Greg Bryan, Columbia University
In its first year, the Simons Collaboration on Learning the Universe collaboration has launched a wide range of projects to develop new methodology and infrastructure required to perform fast and accurate cosmological galaxy formation simulations and use these to infer initial conditions and parameters from data in a rigorous and effective way. The first annual meeting will bring together group members to present talks that will evaluate these efforts, (re-)connect the various threads through the creation of shared pipelines and other joint infrastructure, and think deeply and carefully about missing pieces and next steps.
Throughout the year, individual projects and working groups are coordinated via regular, smaller, and often virtual meetings. We look forward to gathering at the Simons Foundation, in person, for invigorating discussions and excellent talks, which will promote overall progress as well as further develop the collaboration’s goals.
Thursday, September 15
8:30 AM CHECK-IN & BREAKFAST 9:30 AM Greg Bryan | The Collaboration in Year One 10:30 AM BREAK 11:00 AM Ben Wandelt | Learning the Universe with Implicit Inference 12:00 PM LUNCH 1:00 PM Laurence Perreault-Levasseur | TBA 2:00 PM BREAK 2:30 PM Eve Ostriker | Report from the WG on Resolved Modeling of the Interstellar Medium, Star Formation and Galactic Winds 3:30 PM BREAK 4:00 PM Shirley Ho | Update and Musing from the Accelerated Forward Modeling Working Group 5:00 PM DAY ONE CONCLUDES
Friday, September 16
8:30 AM CHECK-IN & BREAKFAST 9:30 AM Rachel Somerville | Status of the Observational 10:30 AM BREAK 11:00 AM Guilhem Lavaux | Progress of the BORG Working Group: Success and Challenges for 2023 12:00 PM LUNCH 1:00 PM Shy Genel | Status of the Training Set Generation Working Group and CAMELS 2:00 PM MEETING CONCLUDES
The Collaboration in Year One
Collaboration Director Greg Bryan will review the status of collaboration efforts, with a particular focus on near-term (and long-term) deliverables and areas that need additional efforts. He will also lead a discussion on collaboration structure and infrastructure. Finally, Greg will provide updates from the Black Hole working group, briefly reviewing the various current projects and discussing where we hope to be in year two.
Status of the Training Set Generation Working Group and CAMELS
Shy Genel will review the current status of the TSG efforts and CAMELS on a number of fronts. This includes a review of the wide range of simulations already carried out (many in addition to the original CAMELS data release), as well as a discussion of possible future training set runs. Finally, there will be an update and discussion on a recent joint LtU/CAMELS effort to develop novel ways to intelligently choose the parameter space coverage (in both cosmological and astrophysical parameters) for the expensive simulations that maximize our ability to train emulators or constrain initial conditions/parameters (without “wasting” a lot of simulations on completely unrealistic universes).
Update and Musing from the Accelerated Forward Modeling Working Group
Shirley Ho will provide updates on work exploring and developing frameworks for accelerated forward models. Ho will showcase recent efforts by the SIMBig collaboration designed to generate accurate catalog-level galaxy models that include models for a variety of effects that arise in real observational surveys (including fiber collisions and other effects that can impact inferred properties). Ho will lead the general discussion on the way forward for LtU in terms of what we need for next steps in this working group.
Institut d’Astrophysique de Paris
Progress of the BORG Working Group: Success and Challenges for 2023
The BORG method and software have already proven some success in the analysis of galaxy surveys such as 2M++ and SDSS. The working group is now seeking to make the inference sufficiently robust, fast and accurate at intermediate scales (1-10 Mpc/h) to allow for sampling cosmological parameters jointly with the initial conditions. We will highlight recent progress on that front and the promises of the work plan for the coming year. These activities are notably synergistic with the TSG working group, the Accelerated Simulation working group and the Implicit Likelihood Inference working group.
