, opportunities, & connections
- Abstract:
Astrostatistics is a vibrant, tight-knit field with more open
problems than statisticians to tackle them. These range from very
applied, such as understanding the workings of space telescopes,
to fundamental questions of statistical inference, and sophisticated
computation is the order of the day. The latter is particularly
true as new instruments generate huge volumes of data.
I will provide a sampling of two projects from astrostatistics:
inferring the brightness of faint galaxies using the Chandra space
telescope, and finding unusual events within millions of astronomical
time series. These presented major inferential challenges in
radically different ways. Addressing them took a combination of
statistical modeling, scientific knowledge, and computational
finesse.
Finally, I will share some surprising connections between astrostatistics
and my work in biology. Biology appeared to lead astronomy in data
analysis for many years, but the fields are now coming full circle.
The newest forms of biological data share many features with modern
astronomical data; there is a great potential for "methodological
arbitrage" here for graduate students willing to dive into
astrostatistics.
- Slides [.pdf]
-
|
Astro Projects for Statistics
20 Sep 2011 |
- Projects, problems, and demos
- Doubt: How Do I Know if that is a Real Feature in My Image? (Alanna C)
- Timing analysis of grating data (Vinay K)
- Real time feature detection and classification (Pavlos P)
- Issues in modeling the X-ray data (Aneta S)
- Quasar clustering project (Brandon K)
- Simplicity: Bayesian Energy Quantiles, or Quick Non-parametric way(s) to incorporate Higher Dimensional Data (Alanna C)
- Source detection in 4D (Vinay K)
- Physics demos: poisson, atomic lines, dispersion spectra (Alanna C)
-
|
Group
11-13 Oct 2011 |
- Projects
- Tuesday 11 Oct
- 9:30a - 10:15a: pyBLoCXS (at SciCen 706)
- 10:15a - 11:15a: proposal
- 11:30a - 12:15p: new projects
- 12:15p - 1:00p: Bayes Factors
- 2:00p - 3:00p: Full Bayes Calibration Uncertainties
- 3:00p - 3:30p: 2D Cal Uncertainties and SCA
- Wednesday 12 Oct
- 10:00a - 10:30a: SolarStat (at CfA Fishbowl)
- 10:30a - 11:15a: Sunspot Classification
- 11:15a - 11:45a: Sunspot Cycles
- 1:00p - 2:00p: Timing analysis with grating data (at CfA M-240)
- 2:00p - 2:45p: Solar DEM features
- 2:45p - 4:00p: computing
- Thursday 13 Oct
- 10:00a - 11:00a: pySALC (at CfA M-240)
- 11:00a - 2:30p: proposal
-
|
Brandon Kelly (CfA/UCSB)
25 Oct 2011 |
- Investigating Star Formation through Hierarchical Bayesian Modeling of Emission from Astronomical Dust
- Abstract: Astronomical dust plays an important role
in the formation of stars and planets. Recently launched observatories,
such as Herschel and Planck, are providing observations which provide
important constraints on the properties of astronomical dust.
However, the traditional least-squares analysis used by astronomers
is highly inefficient for this problem, and leads to biases and
incorrect conclusions. In this talk I will discuss a hierarchical
Bayesian approach to deriving the physical parameters of astronomical
dust, as well as the distribution of these parameters. I will also
discuss an ancillarity-sufficiency interweaving strategy for boosting
the efficiency of the MCMC sampler. Finally, I will present results
from our model as applied to a nearby star-forming region. The
results obtained from our Bayesian approach lead to opposite
scientific conclusions compared to those obtained from the least-squares
analysis. The results obtained from our Bayesian analysis are
consistent with astrophysical theories of dust formation, while the
least-squares results are inconsistent with astrophysical theory.
- Slides: [.pdf] | [.ppt]
-
|
Raffaele D'Abrusco (CfA)
1 Nov 2011 |
- Knowledge Discovery workflows for exploration of complex multi-wavelengths
astronomical datasets. Application to CSC+, a sample of AGNs built on the Chandra Source Catalog
- Abstract:
A complete understanding of all astronomical sources requires a global
multi-wavelength approach and that, at the same time, the availability of
large surveys of the sky in different spectral regions has propelled the
aggregation of massive and complex datasets. The traditional approach to
data analysis that involves well informed testing of different models
cannot make justice of the richness of the these new datasets and, in some
sense, of the intrinsically peculiar type of knowledge therein contained.
Knowledge Discovery (KD) techniques, while relatively new to astronomy,
have been successfully used in several other disciplines, from finance to
genomics, for the determination of complex or simple but yet unseen
patterns in large datasets.
In this talk I shall describe CLaSPS, a method for the characterization of
the multi-dimensional astronomical sources, based on KD unsupervised
clustering algorithms that are used to determine the spontaneous
aggregations of sources in the high-dimensional space generated by their
observables. Then, a data-driven criterion is applied to pick the most
interesting clusterings in terms of astronomical properties of the sample.
