BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Dr Jeffrey D. Scargle (NASA Ames Research Center\, US) DTSTART:20211012T160000Z DTEND:20211012T170000Z DTSTAMP:20260422T225757Z UID:Astrostats/1 DESCRIPTION:Title: Adventures in Astronomical Time Series Analysis\nby Dr Jeffrey D. S cargle (NASA Ames Research Center\, US) as part of IAU-IAA Astrostats & As troinfo seminar(archived version by January 2023)\n\n\nAbstract\nWelcome t o a tour of the volatile\, highly active Universe — in stark contrast to earlier serene '"clockwork’’ visions. Innovative data analysis techni ques have illuminated explosive physical processes animating these systems . Examples include a Fourier transform suited to the irregular sampling ch aracteristic of much astronomical data\, but time domain techniques will b e emphasized for these applications: gamma-ray activity in the Crab Nebula \, gamma-ray bursts\, active galactic nuclei\, and gravitational waves. I hope this talk will change some of the ways you carry out statistical data analysi\n LOCATION:https://researchseminars.org/talk/Astrostats/1/ END:VEVENT BEGIN:VEVENT SUMMARY:Prof Ilya Mandel (Monash University\, Australia) DTSTART:20211109T080000Z DTEND:20211109T090000Z DTSTAMP:20260422T225757Z UID:Astrostats/2 DESCRIPTION:Title: Astrostatistics in Gravitational-wave Astronomy\nby Prof Ilya Mande l (Monash University\, Australia) as part of IAU-IAA Astrostats & Astroinf o seminar(archived version by January 2023)\n\n\nAbstract\nModern astronom ical data sets often raise challenges associated with selection biases\, a ccounting for confusion between backgrounds and foregrounds\, and performi ng inference on big data with complex\, multi-parameter models. I will dis cuss some of the techniques that we used to attack these problems\, illust rating them with results from gravitational-wave observations of merging b lack holes … and a bit further afield.\n LOCATION:https://researchseminars.org/talk/Astrostats/2/ END:VEVENT BEGIN:VEVENT SUMMARY:Dr Torsten Enßlin (Max-Planck-Institute for Astrophysics\, German y) DTSTART:20211214T160000Z DTEND:20211214T170000Z DTSTAMP:20260422T225757Z UID:Astrostats/3 DESCRIPTION:Title: Information field theory\, from astronomical imaging to artificial inte lligence\nby Dr Torsten Enßlin (Max-Planck-Institute for Astrophysics \, Germany) as part of IAU-IAA Astrostats & Astroinfo seminar(archived ver sion by January 2023)\n\n\nAbstract\nTurning the raw data of an instrument into high-fidelity pictures of the Universe is a central theme in astrono my. Information field theory (IFT) describes probabilistic image reconstru ction from incomplete and noisy data exploiting all available information. Astronomical applications of IFT are galactic tomography\, gamma- and rad io- astronomical imaging\, and the analysis of cosmic microwave background data. This talk introduces into the basic ideas of IFT\, highlights its a stronomical applications\, and explains its relation with contemporary art ificial intelligence.\n LOCATION:https://researchseminars.org/talk/Astrostats/3/ END:VEVENT BEGIN:VEVENT SUMMARY:Makoto Uemura (Hiroshima University\, Japan) DTSTART:20220111T080000Z DTEND:20220111T090000Z DTSTAMP:20260422T225757Z UID:Astrostats/4 DESCRIPTION:Title: Follow-up observations of galactic transients with astroinformatics \nby Makoto Uemura (Hiroshima University\, Japan) as part of IAU-IAA Astro stats & Astroinfo seminar(archived version by January 2023)\n\n\nAbstract\ nMethods such as Bayesian inference and machine learning have recently bec ome readily available\, and are used not only on state-of-the-art data\, b ut also in various aspects of astronomical research. Our group has a 1.5- m optical telescope in Hiroshima\, Japan\, which is used for time-domain a stronomy. I will talk about the applications of astroinformatics tools for the follow-up observations of galactic transients. The topics include the discriminative model of transients\, decision making based on the informa tion theory\, and reconstruction of the geometrical structure of the accre tion disk.