AstroStat Talks 2021-2022
Last Updated: 20220517

International CHASC AstroStatistics Centre

Topics in Astrostatistics

AY 2020-2021

Archive


Schedule Tuesdays Noon - 1:30PM Eastern Time
Location Remote



Presentations
Lucas Makinen (Sorbonne & CfA)
Sep 14 2021
Noon EDT
Zoom
Lossless Neural Compression for Cosmological Simulations: How to compress a universe into a handful of numbers
Abstract: We present a comparison of simulation-based inference to full, field-based analytical inference in cosmological data analysis. To do so, we explore parameter inference for two cases where the information content is calculable analytically: Gaussian random fields whose covariance depends on parameters through the power spectrum; and correlated lognormal fields with cosmological power spectra. We compare two inference techniques: i) explicit field-level inference using the known likelihood and ii) implicit likelihood inference with maximally informative summary statistics compressed via Information Maximising Neural Networks (IMNNs). We find that a) summaries obtained from convolutional neural network compression do not lose information and therefore saturate the known field information content, both for the Gaussian covariance and the lognormal cases, b) simulation-based inference using these maximally informative nonlinear summaries recovers nearly losslessly the exact posteriors of field-level inference, bypassing the need to evaluate expensive likelihoods or invert covariance matrices, and c) even for this simple example, implicit, simulation-based likelihood incurs a much smaller computational cost than inference with an explicit likelihood. This work uses a new IMNNs implementation in JAX that can take advantage of fully-differentiable simulation and inference pipeline. We also demonstrate that a single retraining of the IMNN summaries effectively achieves the theoretically maximal information, enhancing the robustness to the choice of fiducial model where the IMNN is trained.
Presentation slides [.pdf]
Presentation video [!yt]
arXiv:2107.07405 [arxiv.org]
Code Tutorial [collab.research.google.com]
 
Aneta Siemiginowska (CfA)
Nov 4 2021
4pm EDT
CfA Colloquium
Adventures in Astrostatistics
Abstract: Over the past two decades Chandra X-ray Observatory has collected exquisite data contributing to discoveries and significant advances in our understanding of many aspects of astrophysical phenomena. The Chandra data and the data from other modern X-ray telescopes have challenged traditional analysis methods and inspired development of new algorithms and methodologies in the growing field of astrostatistics. I will provide an overview of general issues in the analysis of X-ray data and discuss examples of significant contributions to the field brought by the CHASC astrostatistics collaboration. I will share a perspective on future challenges and discuss emerging methodologies for data science in high energy astrophysics.
CfA Colloquium
Presentation slides [.pdf]
Presentation Video [!yt]
 
Willow Fox-Fortino (UPenn/UDel)
Nov 9 2021
12:30pm EST
Zoom
Reducing ground-based astrometric errors with Gaia and Gaussian processes
Abstract: Stochastic field distortions caused by atmospheric turbulence are a fundamental limitation to the astrometric accuracy of ground-based imaging. This distortion field is measurable at the locations of stars with accurate positions provided by the Gaia DR2 catalog; we develop the use of Gaussian process regression (GPR) to interpolate the distortion field to arbitrary locations in each exposure. We introduce an extension to standard GPR techniques that exploits the knowledge that the 2D distortion field is curl-free. Applied to several hundred 90 s exposures from the Dark Energy Survey as a test bed, we find that the GPR correction reduces the variance of the turbulent astrometric distortions #12 , on average, with better performance in denser regions of the Gaia catalog. The rms per-coordinate distortion in the riz bands is typically #7 mas before any correction and #2 mas after application of the GPR model. The GPR astrometric corrections are validated by the observation that their use reduces, from 10 to 5 mas rms, the residuals to an orbit fit to riz-band observations over 5 yr of the r = 18.5 trans-Neptunian object Eris. We also propose a GPR method, not yet implemented, for simultaneously estimating the turbulence fields and the 5D stellar solutions in a stack of overlapping exposures, which should yield further turbulence reductions in future deep surveys.
2021, AJ 162, 106 [ADS]
Presentation slides [.pdf]
Presentation Video [!yt]
 
