DMS-EPSRC Collaborative Research: Advancing Statistical Foundations and Frontiers from and for Emerging Astronomical Data Challenges
Lead Research Organisation:
Imperial College London
Department Name: Mathematics
Abstract
Statistical theory and methods play a fundamental role in scientific discovery and advancement, including in modern astronomy, where data are collected on increasingly massive scales and with more varieties and complexity. New technology and instrumentation are spawning a diverse array of emerging data types and data analytic challenges, which in turn require and inspire ever more innovative statistical methods and theories. This proposal is guided by the dual aims of advancing statistical foundations and frontiers, motivated by astronomical problems and providing principled data analytic solutions to challenges in astronomy. The CHASC International Center for Astrostatistics has an extensive track record in accomplishing both tasks. This NSF-EPSRC project leverages CHASC's track record to make progress in several new projects. Fitting sophisticated astrophysical models to complex data that were collected with high-tech instruments, for example, often involves a sequence of statistical analyses. Several UK-led projects center on developing new statistical methods that properly account for errors and carry uncertainty forward within such sequences of analyses. Additional US-led work will focus on developing theoretical properties of novel statistical estimation procedures to address data-analytic challenges associated with solar flares and X-ray observations. Other US-led projects involve fast and automatic detection of astronomical objects such as galaxies from 2D or even 4D data. The PIs will develop statistical theory and methods in the context of these projects, building statistical foundations and pushing the frontiers of statistics forward for broad impact that will extend well beyond astrostatistics. The PIs plan to offer effective methods and algorithms for tackling emerging challenges in astronomy, with the aspiration of promoting such principled data-analytic methods among researchers in astronomy. Its provision of free software via the CHASC GitHub Software Library will enable the distribution and impact of the proposed methods and algorithms.
Organisations
- Imperial College London (Lead Research Organisation)
- Smithsonian Astrophysical Observatory (Collaboration)
- International School for Advanced Studies (Collaboration)
- University of Michigan (Collaboration, Project Partner)
- University of California, Davis (Collaboration)
- Harvard University (Collaboration)
- Harvard University (Project Partner)
- Center for Astrophysics(Harvard & Smith) (Project Partner)
- University of California, Davis (Project Partner)
People |
ORCID iD |
| David Van Dyk (Principal Investigator) |
Publications
Meyer A
(2023)
TD-CARMA: Painless, Accurate, and Scalable Estimates of Gravitational Lens Time Delays with Flexible CARMA Processes
in The Astrophysical Journal
Fan M
(2023)
Identifying Diffuse Spatial Structures in High-energy Photon Lists
in The Astronomical Journal
Autenrieth M
(2023)
Stratified learning: A general-purpose statistical method for improved learning under covariate shift
in Statistical Analysis and Data Mining: The ASA Data Science Journal
Donath A
(2024)
Joint Deconvolution of Astronomical Images in the Presence of Poisson Noise
in The Astronomical Journal
Tak H
(2024)
Six Maxims of Statistical Acumen for Astronomical Data Analysis
in The Astrophysical Journal Supplement Series
Zimmerman R
(2024)
Separating states in astronomical sources using hidden Markov models: with a case study of flaring and quiescence on EV Lac
in Monthly Notices of the Royal Astronomical Society
Autenrieth M
(2024)
Improved weak lensing photometric redshift calibration via StratLearn and hierarchical modelling
in Monthly Notices of the Royal Astronomical Society
H McKimm
(2025)
Sampling using adaptive regenerative processes
in Bernoulli
| Description | Faculty of Natural Sciences -- Researcher Mobility Grant for Postdocs and Fellows |
| Amount | £2,000 (GBP) |
| Organisation | Imperial College London |
| Sector | Academic/University |
| Country | United Kingdom |
| Start | 04/2024 |
| End | 11/2024 |
| Description | Roth Studentship |
| Amount | £120,000 (GBP) |
| Organisation | Imperial College London |
| Sector | Academic/University |
| Country | United Kingdom |
| Start | 09/2023 |
| End | 09/2027 |
| Description | StatML CDT: Modern Statistics and Statistical Machine Learning |
| Amount | £6,202,023 (GBP) |
| Funding ID | 2740612 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 09/2022 |
| End | 09/2026 |
| Description | CHASC International Center for Astrostatistics |
| Organisation | Harvard University |
| Country | United States |
| Sector | Academic/University |
