| Presentations |
Hyungsuk Tak (PSU)
Sep 4, 2024
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- Six Maxims of Statistical Acumen for Astronomical Data Analysis
- The production of complex astronomical data is accelerating, especially with newer telescopes producing ever more large-scale surveys. The increased quantity, complexity, and variety of astronomical data demand a parallel increase in skill and sophistication in developing, deciding, and deploying statistical methods. Understanding limitations and appreciating nuances in statistical and machine learning methods and the reasoning behind them is essential for improving data-analytic proficiency and acumen. Aiming to facilitate such improvement in astronomy, we delineate cautionary tales in statistics via six maxims, with examples drawn from the astronomical literature. Inspired by the significant quality improvement in business and manufacturing processes by the routine adoption of Six Sigma, we hope the routine reflection on these Six Maxims will improve the quality of both data analysis andscientific findings in astronomy.
- See also: arXiv:2409.16179 [!arXiv]
- Presentation slides [.pdf]
- Presentation video [!yt]
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Yanke Song (Harvard)
Sep 18, 2024
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- A Poisson-process AutoDecoder for X-ray Sources
- Abstract: X-ray observing facilities such as the Chandra X-ray Observatory and the eROSITA all sky survey have detected millions of astronomical sources associated with high-energy phenomena. The arrival of photons as a function of time follows a Poisson process and can vary by orders-of-magnitude, presenting obstacles for downstream tasks such as source classification, physical property derivation and anomaly detection. Previous work has either failed to directly capture the Poisson nature of the data or only focuses on Poisson rate function reconstruction. In this work, we present Poisson Process AutoDecoder (PPAD). PPAD is a neural field decoder that maps fixed-length latent features to continuous Poisson rate functions across energy band and time via unsupervised learning. It reconstructs the rate function and yields a representation at the same time. We demonstrate the efficacy of PPAD reconstruction, regression, classification and anomaly detection experiments using the Chandra Source Catalog.
- Presentation video [!yt]
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Ann Lee (CMU)
Oct 16, 2024
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- Valid Scientific Inference with Neural Density Estimators
- Abstract: Scientific inference often involves inferring internal key parameters that determine the outcome of a complex physical phenomenon. The data themselves may come in the form of a labeled set that implicitly encodes the likelihood function; for example, in the form of (i) pairs of internal parameters and observable data according to a mechanistic (simulator) model, or as (ii) observed data and internal parameters, where the latter parameters are not directly observable, but have been inferred to high precision via an auxiliary measurement and a theoretical model. We refer to inference in both of these intractable likelihood settings as "Likelihood-Free Inference'" (LFI). Scientists are increasingly leveraging machine learning methods, such as neural density estimator and AI generative models, for parameter inference in LFI settings. However, high-posterior density regions derived from these density estimators do not necessarily have a high probability of including the true parameter of interest, even if the posterior is well-estimated and the labeled data have the same distribution as the target distribution. Furthermore, if the prior distribution is poorly specified, then the HPD regions could severely undercover and/or be biased, thereby leading to misleading scientific conclusions. In this talk, I will present new LFI methodology and algorithms for leveraging neural density estimators to produce confidence regions of parameters of interest that have (i) nominal frequentist coverage for any values of the true unknown parameters, and (ii) smaller average area (yielding higher constraining power) if the prior is well-specified. I will illustrate our methods on examples from astronomy and high-energy physics, and discuss where we stand and what challenges still remain. (This work is joint with Luca Masserano, James Carzon, Antonio Carlos Herling Ribeiro Junior, Alex Shen, Tommaso Dorigo, Michele Doro, Mikael Kuusela, Joshua Speagle, Rafael Izbicki)
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Justina Yang (Harvard)
Oct 23, 2024
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- Emulating Photon Pile-up Effects on X-ray Spectra with a Neural Network
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Christina Reissel (MIT)
Oct 30, 2024
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- Machine Learning for Real-Time Analysis of Gravitational Wave Data
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| 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.
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Fall/Winter 2007-2008
Connors, A., & Protopapas, P. / Steiner, J. / Baines, P. / Zezas, A. / Aldcroft, T.
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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
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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
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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
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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
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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
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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.
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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.
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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.
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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.
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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
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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.
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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.
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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.
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AcadYr 2021-2022
Makinen, T.L.
/ Siemiginowska, A.
/ Fox-Fortino, W.
/ Reddy, K.
/ Primini, F.
/ Mishra-Sharma, S.
/ Meyer, A.
/ Janson, L.
/ Group
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AcadYr 2022-2023
Saydjari, A.
/ Rau, M.M.
/ McKimm, H.
/ Sairam, L.
/ Meyer, A.
/ SCMA8
/ Kochanski, N. & Chen, Y.
/ Jones, G.
/ ISI WSC
/ Li, D.D.
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AcadYr 2023-2024
Garraffo, C.
/ Gu, M.
/ Villar, A. & Martinez-Galarza, J.R.
/ Siemiginowska, A.
/ Protopapas, P.
/ Marshall, H., Athiray, S., & Kashyap, V.L.
/ Daoud, A.
/ Uzsoy, A.-S.
/ Donath, A.
/ Zhang, X.
/ Chen, Y. & Bonamente, M.
/ Bayle, A.
/ Sengupta, S.
/ Li, J.S.
/ Vishwanath, S.
/ Motta, G.
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AcadYr 2024-2025
Tak, H.
/ Song, Y.
/ Lee, A.
/ Yang, J.
/ Reissel, C.
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