\n LOCATION:https://researchseminars.org/talk/Astrostats/4/ END:VEVENT BEGIN:VEVENT SUMMARY:Dan Foreman-Mackey (Flatiron Institute) DTSTART:20220208T160000Z DTEND:20220208T170000Z DTSTAMP:20260422T225757Z UID:Astrostats/5 DESCRIPTION:Title: Methods for scalable probabilistic inference\nby Dan Foreman-Mackey (Flatiron Institute) as part of IAU-IAA Astrostats & Astroinfo seminar(ar chived version by January 2023)\n\n\nAbstract\nMost data analysis pipeline s in astrophysics now have some steps that require detailed probabilistic modeling. As datasets get larger and our research questions get more ambit ious\, we are often pushing the limits of what our statistical frameworks are capable of. In this talk\, I will discuss recent (and not so recent) d evelopments in the field probabilistic programming that enable rigorous Ba yesian inference with large datasets\, and high-dimensional or computation ally expensive models. In particular\, I will highlight some scalable meth ods for time series analysis using Gaussian Processes\, and some of the op en source tools and computational techniques that have the potential to be broadly useful for accelerating inference in astrophysics.\n LOCATION:https://researchseminars.org/talk/Astrostats/5/ END:VEVENT BEGIN:VEVENT SUMMARY:Renate Meyer (University of Auckland\, New Zealand) DTSTART:20220308T080000Z DTEND:20220308T090000Z DTSTAMP:20260422T225757Z UID:Astrostats/6 DESCRIPTION:Title: Bayesian Nonparametric Spectral Analysis for Gravitational Wave Astrono my\nby Renate Meyer (University of Auckland\, New Zealand) as part of IAU-IAA Astrostats & Astroinfo seminar(archived version by January 2023)\n \n\nAbstract\nThe new era of gravitational wave astronomy truly began on S eptember 14\, 2015 with the sensational first direct observation of gravit ational waves\, when LIGO recorded the signature of the merger of two blac k holes. In the subsequent three observing runs of the LIGO/Virgo network\ , gravitational waves from 90 compact binary mergers have been announced. Moreover\, the future space-based observatory LISA will open the low-freq uency window on gravitational waves and will be sensitive to a vast range of sources including the white dwarf binaries in our Milky Way and mergers of supermassive black holes at the centre of galaxies. Beyond signal dete ction\, a major challenge has been the development of statistical methodol ogy for estimating the physical waveform parameters and quantifying their uncertainties. Bayesian methods and MCMC have played a key role in this ne w era of astrophysics. I will review the statistical methods that enabled the estimation of the waveform parameters. This challenge has also been a key driver for new theoretical and methodological advancements in statisti cs. The call for a more robust instrumental noise characterization aiming at a simultaneous estimation of noise characteristics and gravitational wa ve parameters has triggered ongoing research into Bayesian nonparametric a nalysis of time series. Starting with nonparametric Bayesian approaches to spectral density estimation of univariate Gaussian stationary time series \, I will review novel extensions to multivariate\, non-Gaussian\, and loc ally stationary time series.\n LOCATION:https://researchseminars.org/talk/Astrostats/6/ END:VEVENT BEGIN:VEVENT SUMMARY:Roberto Trotta (SISSA\, Italy) DTSTART:20220614T160000Z DTEND:20220614T170000Z DTSTAMP:20260422T225757Z UID:Astrostats/7 DESCRIPTION:Title: A general-purpose method for supervised learning under covariate shift with applications to observational cosmology\nby Roberto Trotta (SISSA \, Italy) as part of IAU-IAA Astrostats & Astroinfo seminar(archived versi on by January 2023)\n\n\nAbstract\nSupervised machine learning will be cen tral in the analysis of upcoming large-scale sky surveys. However\, select ion bias for astronomical objects yields labelled training data that are n ot representative of the unlabelled target data distribution. This affects the predictive performance with unreliable target predictions and poor ge neralization. I will present StratLearn\, a novel and statistically princi