| PI Contribution | The PI is a member of the CHASC International Center for Astrostatistics. Since its founding at Harvard in 1997, CHASC has established an impressive track record both for developing new statistical methods to solve challenging problems in astrophysics and for leveraging these problems to devise new general purpose statistical theory, methods and computational techniques. CHASC actively engages diverse groups of postgraduate students in its interdisciplinary research; many of these students have gone on to successful academic careers. CHASC is devoted to promote sound statistical practice among scientists to derive insight and knowledge, and to help statisticians develop better scientific understanding and insight. The CHASC Center also provides a worldwide forum for exchanges on challenging data problems in astronomy and for disseminating the methods developed by the Center, including free software. Several CHASC packages have been incorporated into CIAO, the primary data analysis environment used by X- ray astronomers. Other software packages are distributed via the CHASC GitHub Astrostatistics Software Library. In addition, CHASC organizes many sessions at meetings of statisticians to highlight newly developed methods of general interest, and at meetings of astronomers sometimes to convey new methods and other times with a more basic educational emphasis. Van Dyk (PI, Imperial) was the original founder of CHASC and has been its overall leader since. He is an international leader in astrostatistics with specific expertise in Bayesian methodology, multi-level models, and EM-like and MCMC algorithms. He has published extensively in leading astrophysical and statistical journals. |
| Collaborator Contribution | Meng (PI, Harvard) has extensive research experience in statistical modeling, fitting, and improvement, in developing methods for complex incomplete data, and in the interplay of inferential perspectives for complex model fitting and estimation. He is also a leading voice in promoting principled data scientific and statistical methods, especially in his role as the Founding Editor-in-Chief for Harvard Data Science Review, a fast expanding international forum for perspectives, education, and research in data science. Lee (PI, Davis) has deep expertise in image processing, large scale computations, spatio-temporal modeling, time series and change point problems. He publishes frequently in leading statistics and engineering outlets. He and van Dyk are two of the three co-founders of the ASA Astrostatistics Interest Group. Chen is an alumna of the Center where she led a major effort on the calibration project, and has been involved in multiple collaborative astrostatistics projects since joining University Michigan. The astronomers (Kashyap and Siemiginowska, Center for Astrophysics | Harvard & Smithsonian) are leading experts in the analysis of high energy astrophysics data. They have vast experience with instrument calibration, astronomical software analysis systems, and are deeply involved with the development of methods, algorithms, and publicly available software for Chandra data. In consultation with these experts the statistics PIs (Meng, van Dyk, Lee, and Chen) oversee the entire project, with the PI in the lead institute (Meng) as the overall coordinator and convener. Research carried out simultaneously at the four institutions in all stages of the project. |
| Impact | Fan et al. (2023), as well as the preprint: Meyer et al. (2023+ , arXiv:2207.09327) which is under review a ApJ. |
| Description | CHASC International Center for Astrostatistics |
| Organisation | Smithsonian Astrophysical Observatory |
| Country | United States |
| Sector | Public |
| PI Contribution | The PI is a member of the CHASC International Center for Astrostatistics. Since its founding at Harvard in 1997, CHASC has established an impressive track record both for developing new statistical methods to solve challenging problems in astrophysics and for leveraging these problems to devise new general purpose statistical theory, methods and computational techniques. CHASC actively engages diverse groups of postgraduate students in its interdisciplinary research; many of these students have gone on to successful academic careers. CHASC is devoted to promote sound statistical practice among scientists to derive insight and knowledge, and to help statisticians develop better scientific understanding and insight. The CHASC Center also provides a worldwide forum for exchanges on challenging data problems in astronomy and for disseminating the methods developed by the Center, including free software. Several CHASC packages have been incorporated into CIAO, the primary data analysis environment used by X- ray astronomers. Other software packages are distributed via the CHASC GitHub Astrostatistics Software Library. In addition, CHASC organizes many sessions at meetings of statisticians to highlight newly developed methods of general interest, and at meetings of astronomers sometimes to convey new methods and other times with a more basic educational emphasis. Van Dyk (PI, Imperial) was the original founder of CHASC and has been its overall leader since. He is an international leader in astrostatistics with specific expertise in Bayesian methodology, multi-level models, and EM-like and MCMC algorithms. He has published extensively in leading astrophysical and statistical journals. |
