| Presentations |
Hyungsuk Tak (PSU)
Sep 4, 2024
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- Six Maxims of Statistical Acumen for Astronomical Data Analysis
- Abstract: 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.
- Slides [.pdf] (slightly evolved from presented slides)
- 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)
- Presentation slides [.pdf]
- Presentation Video [!yt]
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Christina Reissel (MIT)
Oct 30, 2024
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- Machine Learning for Real-Time Analysis of Gravitational Wave Data
- Abstract:
The detection of gravitational waves has opened a new field of astronomy. Upgrades to gravitational wave observatories will further increase the sensitivity to a broader range of astrophysical events, specifically to weaker signals from more distant or lower-mass events, simultaneously increasing backgrounds dramatically. At the same time, artificial intelligence (AI) enables fast decision-making and interpretation with performances that transcend other strategies.
In this talk, we present ml4gw, an end-to-end software framework for real-time gravitational-wave data analysis. Combining AI-based algorithms for denoising, binary black detection, anomaly detection, and real-time parameter estimation, we aim for a fast, intuitive pipeline which is able to leverage the powerful modeling techniques available in the gravitational wave literature. This real-time gravitational-wave data analysis will be an essential backbone for optimal multi-messenger astronomy, providing fast and accurate triggers about potentially interesting events, enabling minimal reaction times of other observatories.
- Presentation Video [!yt]
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Justina Yang (Harvard)
Nov 6, 2024
Noon EST
Zoom+SciCen 706
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- Emulating Photon Pile-up Effects on X-ray Spectra with a Neural Network
- Abstract: In X-ray astronomy, photon pile-up occurs when two or more X-ray photons strike a detector within the same readout time and are recorded as a single "piled-up" event. In instruments such as the ACIS detector on the Chandra X-ray Observatory, pile-up can cause effects such as artificially low event count rates or the misidentification of events as events of higher energies. These effects distort the shape and intensity of observed spectra, especially for bright sources, and complicate the inference of source properties. However, pile-up effects are challenging to model analytically and the standard statistical method for modeling pile-up (Davis 2001) is not well-suited to analyzing data with severe amounts of pile-up. We therefore construct a neural network emulator to mimic the empirical effects of pile-up on X-ray spectra. We first limit the emulator to data simulated with MARX (Davis et al. 2012), examine the emulator's ability to mimic an approximate version of pile-up, and describe how the emulator can be incorporated into standard parameter inference methods. We also discuss next steps for refining the neural network to emulate the empirical version of pile-up in the ACIS detector. We expect that our method can be adapted for future X-ray detectors to emulate instrumental effects that are analogous to pile-up.
- Presentation slides [.pdf]
- Presentation Video [!yt]
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Robert Zimmerman (Toronto+Imperial)
Nov 13, 2024
Zoom |
- Hidden Markov Modeling of X-ray light curves
- Abstract: We present a new method to distinguish between different states (e.g., high and low, quiescent and flaring) in astronomical sources with count data. The method models the underlying physical process as latent variables following a continuous-space Markov chain that determines the expected Poisson counts in observed light curves in multiple passbands. For the underlying state process, we consider several autoregressive processes, yielding continuous-space hidden Markov models of varying complexity. Under these models, we can infer the state that the object is in at any given time. The continuous state predictions from these models are then dichotomized with the help of a finite mixture model to produce state classifications. We apply these techniques to X-ray data from the active dMe flare star EVLac, splitting the data into quiescent and flaring states. We find that a first-order vector autoregressive process efficiently separates flaring from quiescence: flaring occurs over 30-40% of the observation durations, a well-defined persistent quiescent state can be identified, and the flaring state is characterized by higher temperatures and emission measures.
- Presentation slides [.pdf]
- Presentation video [!yt]
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Herman Marshall (MIT)
Nov 20, 2024
Noon EST
SciCen 706 + Zoom |
- How can we test the origins of extragalactic neutrinos?
- Abstract: The processes that can produce high energy neutrinos are relevant to high energy physics and astrophysics, representing environments that cannot be produced on Earth. They are detected by a large experiment buried in the ice at the south pole. The experiment uses the bulk of the earth as a way to block background. Neutrinos have such a low cross section for interaction that they travel through the earth, interacting in the ice with upward-going tracks of secondary particles. They are extremely rare and have large error regions on the sky, so it is difficult to identify any specific source. There have been a few population studies using cross-correlation methods, such as Buson et a. (2022, 2022ApJ...933L..43B). There are, of course, complications but Buson et al claim a p-value of 6e-7, seemingly irrefutable. After expressing interest (perhaps "doubt") in their methods, I was asked by Dr. Buson at a recent meeting to see if a set of statisticians could review their methods for validating the associations of neutrinos with radio loud quasars.
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Matteo Guardini (ESO/Garching)
Jan 22, 2025
Zoom |
- Automatic astrophysical component detection and modeling
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Aritra Banerjee (Minnesota)
Apr 2, 2025 | Noon EDT
SciCen 706 + Zoom
- Signal Detection Under Unknown Background when Only One Unlabeled Data is Available
- Abstract:
Searches for new physics involve detecting the presence of a specific signal in data that is contaminated by a background arising from several other sources. This task is particularly challenging when a reliable description of the background is unavailable. The aim of this work is to develop a statistical method to test the presence of the signal of interest in the data and estimate the signal proportion even when the background is unknown or misspecified. Moreover, we carry out the signal search only using a single physics dataset generated from the experiments that may or may not contain the signal of interest. Our approach relies on using orthonormal expansion to model the deviation between a proposal density and the unknown density generating the data. We propose choosing the proposal density in such a way so that one can ensure a conservative estimate of the signal proportion to avoid false discovery. Reliability of this approach is demonstrated through simulation studies, application on realistic simulated data from the Fermi Large Area Telescope and on data from the ATLAS experiment. We also perform a comparative analysis of our proposed method with the so-called "safeguard" or "spurious signal" method commonly employed in particle physics and explore cases where the latter leads to false discoveries.
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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.
/ Reissel, C.
/ Yang, J.
/ Zimmerman, R.
/ Marshall, H.
/ Guardini, M.
/ Banerjee, A.
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