Karthik Reddy (UMBC & CfA)
Nov 16 2021
Noon EST
Zoom
Astrophysical Jets with Astrostatistics: Using X-ray/Radio structural differences to understand their X-ray emission
Abstract: The mechanism responsible for the kpc-scale emission from extragalactic jets constrains an important quantity: the energy the jet feeds back into the host galaxy and the cluster. Besides spectral data, observations of X-ray/radio positional offsets in these jets' individual components (or knots) provide an important clue. The first step in utilizing these offsets would be to establish their statistics and any trends that may emerge. In this talk, I will describe the application of a statistical tool called LIRA (Low-count Image Reconstruction and Analysis) to extract offsets from X-ray observations while accounting for Poisson fluctuations and emission from nearby bright sources, and will discuss the results of this work. I will also describe ongoing work on optimizing the LIRA code and understanding the effects of PSF uncertainties in analyses with LIRA.
Presentation Slides [.pptx]
Presentation Video [!yt]
 
Frank Primini (CfA)
Dec 14 2021
Noon EST
Zoom
Q&A: Statistical Challenges in the Chandra Source Catalog
 
Siddharth Mishra-Sharma (MIT)
Apr 5 2022
Noon EDT
Zoom
Leveraging neural simulation-based inference for astrophysical dark matter searches
Abstract: Advancements in machine learning have enabled new ways of performing inference on models defined through complex, high-dimensional simulations. I will present applications of these simulation-based inference (SBI) methods to two systems where the goal is to look for signatures of dark matter. First, I will describe how SBI can be used to combine information from thousands of strong gravitational lenses in a principled and scalable way to extract the population properties of dark matter subhalos. Then, I will present an application to gamma-ray data from the Fermi space telescope, with the goal being to understand the origin of the long-standing Galactic Center excess. I will show how neural SBI can be used to extract more information from the gamma-ray data than is possible using conventional techniques, and highlight how this makes our pipeline more robust to known systematic effects such as mis-modeling of the Galactic diffuse foreground.
Presentation slides [.pdf]
Presentation video [!yt]
References:
Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning [arXiv]
A neural simulation-based inference approach for characterizing the Galactic Center gamma-ray excess [arXiv]
 
Antoine Meyer (Imperial)
Apr 19 2022
Noon EDT
Phillips auditorium at CfA
& Zoom
Cosmological time delay estimation with Continuous Auto-Regressive Moving Average processes
Abstract: Strong gravitational lensing occurs when the gravitational field of a galaxy bends the light emitted by a distant source, causing multiple images of the same source to appear in the sky when viewed from Earth. Fluctuations in the source brightness are observed in the images at different times, due to the different paths the lensed images take to travel to the observer. The time delay between brightness fluctuations can be used to constrain cosmological parameters such as the expansion rate of the Universe. We develop a Bayesian method to estimate cosmological time delays, using Continuous Auto-Regressive Moving Average (CARMA) processes to model the irregularly sampled time series of brightness data from the several observed images of the source. Our model accounts for heteroskedastic measurement errors and an additional source of independent extrinsic long-term variability in the source brightness, known as microlensing. We employ the Kalman Filter algorithm for efficient likelihood computation and perform posterior sampling using the MultiNest implementation of nested sampling to deal with posterior multimodality.
Presentation slides [.pdf]
Presentation video [!yt]
 