| Collaborator Contribution | Meng (PI, Harvard) has extensive research experience in statistical modeling, fitting, and improvement, in developing methods for complex incomplete data, and in the interplay of inferential perspectives for complex model fitting and estimation. He is also a leading voice in promoting principled data scientific and statistical methods, especially in his role as the Founding Editor-in-Chief for Harvard Data Science Review, a fast expanding international forum for perspectives, education, and research in data science. Lee (PI, Davis) has deep expertise in image processing, large scale computations, spatio-temporal modeling, time series and change point problems. He publishes frequently in leading statistics and engineering outlets. He and van Dyk are two of the three co-founders of the ASA Astrostatistics Interest Group. Chen is an alumna of the Center where she led a major effort on the calibration project, and has been involved in multiple collaborative astrostatistics projects since joining University Michigan. The astronomers (Kashyap and Siemiginowska, Center for Astrophysics | Harvard & Smithsonian) are leading experts in the analysis of high energy astrophysics data. They have vast experience with instrument calibration, astronomical software analysis systems, and are deeply involved with the development of methods, algorithms, and publicly available software for Chandra data. In consultation with these experts the statistics PIs (Meng, van Dyk, Lee, and Chen) oversee the entire project, with the PI in the lead institute (Meng) as the overall coordinator and convener. Research carried out simultaneously at the four institutions in all stages of the project. |
| Impact | Fan et al. (2023), as well as the preprint: Meyer et al. (2023+ , arXiv:2207.09327) which is under review a ApJ. |
| Description | CHASC International Center for Astrostatistics |
| Organisation | University of California, Davis |
| Country | United States |
| Sector | Academic/University |
| PI Contribution | The PI is a member of the CHASC International Center for Astrostatistics. Since its founding at Harvard in 1997, CHASC has established an impressive track record both for developing new statistical methods to solve challenging problems in astrophysics and for leveraging these problems to devise new general purpose statistical theory, methods and computational techniques. CHASC actively engages diverse groups of postgraduate students in its interdisciplinary research; many of these students have gone on to successful academic careers. CHASC is devoted to promote sound statistical practice among scientists to derive insight and knowledge, and to help statisticians develop better scientific understanding and insight. The CHASC Center also provides a worldwide forum for exchanges on challenging data problems in astronomy and for disseminating the methods developed by the Center, including free software. Several CHASC packages have been incorporated into CIAO, the primary data analysis environment used by X- ray astronomers. Other software packages are distributed via the CHASC GitHub Astrostatistics Software Library. In addition, CHASC organizes many sessions at meetings of statisticians to highlight newly developed methods of general interest, and at meetings of astronomers sometimes to convey new methods and other times with a more basic educational emphasis. Van Dyk (PI, Imperial) was the original founder of CHASC and has been its overall leader since. He is an international leader in astrostatistics with specific expertise in Bayesian methodology, multi-level models, and EM-like and MCMC algorithms. He has published extensively in leading astrophysical and statistical journals. |
| Collaborator Contribution | Meng (PI, Harvard) has extensive research experience in statistical modeling, fitting, and improvement, in developing methods for complex incomplete data, and in the interplay of inferential perspectives for complex model fitting and estimation. He is also a leading voice in promoting principled data scientific and statistical methods, especially in his role as the Founding Editor-in-Chief for Harvard Data Science Review, a fast expanding international forum for perspectives, education, and research in data science. Lee (PI, Davis) has deep expertise in image processing, large scale computations, spatio-temporal modeling, time series and change point problems. He publishes frequently in leading statistics and engineering outlets. He and van Dyk are two of the three co-founders of the ASA Astrostatistics Interest Group. Chen is an alumna of the Center where she led a major