pled method to improve supervised learning under such covariate shift cond itions\, based on propensity score stratification. In StratLearn\, learner s are trained on subgroups ("strata") of the data conditional on the prope nsity scores\, leading to improved covariate balance and much-reduced bias in the model fit. This general-purpose method has promising applications in observational cosmology\, improving upon existing conditional density e stimation of galaxy redshift from Sloan Data Sky Survey (SDSS) data\; in t he classification of Supernovae (SNe) type Ia from photometric data\, it o btains the best reported AUC on the SNe photometric classification challen ge. If time allows\, I'll discuss the embedding of such a classification i nto a full analysis of SNe data to estimate cosmological parameters.\n LOCATION:https://researchseminars.org/talk/Astrostats/7/ END:VEVENT BEGIN:VEVENT SUMMARY:Josh Speagle (Toronto University\, Canada) DTSTART:20220412T160000Z DTEND:20220412T170000Z DTSTAMP:20260422T225757Z UID:Astrostats/8 DESCRIPTION:Title: Statistical Challenges in Stellar Parameter Estimation from Theory and Data\nby Josh Speagle (Toronto University\, Canada) as part of IAU-IAA Astrostats & Astroinfo seminar(archived version by January 2023)\n\n\nAbs tract\nUnderstanding how the Milky Way fits into the broader galaxy popula tion requires studying the properties of other galaxies as well as our own . While it is possible to observe the structure of other galaxies directly \, understanding the structure of our own Galaxy from within requires infe rring the 3-D positions\, velocities\, and other properties of billions of stars. In this talk\, I will discuss some of the statistical challenges i n inferring stellar parameters from modern photometric surveys such as Gai a and SDSS\, focusing in particular on issues with existing theoretical st ellar models\, the complex nature of parameter uncertainties\, and scalabi lity to large datasets. I will then describe some ongoing work trying to s olve these problems using a combination of physics-inspired but data-drive n calibrations along with a host of inference approaches including gradien t-based optimization\, grid-based searches\, importance sampling\, and nes ted sampling.\n LOCATION:https://researchseminars.org/talk/Astrostats/8/ END:VEVENT BEGIN:VEVENT SUMMARY:Takahiko Matsubara (KEK\, Japan) DTSTART:20220510T080000Z DTEND:20220510T090000Z DTSTAMP:20260422T225757Z UID:Astrostats/9 DESCRIPTION:Title: Weakly non-Gaussian formulas of cosmological random fields\nby Taka hiko Matsubara (KEK\, Japan) as part of IAU-IAA Astrostats & Astroinfo sem inar(archived version by January 2023)\n\n\nAbstract\nIn cosmology\, vario us kinds of random fields play important roles\, including 3D distribution s of galaxies and other astronomical objects\, 2D distributions of cosmic microwave background radiations and weak lensing fields\, etc. The feature s of non-Gaussianity in these fields contain a lot of cosmological informa tion. In this talk\, I will present a method to analytically describe the effects of weak non-Gaussianity in field statistics\, such as the peak abu ndance\, peak correlations\, Minkowski functionals\, etc.\n LOCATION:https://researchseminars.org/talk/Astrostats/9/ END:VEVENT BEGIN:VEVENT SUMMARY:Eric Thrane (Monash University\, Australia) DTSTART:20220809T080000Z DTEND:20220809T090000Z DTSTAMP:20260422T225757Z UID:Astrostats/10 DESCRIPTION:Title: The population properties of merging compact binaries from gravitation al waves.\nby Eric Thrane (Monash University\, Australia) as part of I AU-IAA Astrostats & Astroinfo seminar(archived version by January 2023)\n\ n\nAbstract\nWith the publication of the third gravitational-wave transien t catalog (GWTC-3)\, the LIGO and Virgo Collaborations have confidently id entified 90 signals from merging compact binaries. By analysing the morpho logy of each gravitational waveform\, we are able to work out the masses a nd spins of the black holes and neutron stars that source these signals. B y studying the distributions of black-hole mass\, spin\, and distance\, we are painting a picture of the population properties of compact mergers\, providing clues about the fate of massive stars and telling us how and whe re binary black holes are assembled. In this talk\, I describe how we use Bayesian hierarchical modelling to study merging black holes. I emphasise the importance of model checking to avoid faulty conclusions from model mi sspecification.