I will discuss the application of this method to a sample of optically
selected AGNs with X-ray observations in the Chandra Source Catalog and
other multi-wavelength data, which is representative of the VO-powered
inhomogeneous astronomical dataset that will be more and more common in the
future. The goals of this project are to test known correlations, possibly
determine new patters and establish diagnostics for an improved
classification of X-ray selected AGNs with multi-wavelength observations.
As an example of unknown low-dimensional patters. I will also briefly
discuss a recent result on Blazars which is by-product of the application
of CLaSPS to a sample of AGNs with multi-wavelength data.
- Slides [.pdf]
-
|
Ed Turner (Princeton)
15 Nov 2011 |
- A Bayesian Analysis of the Astrobiological Implications of the Rapid
Emergence of Life on the Early Earth
- Abstract:
Life arose on Earth sometime in the first few hundred million years
after the young planet had cooled to the point that it could support
water-based organisms on its surface. The early emergence of life on
Earth has been taken as evidence that the probability of abiogenesis
is high, if starting from young-Earth-like conditions. This argument is
revisited quantitatively in a Bayesian statistical framework. Using
a simple model of the probability of abiogenesis, a Bayesian estimate of
its posterior probability is derived based on the datum that life emerged
fairly early in Earth's history and that, billions of years later,
sentient creatures noted this fact and considered its implications.
Given only this very limited empirical information, the choice of
Bayesian prior for the abiogenesis probability parameter has a very
strong influence on the computed posterior probability. In particular,
although life began on the Earth quite soon after it became habitable, that
fact is statistically consistent with an arbitrarily low intrinsic
probability of abiogenesis for plausible uninformative priors and,
therefore, with life being arbitrarily rare in the Universe. The
presentation will emphasize generic statistical properties of problems of
this general character, which occur in cosmology and many other areas
of science, as well as in the context of abiogenesis.
- Slides [.pdf]
-
|
Group
29 Nov 2011 |
- 20 Questions
- Wherein stats grad students ask questions of astronomers, who,
if they can't answer the question, will get to ask back a question
on statistics. Also, demos.
-
|
Jin Xu (UC Irvine)
7 Feb 2012 |
- New Results of Fully Bayesian
- slides [.pdf]
-
|
Tom Loredo (Cornell)
15 Feb 2012
3:15pm - 4:30pm
Pratt Conference Room at CfA |
- Adaptive scheduling of exoplanet observations via Bayesian adaptive exoploration
-
-
|
Group
16-17 Feb 2012 |
- Solar-Statistics mini Workshop
- Thursday, Feb 16 (@ Pratt)
- 2:00pm - 3:45pm: Stats Tutorial
- 4:15pm - 6:00pm: Solar Tutorial
- Friday, Feb 17 (@ Phillips)
- 9:00am - 10:30am: Feature Recognition
- 11:00am - 12:30pm: Thermal Structure
- 2:00pm - 3:30pm: Multi-D Joint Analysis
- 4:00pm - 5:30pm: Massive Data Streams
-
|
Alex Blocker (Harvard)
21 Feb 2012 |
- Discussion of Maximal Information Coefficient
-
-
|
Paul Baines & Irina Udaltsova (UC Davis)
6 Mar 2012 |
- logN-logS
-
|
Andreas Zezas (Crete)
20 Mar 2012 |
- Adaptive Smoothing
- The goal is to derive the ideal tool for quick astronomical analysis: a statistically principled, adaptively smoothing, flux-conserving, semi-parametric tool that works in 2-D, on Poisson data, and runs reasonably quickly. Some useful references to read up on:
-- ASMOOTH: A simple and efficient algorithm for adaptive kernel smoothing of two-dimensional imaging data, Ebeling, H., White, D.A., & Rangarajan, F.V.N., 2006, MNRAS, 368, 65 [arXiv:0601306]
-- csmooth, CIAO ahelp page, cxc/ciao/ahelp/csmooth
-- Multiple Testing of Local Maxima for Detection of Unimodal Peaks in 1D, Schwartzman, A., Gavrilov, Y., & Adler, R.J., 2011 [.pdf]
-- Multiple Testing of Local Maxima for Detection of Peaks in ChIP-Seq Data, Schwartzman, A., Jaffe, A., Gavrilov, Y., & Meyer, C.A., 2011, HU Biostatistics Working Paper Series, 133 [.pdf]
-- A Wavelet-Based Algorithm for the Spatial Analysis of Poisson Data, Freeman, P.E., Kashyap, V., Rosner, R., & Lamb, D.Q., 2002, ApJS, 138, 185 [.pdf]
-
|
Omiros Papaspiliopoulos (U Pompeu Fabra)
10 Apr 2012 |
- MCMC
-
|
Wang La Zhi (Harvard)
15 May 2012 |
- Luminosity Functions
-
|
|
-
|