effort on the calibration project, and has been involved in multiple collaborative astrostatistics projects since joining University Michigan. The astronomers (Kashyap and Siemiginowska, Center for Astrophysics | Harvard & Smithsonian) are leading experts in the analysis of high energy astrophysics data. They have vast experience with instrument calibration, astronomical software analysis systems, and are deeply involved with the development of methods, algorithms, and publicly available software for Chandra data. In consultation with these experts the statistics PIs (Meng, van Dyk, Lee, and Chen) oversee the entire project, with the PI in the lead institute (Meng) as the overall coordinator and convener. Research carried out simultaneously at the four institutions in all stages of the project. |
| Impact | Fan et al. (2023), as well as the preprint: Meyer et al. (2023+ , arXiv:2207.09327) which is under review a ApJ. |
| Description | CHASC International Center for Astrostatistics |
| Organisation | University of Michigan |
| Country | United States |
| Sector | Academic/University |
| PI Contribution | The PI is a member of the CHASC International Center for Astrostatistics. Since its founding at Harvard in 1997, CHASC has established an impressive track record both for developing new statistical methods to solve challenging problems in astrophysics and for leveraging these problems to devise new general purpose statistical theory, methods and computational techniques. CHASC actively engages diverse groups of postgraduate students in its interdisciplinary research; many of these students have gone on to successful academic careers. CHASC is devoted to promote sound statistical practice among scientists to derive insight and knowledge, and to help statisticians develop better scientific understanding and insight. The CHASC Center also provides a worldwide forum for exchanges on challenging data problems in astronomy and for disseminating the methods developed by the Center, including free software. Several CHASC packages have been incorporated into CIAO, the primary data analysis environment used by X- ray astronomers. Other software packages are distributed via the CHASC GitHub Astrostatistics Software Library. In addition, CHASC organizes many sessions at meetings of statisticians to highlight newly developed methods of general interest, and at meetings of astronomers sometimes to convey new methods and other times with a more basic educational emphasis. Van Dyk (PI, Imperial) was the original founder of CHASC and has been its overall leader since. He is an international leader in astrostatistics with specific expertise in Bayesian methodology, multi-level models, and EM-like and MCMC algorithms. He has published extensively in leading astrophysical and statistical journals. |
| Collaborator Contribution | Meng (PI, Harvard) has extensive research experience in statistical modeling, fitting, and improvement, in developing methods for complex incomplete data, and in the interplay of inferential perspectives for complex model fitting and estimation. He is also a leading voice in promoting principled data scientific and statistical methods, especially in his role as the Founding Editor-in-Chief for Harvard Data Science Review, a fast expanding international forum for perspectives, education, and research in data science. Lee (PI, Davis) has deep expertise in image processing, large scale computations, spatio-temporal modeling, time series and change point problems. He publishes frequently in leading statistics and engineering outlets. He and van Dyk are two of the three co-founders of the ASA Astrostatistics Interest Group. Chen is an alumna of the Center where she led a major effort on the calibration project, and has been involved in multiple collaborative astrostatistics projects since joining University Michigan. The astronomers (Kashyap and Siemiginowska, Center for Astrophysics | Harvard & Smithsonian) are leading experts in the analysis of high energy astrophysics data. They have vast experience with instrument calibration, astronomical software analysis systems, and are deeply involved with the development of methods, algorithms, and publicly available software for Chandra data. In consultation with these experts the statistics PIs (Meng, van Dyk, Lee, and Chen) oversee the entire project, with the PI in the lead institute (Meng) as the overall coordinator and convener. Research carried out simultaneously at the four institutions in all stages of the project. |
| Impact | Fan et al. (2023), as well as the preprint: Meyer et al. (2023+ , arXiv:2207.09327) which is under review a ApJ. |
| Description | Collaboration with Dr. Aneta Siemiginowska |
| Organisation | Harvard University |
| Department | Harvard-Smithsonian Center for Astrophysics |
| Country | United States |
| Sector | Academic/University |
| PI Contribution | Multi-disciplinary research. Statistical contributions to solve intricate astrophysical problems. |