\n LOCATION:https://researchseminars.org/talk/Astrostats/10/ END:VEVENT BEGIN:VEVENT SUMMARY:Shiro Ikeda (Institute of Statistical Mathematics\, Japan) DTSTART:20220913T080000Z DTEND:20220913T090000Z DTSTAMP:20260422T225757Z UID:Astrostats/11 DESCRIPTION:Title: Data Science and Imaging the Black Hole Shadow\nby Shiro Ikeda (In stitute of Statistical Mathematics\, Japan) as part of IAU-IAA Astrostats & Astroinfo seminar(archived version by January 2023)\n\n\nAbstract\nIn Ap ril 2019\, the EHTC (Event Horizon Telescope collaboration) released the f irst image of the M87 black hole shadow and this May\, the black hole shad ow image of our Milky Way galaxy was released. The EHTC has more than 300 members from different backgrounds and countries. I have been involved in this project as a data scientist for more than 8 years and collaborated wi th EHTC members to develop a new imaging method. The EHT is a huge VLBI (v ery long baseline interferometer)\, which is different from optical telesc opes in that a lot of computation is required to obtain a single image. Bl ack hole imaging is also very interesting from the data scientific viewpoi nt. In this talk\, I will explain how the new imaging technique has been d eveloped and the final images were created through our discussions.\n LOCATION:https://researchseminars.org/talk/Astrostats/11/ END:VEVENT BEGIN:VEVENT SUMMARY:Jason McEwen (University College London\, UK) DTSTART:20221011T160000Z DTEND:20221011T170000Z DTSTAMP:20260422T225757Z UID:Astrostats/12 DESCRIPTION:Title: Bayesian model selection for likelihood-based and simulation-based inf erence.\nby Jason McEwen (University College London\, UK) as part of I AU-IAA Astrostats & Astroinfo seminar(archived version by January 2023)\n\ n\nAbstract\nIn the study of cosmology\, where we seek to uncover an under standing of the fundamental physical processes underlying the origin\, con tent\, and evolution of our Universe\, we are not blessed with the ability to perform experiments - rather\, we have only one Universe to observe. In this scenario\, while we are of course interested in estimating the par ameters of models describing the physical processes observed\, we are ofte n most interested in selecting the best underlying model\, which has given rise to the prevalence of Bayesian model selection in cosmology and astro physics. While I will motivate recent developments in Bayesian model sele ction from problems in cosmology and astrophysics\, I will mostly focus on new methodological advances. I will discuss new approaches that leverage ideas across statistics\, optimization and machine learning to bring to b ear the respective strengths of these paradigms to the highly computationa lly challenging problem of Bayesian model selection. In particular\, I wil l review the learnt harmonic mean estimator for both likelihood-based and simulation-based inference and the proximal nested sampling framework for high-dimensional model selection.\n LOCATION:https://researchseminars.org/talk/Astrostats/12/ END:VEVENT BEGIN:VEVENT SUMMARY:A/Prof. Aaron Robotham (University of Western Australia) DTSTART:20221108T080000Z DTEND:20221108T090000Z DTSTAMP:20260422T225757Z UID:Astrostats/13 DESCRIPTION:Title: Exploring the Limits of the Bayesian Universe: How to Tackle Breadth a nd Depth\nby A/Prof. Aaron Robotham (University of Western Australia) as part of IAU-IAA Astrostats & Astroinfo seminar(archived version by Janu ary 2023)\n\n\nAbstract\nIn the last 10 years it is notable that students are much more enthused about projects involving “machine learning”\, b ut it is important we do not lose perspective on the scientific insights s till offered by a comprehensive and pragmatic