Lucas Janson (Harvard)
May 17 2022
Noon EDT
Zoom
Controlled Discovery and Localization of Astronomical Point Sources via Bayesian Linear Programming (BLiP)
Abstract: In many statistical problems, it is necessary to simultaneously discover signals and localize them as precisely as possible. For instance, astronomical sky surveys aim to identify point sources, but noise and other optical artifacts make it hard to identify the exact locations of those point sources. So the statistical task is to output as many regions as possible and have those regions be as small as possible, while controlling how many outputted regions contain no signal. The same problem arises in any application where signals cannot be perfectly localized, such as fine-mapping in genetics and change point detection in time series data. However, there are two competing objectives: maximizing the number of discoveries and minimizing the size of those discoveries (all while controlling false discoveries), so our first contribution is to propose a single unified measure we call the resolution-adjusted power that formally trades off these two objectives and hence, in principle, can be maximized subject to a constraint on false discoveries. We take a Bayesian approach, but the resulting posterior optimization problem is intractable due to its non-convexity and high-dimensionality. Thus our second contribution is Bayesian Linear Programming (BLiP), a method which overcomes this intractability to jointly detect and localize signals in a way that verifiably nearly maximizes the expected resolution-adjusted power while provably controlling false discoveries. BLiP is very computationally efficient and can wrap around any Bayesian model and algorithm for approximating the posterior distribution over signal locations. Applying BLiP on top of existing state-of-the-art Bayesian analyses of the Sloan Digital Sky Survey (for astronomical point source detection) and UK Biobank data (for genetic fine-mapping) increased the resolution-adjusted power by 30-120% with just a few minutes of computation. BLiP is implemented in the new packages pyblip (Python) and blipr (R). This is joint work with Asher Spector.
Presentation slides [.pdf]
Presentation video [!yt]
Reference: Controlled Discovery and Localization of Signals via Bayesian Linear Programming [arXiv:2203.17208]
 
 
 
 
