| Collaborator Contribution | Multi-disciplinary research. Expertise in astrophysics and astrostatistics to solve intricate astrophysical problems. |
| Impact | We are currently in the process of writing a paper for publication in a scientific journal. The collaboration led to several secondments to Harvard-Smithsonian Center for Astrophysics. One of these secondments was during the time of this award, from October to December 2023 (for around two months). |
| Start Year | 2023 |
| Description | Collaboration with Dr. Vinay Kashyap |
| Organisation | Harvard University |
| Department | Harvard-Smithsonian Center for Astrophysics |
| Country | United States |
| Sector | Academic/University |
| PI Contribution | Multi-disciplinary research. Statistical contributions to solve intricate astrophysical problems. |
| Collaborator Contribution | Multi-disciplinary research. Expertise in astrophysics and astrostatistics to solve intricate astrophysical problems. |
| Impact | We are currently in the process of writing a paper for publication in a scientific journal. The collaboration led to several secondments to Harvard-Smithsonian Center for Astrophysics. One of these secondments was during the time of this award, from October to December 2023 (for around two months). |
| Start Year | 2022 |
| Description | Collaboration with Prof. Roberto Trotta |
| Organisation | International School for Advanced Studies |
| Country | Italy |
| Sector | Academic/University |
| PI Contribution | Multi-disciplinary research. Statistical contributions to solve intricate astrophysical problems, and development of novel statistical methodology inspired by problems in astrophysics. |
| Collaborator Contribution | Multi-disciplinary research. Expertise in astrophysics and astrostatistics to solve intricate astrophysical problems. |
| Impact | We are currently in the process of publishing one paper in the Monthly Notices of the Royal Astronomical Society, and we are working on two additional papers that should be ready for submission for publication this year. The collaboration is multi-disciplinary. Prof. Trotta at SISSA is a trained astrophysicists, and Prof. van Dyk and I at Imperial College are trained statisticians. The collaboration has led to several secondments at SISSA. One of the secondments was during the time of this award, in October 2023 (for three weeks). |
| Start Year | 2020 |
| Title | Jolideco: a Python library for Joint Likelihood deconvolution |
| Description | Jolideco is a Python library for Joint Likelihood deconvolution of a set of observations in the presence of Poisson noise. It can be used to combine data from multiple x-ray instruments such as Chandra, XMM-Newton or gamma-ray instruments such as Fermi-LAT or H.E.S.S.. In general Jolideco is designed to work with data from any instrument affected by Poisson noise. It has a nice user interface and is designed to be simple to use. |
| Type Of Technology | Software |
| Year Produced | 2024 |
| Open Source License? | Yes |
| Impact | olideco is a Python package for Joint Likelihood Deconvolution of astronomical images affected by Poisson noise. It allows you to deblur and denoise images and do a joint image reconstruction of multiple images from different instruments, while taking their specific instrument response functions, such as point spread functions, exposure and instrument specific background emission into account. To ensure a high fidelity of reconstructed features in the images, Jolideco relies on a patch based image prior, which is based on a Gaussian Mixture Model (GMM). |
| URL | https://zenodo.org/doi/10.5281/zenodo.11267661 |
| Title | SRGonG |
| Description | Data from high-energy observations are usually obtained as lists of photon events. A common analysis task for such data is to identify whether diffuse emission exists, and to estimate its surface brightness, even in the presence of point sources that may be superposed. We have developed a novel nonparametric event list segmentation algorithm to divide up the field of view into distinct emission components. We use photon location data directly, without binning them into an image. We first construct a graph from the Voronoi tessellation of the observed photon locations and then grow segments using a new adaptation of seeded region growing that we call Seeded Region Growing on Graph, after which the overall method is named SRGonG. Starting with a set of seed locations, this results in an oversegmented data set, which SRGonG then coalesces using a greedy algorithm where adjacent segments are merged to minimize a model comparison statistic; we use the Bayesian Information Criterion. |
| Type Of Technology | Software |
| Year Produced | 2023 |
| Open Source License? | Yes |