application of Bayesian prin ciples. Here I will discuss the work my group has undertaken over the last 7 years to build up a fully generative model of galaxies that has culmina ted in the Bayesian modelling software ProFuse (Robotham+ 2022). The posit ive is that encoding our knowledge and ignorance in a Bayesian manner has opened up new insights to physical processes that form galaxies\, the nega tive is that this approach has a high barrier of entry which can be a poor fit to a modern ~3 year PhD\n LOCATION:https://researchseminars.org/talk/Astrostats/13/ END:VEVENT BEGIN:VEVENT SUMMARY:Dr Jessi Cisewski Kehe (University of Wisconsin) DTSTART:20221213T160000Z DTEND:20221213T170000Z DTSTAMP:20260422T225757Z UID:Astrostats/14 DESCRIPTION:Title: Getting something out of nothing: topological data analysis for cosmo logy\nby Dr Jessi Cisewski Kehe (University of Wisconsin) as part of I AU-IAA Astrostats & Astroinfo seminar(archived version by January 2023)\n\ n\nAbstract\nThe transference from data to information is a key component of many areas of research in astronomy and cosmology. This process can be challenging when data exhibit complicated spatial structures\, such as th e large-scale structure (LSS) of the Universe. Methods that target shape- related features may be helpful for summarizing qualitative properties tha t are not retrieved with standard techniques. Topological data analysis ( TDA) provides a framework for quantifying shape-related properties of data . Persistent homology is a popular TDA tool that offers a procedure to re present\, visualize\, and interpret complex data by extracting topological features which may be used to infer properties of the underlying structur es. Persistent homology is used to find different dimensional holes in a dataset across different scales\, where zero-dimensional holes are cluster s\, one-dimensional holes are closed loops\, two-dimensional holes are voi ds\, and so on. The information is summarized in a persistence diagram\, which may be used for further analysis such as visualization\, inference\, or classification. I will give an overview of persistent homology and di scuss its use in some cosmology applications\, such as discriminating LSS under varying cosmological assumptions.\n LOCATION:https://researchseminars.org/talk/Astrostats/14/ END:VEVENT BEGIN:VEVENT SUMMARY:A/Prof. Yuan-Sen Ting (Australian National University) DTSTART:20230110T080000Z DTEND:20230110T090000Z DTSTAMP:20260422T225757Z UID:Astrostats/15 DESCRIPTION:Title: Galaxy Merger Reconstruction with Generative Graph Neural Networks \nby A/Prof. Yuan-Sen Ting (Australian National University) as part of IAU -IAA Astrostats & Astroinfo seminar(archived version by January 2023)\n\n\ nAbstract\nA key yet unresolved question in modern-day astronomy is how ga laxies formed and evolved. The quest to understand how galaxies evolve has led many semi-analytic models to infer the galaxy properties from their m erger history. However\, most classical approaches rely on studying the gl obal connection between dark matter haloes and galaxies\, often reducing t he study to crude summary statistics. The recent advancement in graph neur al networks might open up many new possibilities\; graphs are a natural de scriptor of galaxy progenitor systems – any progenitor system at a high redshift can be regarded as a graph\, with individual progenitors as nodes on the graph. In this presentation\, I will discuss the power of generati ve graph neural networks to connect high-redshift progenitor systems with local observables. We showed that based on equivariant graph normalizing f low\, our model could robustly recover the progenitor systems\, including their masses\, merging redshifts and pairwise distances at redshift z = 2 conditioned on their z = 0 properties. In addition\, the probabilistic nat ure of our model enables other downstream tasks\, including detecting anom alies in galaxy configuration and identifying subtle correlations of the p rogenitor features.\n LOCATION:https://researchseminars.org/talk/Astrostats/15/ END:VEVENT END:VCALENDAR