Archive
Fall/Winter 2004-2005
Siemiginowska, A. / Connors, A. / Kashyap, V. / Zezas, A. / Devor, J. / Drake, J. / Kolaczyk, E. / Izem, R. / Kang, H. / Yu, Y. / van Dyk, D.
Fall/Winter 2005-2006
van Dyk, D. / Ratner, M. / Jin, J. / Park, T. / CCW / Zezas, A. / Hong, J. / Siemiginowska, A. & Kashyap, V. / Meng, X.-L.
Fall/Winter 2006-2007
Lee, H. / Connors, A. / Protopapas, P. / McDowell, J., / Izem, R. / Blondin, S. / Lee, H. / Zezas, A., & Lee, H. / Liu, J.C. / van Dyk, D. / Rice, J.
Fall/Winter 2007-2008
Connors, A., & Protopapas, P. / Steiner, J. / Baines, P. / Zezas, A. / Aldcroft, T.
Fall/Winter 2008-2009
H. Lee / A. Connors, B. Kelly, & P. Protopapas / P. Baines / A. Blocker / J. Hong / H. Chernoff / Z. Li / L. Zhu (Feb) / A. Connors (Pt.1) / A. Connors (Pt.2) / L. Zhu (Mar) / E. Kolaczyk / V. Liublinska / N. Stein
Fall/Winter 2009-2010
A.Connors / B.Kelly / N.Stein, P.Baines / D.Stenning / J. Xu / A.Blocker / P.Baines, Y.Yu / V.Liublinska, J.Xu, J.Liu / Meng X.L., et al. / A. Blocker, et al. / A. Siemiginowska / D. Richard / A. Blocker / Xie X. / Xu J. / V. Liublinska / L. Jing
AcadYr 2010-2011
Astrostat Haiku / P. Protopapas / A. Zezas & V. Kashyap / A. Siemiginowska / K. Mandel / N. Stein / A. Mahabal / Hong J.S. / D. Stenning / A. Diaferio / Xu J. / B. Kelly / P. Baines & I. Udaltsova / M. Weber
AcadYr 2011-2012
A. Blocker / Astro for Stat / B. Kelly / R. D'Abrusco / E. Turner / Xu J. / T. Loredo / A. Blocker / P. Baines / A. Zezas et al. / Min S. & Xu J. / O. Papaspiliopoulos / Wang L. / T. Laskar
AcadYr 2012-2013
N. Stein / A. Siemiginowska / D. Cervone / R. Dawson / P. Protopapas / K. Reeves / Xu J. / J. Scargle / Min S. / Wang L. & D. Jones / J. Steiner / B. Kelly / K. McKeough
AcadYr 2013-2014
Meng X.-L. / Meng X.-L., K. Mandel / A. Siemiginowska / S. Vrtilek & L. Bornn / Lazhi W. / D. Jones / R. Wong / Xu J. / van Dyk D. / Feigelson E. / Gopalan G. / Min S. / Smith R. / Zezas A. / van Dyk D. / Hyungsuk T. / Czerny, B. / Jones D. / Liu K. / Zezas A.
AcadYr 2014-2015
Vegetabile, B. & Aldcroft, T., / H. Jae Sub / Siemiginowska, A. & Kashyap, V. / Pankratius, V. / Tak, H. / Brenneman, L. / Johnson, J. / Lynch, R.C. / Fan, M.J. / Meng, X.-L. / Gopalan, G. / Jiao, X. / Si, S. / Udaltsova, I. & Zezas, A. / Wang, L. / Tak, H. / Eadie, G. / Czekala, I. / Stenning, D. / Stampoulis, V. / Aitkin, M. / Algeri, S. / Barnacka, A.
AcadYr 2015-2016
DePasquale, J. / Tak, H. / Meng, X.-L. / Jones, D. / Huang, J. / Blanchard, P. / Chen, Y. & Wang, X. / Tak, H. / Mandel, K. / Jiao, X. / Wang, X. & Chen, Y. / IACHEC WG / Si, S. / Drake, J. / Stampoulis, V. / Algeri, S. / Stein, N. / Chunzhe, Z. / Andrews, J. / Vrtilek, S. / Udaltsova, I. & Stampoulis, V.
AcadYr 2016-2017
Wang, X. & Chen, Y. / Kashyap, V., Siemiginowska, A., & Zezas, A. / Stampoulis, V. / Portillo, S. / Zhang, K. / Mandel, K. / DiStefano, R. / Finkbeiner, D. & Meade, B. / Gong, R. / Shihao Y. / Zhirui, H. / Xufei, W. / Campos, L. / Tak, H. / Xufei, W. / Jones, D. / Algeri, S. / Speagle, J. / Czekala, I.
AcadYr 2017-2018
AstroStat Day / Speagle, J. / Collin, G. / McKeough, K. & Yang, S. / McKeough, K. & Campos, L. / M. Ntampaka / H. Marshall / D. Huppenkothen / X. Yu / R. DiStefano / J. Yee / H. Tak / A. Avelino
AcadYr 2018-2019
Stenning, D. / Dvorkin, C. / Sottosanti, A. / Yu, X. / Chen, Y. / Jones, D. / Lee, T.C.-M. / Tak, H. / Kashyap, V., McKeough, K., Campos, L., et al. / Baines, P. / Collin, G. / Muthukrishna, D. / Zhang, D. / Algeri, S. / Janson, L. / Ward, S. / de Beurs, Z.
AcadYr 2019-2020
McKeough, K. / Astudillo, J. & Protopapas, P. / Zezas, A. / Speagle, J. / Meng, X.-L., Siemiginowska, A., & Kashyap, V. / Bonfini, P. / Liu, C. / Guenther, H. / Castrillon, J. / McKeough, K. / Broekgaarden, F. / Autenrieth, M. / Motta, G. / Zucker, C. / Tak, H. / Kashyap, V. & Wang, X. / Wang, J. / Wang, X. & Ingram, J.
AcadYr 2020-2021
Diaz Rivero, A. / Marshall, H. & Chen, Y. / McKeough, K. / Chen, Y. / Patil, A. / Jerius, D. / Wang, X. / Siemiginowska, A. / Xu, C. / Picquenot, A. / Jacovich, T. / Geringer-Sameth, A. / Toulis, P. / Donath, A. / Ergin, T. / Phillipson, R. / Sun, H. / Autenrieth, M.
AcadYr 2021-2022
Makinen, T.L. / Siemiginowska, A. / Fox-Fortino, W. / Reddy, K. / Primini, F. / Mishra-Sharma, S. / Meyer, A. / Janson, L.

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