| Impact | Using SRGonG we are able to identify point-like and diffuse extended sources in the data with equal facility. We validate SRGonG using simulations, demonstrating that it is capable of discerning irregularly shaped low-surface-brightness emission structures as well as point-like sources with strengths comparable to that seen in typical X-ray data. We demonstrate SRGonG's use on the Chandra data of the Antennae galaxies and show that it segments the complex structures appropriately. |
| URL | https://iopscience.iop.org/article/10.3847/1538-3881/aca478 |
| Title | TD-CARMA |
| Description | Fit CARMA processes to AGN light curves to estimate gravitational lens time delays. Uses MultiNest for Bayesian inference, to efficiently sample from multimodal posterior distribution of time delay parameter (and CARMA parameters). |
| Type Of Technology | Software |
| Year Produced | 2023 |
| Open Source License? | Yes |
| Impact | Cosmological parameters encoding our understanding of the expansion history of the universe can be constrained by the accurate estimation of time delays arising in gravitationally lensed systems. We propose TD-CARMA, a Bayesian method to estimate cosmological time delays by modeling observed and irregularly sampled light curves as realizations of a continuous auto-regressive moving average (CARMA) process. Our model accounts for heteroskedastic measurement errors and microlensing, an additional source of independent extrinsic long-term variability in the source brightness. The semiseparable structure of the CARMA covariance matrix allows for fast and scalable likelihood computation using Gaussian process modeling. We obtain a sample from the joint posterior distribution of the model parameters using a nested sampling approach. This allows for "painless" Bayesian computation, dealing with the expected multimodality of the posterior distribution in a straightforward manner and not requiring the specification of starting values or an initial guess for the time delay, unlike existing methods. In addition, the proposed sampling procedure automatically evaluates the Bayesian evidence, allowing us to perform principled Bayesian model selection. TD-CARMA is parsimonious, and typically includes no more than a dozen unknown parameters. We apply TD-CARMA to six doubly lensed quasars HS2209+1914, SDSS J1001+5027, SDSS J1206+4332, SDSS J1515+1511, SDSS J1455+1447, and SDSS J1349+1227, estimating their time delays as -21.96 ± 1.448, 120.93 ± 1.015, 111.51 ± 1.452, 210.80 ± 2.18, 45.36 ± 1.93, and 432.05 ± 1.950, respectively. These estimates are consistent with those derived in the relevant literature, but are typically two to four times more precise. |
| URL | https://github.com/astrostat/TD-CARMA |
| Description | Banff International Research Station Workshop on "Astrostatistics in Canada and Beyond" |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Professor van Dyk was an invited participant in this multi-disciplinary residential workshop on "Astrostatistics in Canada and Beyond" held at the Banff International Research Station Workshop (for the Mathematical Sciences). The workshop was held in October 2023. BIRS is a world-leading centre for research in the Mathematical and related sciences, hosting focused discussion-oreinted weeklong workshops on pressing topics in mathematics, statistics, and their applications. This workshop including leading researchers in statistical methods for astrophysics from around the world, including astronomers, statisticians, physicists, and computer scientists. Professor van Dyk gave a well-received invited talk on supervised learning when the training set is not representative of the target population. |
| Year(s) Of Engagement Activity | 2023 |
| URL | https://www.birs.ca/events/2023/5-day-workshops/23w5094 |
| Description | Banff International Research Station Workshop on Systematic Effects and Nuisance Parameters in Particle Physics Data Analyses |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Professor van Dyk was an invited participant in this multi-disciplinary residential workshop on "Systematic Effects and Nuisance Parameters in Particle Physics Data Analyses" held at the Banff International Research Station Workshop (for the Mathematical Sciences). The workshop was held in April 2023. BIRS is a world-leading centre for research in the Mathematical and related sciences, hosting focused discussion-oreinted weeklong workshops on pressing topics in mathematics, statistics, and their applications. This workshop including leading researchers in statistical methods for particle physics from around the world. Professor van Dyk gave a well-received invited talk on Bayesian methods to handle systematic errors. |
| Year(s) Of Engagement Activity | 2023 |
| URL | https://www.birs.ca/events/2023/5-day-workshops/23w5096 |