Report from the WG on Resolved Modeling of the Interstellar Medium, Star Formation and Galactic Winds
The evolution of galaxies, including the stellar component that is the main tracer of matter in the universe, depends on physical processes that occur within the interstellar medium (ISM). Gravitational collapse of ISM gas leads to star formation (SF), and then energy is returned to the ISM from recently formed massive stars. Since the return of energy sets the ISM turbulent, thermal and magnetic pressure, this regulates future star formation. At the same time, energy return (in the form of stellar winds and radiation, supernova explosions and cosmic rays) drives outflows of warm and hot thermal gas and relativistic particles into the circumgalactic medium. The mass and energy flows out of galaxies as galactic winds (GWs) reduce future star formation over long timescales. While these processes are known to be essential to the evolution of galaxies, it is not possible to include them directly in cosmological simulations of galaxy formation due to limited spatial/mass resolution. To date, cosmological simulations as well as semi-analytic models have primarily relied on subgrid models for SF, the ISM and GWs with simple functional forms and parameters that are set via empirical tuning. This means that the galaxy formation simulations and semi-anlytic models are not fundamentally predictive; they may obtain the “right answer” for the wrong reason, and/or they may mask issues with standard cosmological theory. A key goal of the LtU collaboration is to move from current subgrid models of ISM, SF and GW processes that are empirically tuned to new models that are based on calibrations from resolved radiation-magnetohydrodynamic simulations that directly treats the relevant physics. The simulations we are using include star-forming ISM “patch” models with a range of galactic conditions, as well as global simulations in dwarfs via cosmological zooms to reach comparable resolution (~1–10 pc) and global simulations in spirals that have a resolution of 10–100 pc. The first two types of simulations enable us to directly model the required physics, with “patch” simulations providing full exploration of parameter space and global dwarfs testing sensitivity to geometry and assessing wind propagation on scales beyond a few kpc. The third type of simulation provides a bridge to lower-resolution cosmological galaxy formation simulations. In addition to the simulations, we are working on algorithms that use robust cosmological variables (i.e., variables that are well resolved in cosmological models) as inputs to calibrated SF rate predictors, and on algorithms for launching and recouping multiphase winds. Eve Ostriker will provide an update on the work to date that has been accomplished by the working group members on the above tasks, and on plans for the coming year. Ostriker will also discuss networking with the other WG to implement our models.
Status of the Observational
Rachel Somerville will discuss current plans for the generation of a set of pipelines (developed in conjunction with many of the other working groups) to create mock galaxy and secondary CMB maps. These include a stellar synthesis pipeline (SynthObs1) which takes snapshots from cosmological hydrodynamic simulations and generates dust-free (and dust-extinct) stellar and nebular emission properties in specific bands. A second pipeline (SynthObs2) will use the first pipeline to create light cones and realistic photometric data. A pair of addition pipelines (SynthObs3 and SynthObs4) will generate CMB secondary anisotropies from cosmological hydrodynamics simulations and then project to the sky with realistic observational effects.
Institut d’Astrophysique de Paris
Learning the Universe with Implicit Inference
Implicit inference is an approach to doing Bayesian statistics that has unlocked a large class of previously intractable problems. Implicit inference can work even when the likelihood and/or the prior are intractable distributions because it only requires the ability to generate parameters and data of interest. It is made possible through multiple recent advances in machine learning and deep learning: the efficient representation of multivariate probability density functions; fast generative models for parameters, signal and data; and a dictionary that allows us to translate posterior inference into optimization problems that can be solved using stochastic gradient descent. Ben Wandelt will outline the plan to employ these techniques to “Learn the Universe” and identify outstanding problems as well as promising steps towards their solutions, such as: the generalization to very high-dimensional parameters space; integrating physics priors and constraints with machine-learning approaches; robustness to model imperfections; and validation of the resulting inferences.
Participation & Funding
Participation in the meeting falls into the following four categories. An individual’s participation category is communicated via their letter of invitation.
Group A – PIs and Speakers
The foundation will arrange and pay for all air and train travel to the conference as well as hotel accommodations and reimbursement of local expenses. Business-class or premium economy airfare will be booked for all flights over five hours.
Group B – Out-of-town Participants
The foundation will arrange and pay for all air and train travel to the conference as well as hotel accommodations and reimbursement of local expenses. Economy-class airfare will be booked for all flights.
Group C – Local Participants
Individuals in Group C are considered local and will not receive financial support, but are encouraged to enjoy all conference-hosted meals.
Group D – Remote Participants
Individuals in Group D will participate in the meeting remotely. Please register at the link above and a remote participation link will be sent to you approximately two weeks prior to the meeting.
Travel & Hotel
Air and Train
The foundation will arrange and pay for all air and train travel to the conference for those in Groups A and B. Please provide your travel specifications by clicking the registration link above. If you are unsure of your group, please refer to your invitation sent via email.
For participants in Groups A & B driving to Manhattan, The James NoMad Hotel offers valet parking. Please note there are no in-and-out privileges when using the hotel’s garage, therefore it is encouraged that participants walk or take public transportation to the Simons Foundation.
Participants in Groups A & B who require accommodations are hosted by the foundation for a maximum of three nights at The James NoMad Hotel. Any additional nights are at the attendee’s own expense. To arrange accommodations, please register at the link above.
The James NoMad Hotel
22 E 29th St
New York, NY 10016
(between 28th and 29th Streets)
For driving directions to The James NoMad, please click here.
ALL in-person meeting attendees must be vaccinated against the COVID-19 virus with a World Health Organization approved vaccine, be beyond the 14-day inoculation period of their final dose, and provide proof of vaccination upon arrival to the conference. Acceptable vaccines can be found at the bottom of this page on the WHO’s site.
Individuals in Groups A & B will be reimbursed for meals not hosted by the Simons Foundation as well as local expenses, including ground transportation. Additional information in this regard will be emailed on the final day of the meeting.
Meeting Questions and Assistance
Manager, Events and Administration, MPS, Simons Foundation