| Description | CHASC Seminar |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Professor van Dyk co-organises the CHASC Seminar with programme partners at UC Davis, Harvard, and the Harvard-Smithsonian Center for Astrophysics. The seminar is run monthly or fortnightly. Speakers present new state-of-the-art statistical methods of interest to astronomers and/or statistical challenges arising in astronomy with the aim of sparking new collaborations and methodological development. The seminar is held in person with video links and attacks participants from the University of California, Davis, Imperial College London, Harvard University, the Harvard Smithsonian, University of Crete, Cambridge University, Simon Fraser University, University of Toronto, and other leading academic centres. |
| Year(s) Of Engagement Activity | 2022,2023,2024,2025 |
| URL | https://hea-www.harvard.edu/astrostat/CHASC_2223/ |
| Description | CMStat 2023 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Professor van Dyk was invited to present his research on "Stratified Learning: Improved Learning under Covariate Shift" at this important international conference held in Berlin in December 2023. The session was well-attended and the presentations were followed by a lively discussion of the work funded by this grant. |
| Year(s) Of Engagement Activity | 2023 |
| URL | https://www.cmstatistics.org/CMStatistics2023/ |
| Description | Cosmological Inference in High Dimension, Kavli Science Focus Meeting (Cambridge, UK, November 2024) |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Other audiences |
| Results and Impact | Delivered an invited presentation at the Kavli Science Focus Meeting in Cambridge (UK). The presentation sparked engaging discussions and interest among attendees. |
| Year(s) Of Engagement Activity | 2024 |
| Description | IMS International Conference on Statistics and Data Science |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Professor van Dyk was invited to present his research on "Stratified Learning: Improved Learning under Covariate Shift" at this important international conference held in Lisbon in December 2023. |
| Year(s) Of Engagement Activity | 2023 |
| URL | https://sites.google.com/view/icsds2023 |
| Description | JSM 2022 Discussion |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Professor van Dyk was the invited discussant at a technical session on "Open Problems in Astrophysics" at the Joint Statistical Meetings held in Washington DC in August 2022. The JSM is the largest gathering of professional statisticians in the world and attacks researchers and practitioners working in academia, government, and industry. The session itself was well attended and sparked a lively discussion giving Professor van Dyk an opportunity to discuss ongoing research funded by this project. |
| Year(s) Of Engagement Activity | 2022 |
| URL | https://ww2.amstat.org/meetings/jsm/2022/onlineprogram/ActivityDetails.cfm?SessionID=223148 |
| Description | JSM 2023 Discussant |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Professor van Dyk was the invited discussant at a technical session on "Uncertainty Quantification in Astronomy" at the Joint Statistical Meetings held in Toronto, Ontario in August 2023. The JSM is the largest gathering of professional statisticians in the world and attacks researchers and practitioners working in academia, government, and industry. As in 2022, the session was well attended and sparked a lively discussion giving Professor van Dyk an opportunity to discuss ongoing research funded by this project |
| Year(s) Of Engagement Activity | 2023 |
| URL | https://ww2.amstat.org/meetings/jsm/2023/ |
| Description | Multidisciplinary Seminars |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | Delivered presentation on statistical methods in astronomy to groups of statisticians and astronomers, with the aim of encouraging interdisciplinary research. Held one-on-one and small group meetings with groups of researchers and postgraduate students to discuss particular project ideas. Events were held Switzerland, South Korea, and the US (x3). |
| Year(s) Of Engagement Activity | 2023,2024,2025 |
| Description | Poster presentation at "Statistical Challenges in Modern Astronomy VIII" |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Presented a poster at the conference "Statistical Challenges in Modern Astronomy VIII", in order to disseminate information about a research project on Bayesian modelling of photon pile-up in the charge-coupled devices onboard X-ray telescopes. An impact was increased awareness about the problem and potential solutions. |
| Year(s) Of Engagement Activity | 2023 |
| Description | Poster presentation at 25 Years of Chandra Symposium |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Several hundred conference participants viewed my poster presentation, sparking many questions and discussions. |
| Year(s) Of Engagement Activity | 2024 |
| URL | https://cxc.harvard.edu/cdo/symposium_2024/ |
| Description | Presentation at "Bayes Comp 2023" |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Gave a presentation on the use of Simulation-based inference for models of photon pile-up in the charge-coupled devices onboard X-ray telescopes. |
| Year(s) Of Engagement Activity | 2023 |
| Description | Presentation at PHYSTAT -- Statistics meets Machine Learning (Imperial College London, UK) |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Other audiences |
| Results and Impact | Delivered a presentation for the 2024 PHYSTAT meetings at Imperial College London. The presentation sparked engaging discussions and interest among attendees. |
| Year(s) Of Engagement Activity | 2024 |
| Description | Presentation at Statistical Society of Canada 2024 Annual Meeting |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Schools |
| Results and Impact | Around 30 conference attendees attended my conference presentation, which sparked questions and discussion afterwards (the discussion with one participant led to several additional experiments related to the project). |
| Year(s) Of Engagement Activity | 2024 |
| Description | Presentation at World Meetings of the International Society for Bayesian Analysis (July 2025) |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | I served as an invited discussant in scientific session on "Bayesian Methods for Astrophysics Discovery based on Big Data" |
| Year(s) Of Engagement Activity | 2024 |
| URL | https://www.unive.it/web/en/5492/programme |
| Description | RISE-CHASC Workshop : August 2 and 3, 2022 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | The RISE-CHASC Workshop was held at the Harvard Smithsonian Center for Astrophysics. Hosting more than 850 scientists, engineers, and support staff, the CfA is among the largest astronomical research institutes in the world. These researchers are the primary users of the methods developed under this project. The workshop provided us an excellent opportunity to disseminate recently developed methodology to end-users. Presentations with extended question-and-answer sessions and open discussion were held on a range of topics funded by this programme (e.g., Modelling populations off-ray sources, New methods for estimating cosmological parameters, Bayesian astrophysical image analysis: extended sources and boundaries, Bayesian source detection, Machine-learning based source classification, Flare detection, Non-parametric image segmentation, etc.) A range of follow-up discussions indicated interest in our work and possible/likely update our our research outputs. |
| Year(s) Of Engagement Activity | 2022 |
| URL | https://hea-www.harvard.edu/AstroStat/CHASC_2122/workshop.html |
| Description | Short Course in Astrostatistics at (Center for Astrophysics | Harvard & Smithsonian, Feb 2025) |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | This course will review the mathematical foundations of Bayesian methods, discuss techniques for specifying prior distributions, and study computational techniques including MCMC. Particular attention will be paid to Bayesian multi-level models- statistical models with multiple levels of structure. These models have wide applicability in astronomy and astrophysics because information is often available on multiple "levels" that allow complex models to be represented as a sequence of simple sub-models. Hierarchical models are a particular type of multi-level model that describe a population of objects (stars, pixels, etc.) with object-level parameters following a common distribution (specified in a lower level of the multi-level model). In the course we will discuss how Bayesian hierarchical models facilitate a concept called "shrinkage," which can produce better estimates of the parameters describing the objects in populations than can simple object-by-object estimators. We will demonstrate advantages of using multi-level/hierarchical models and shrinkage estimators via examples from cosmology. |
| Year(s) Of Engagement Activity | 2025 |
| Description | Short Course on Bayesian Astrostatistics (University of Padua, May 2024) |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | This course will review the mathematical foundations of Bayesian methods, discuss techniques for specifying prior distributions, and study computational techniques including MCMC. Particular attention will be paid to Bayesian multi-level models- statistical models with multiple levels of structure. These models have wide applicability in astronomy and astrophysics because information is often available on multiple "levels" that allow complex models to be represented as a sequence of simple sub-models. Hierarchical models are a particular type of multi-level model that describe a population of objects (stars, pixels, etc.) with object-level parameters following a common distribution (specified in a lower level of the multi-level model). In the course we will discuss how Bayesian hierarchical models facilitate a concept called "shrinkage," which can produce better estimates of the parameters describing the objects in populations than can simple object-by-object estimators. We will demonstrate advantages of using multi-level/hierarchical models and shrinkage estimators via examples from cosmology. |
| Year(s) Of Engagement Activity | 2024 |