Fundamental physics from the large-scale structure of the Universe

Project Information

FutureLSS

Grant agreement ID: 853291

DOI

10.3030/853291

Project closed

EC signature date 2 October 2019

Start date 1 September 2020

End date 31 August 2025

Funded under

• EXCELLENT SCIENCE - European Research Council (ERC)

Total cost

€ 1 328 066,00

EU contribution

€ 1 328 066,00

Coordinated by

THE UNIVERSITY OF EDINBURGH
United Kingdom

Periodic Reporting for period 3 - FutureLSS (Fundamental physics from the large-scale structure of the Universe)

Reporting period: 2023-09-01 to 2025-02-28

Summary of the context and overall objectives of the project

This project uses data from two experiments, the Dark Energy Spectroscopic Instrument (DESI) and the Euclid space satellite. Both of these experiments measure the distance of millions of galaxies and with that, they map out the density distribution in the Universe. The distribution of galaxies does depend on many properties of the Universe itself. For example, if there is more Dark Energy in the Universe, the Universe expands faster at late times, which will reduce the density of galaxies we measure with these experiments. On the other hand, if there is more dark matter in the Universe, it will lead to a stronger clustering of galaxies and the Universe will look more clumpy. In this project, we calculate summary statistics like the power spectrum or bispectrum from the distribution of galaxies. These summary statistics can be compared to theoretical models of the Universe. Such tests allow us to determine what the Universe is made of (Dark Matter, Dark Energy, Neutrinos, baryons), but they can also help to determine the initial conditions. Currently, we believe that a rapid expansion phase in the early Universe called inflation set the initial conditions for later galaxy formation. This rapid expansion has theoretical motivation but has not been verified convincingly with data. The aim of this project is to (1) expand the summary statistics used for such tests, to optimally exploit the data provided by the two experiments mentioned above, (2) investigate new observables, to test the dynamics of the early Universe, (3) test General Relativity on cosmological scales.
This project addresses age-old questions like "How did the Universe begin?" and "How will the Universe end?". As such, this project has the potential to revolutionize how we see our role in the Universe. Additionally, the analysis we perform on these datasets uses cutting-edge statistical techniques which we generally make publically available. This project already published four statistical analysis tools as Python packages, which now have been downloaded >70000 times significantly impacting/improving statistical analysis far beyond astronomy.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

The DESI experiment has now taken data since 2020 and even though there have been delays due to a covid induced shutdown, the instrument itself has outperformed expectations. Given the current rate of data acquisition, we now expect the 5-year main survey to finish 1 year ahead of time and the bright sub-sample, which is quite important for this project,s might even finish 2 years ahead of time. This provides additional already funded time at the end of the experiment for new science cases. I am currently working as co-lead of a task force within DESI to explore science cases for this additional time. We are also investigating science cases for a 5-year follow-up experiment.
The second experiment involved in this project (the Eucllid space satellite) did encounter some issues caused by the Russian invasion of Ukraine since this satellite was scheduled to be launched with a Russian Soyuz rocket. Since such collaborations with Russia have now stopped, the launch of Euclid was unclear for some time. However, negotiations with SpaceX have now resulted in a new launch window starting on July 1st, 2023 using Falcon 9 rocket. Currently, the Euclid satellite is on its way to the North American launch site. It now seems very certain that this dramatic re-schedule of the launch vehicle will not lead to any delay and we should have the first Euclid data later this year.
The team working on this project has grown following almost exactly the planned hiring schedule given in the original proposal. For the first year, Dr Mike Wang was working on this project alongside me, while in the second year, Dr Richard Neveux and Thiago Mergulhao were hired. Very recently Dr Samuel Brieden joined as a fourth team member. Dr. Alex Smith was hired on a 6 months contract to help with the production of synthetic DESI datasets used during validation.
The main task was the implementation of the bispectrum analysis pipeline, which Mike has now finished and published. While the estimator is now implemented, we are still working on other aspects of the analysis pipeline. Richard has taken on the task of implementing the perturbative model and Samuel has now started working on an analytic pipeline to forecast and validate our constraints. All team members are part of the DESI and Euclid collaborations and we all attend several regular Zoom calls and collaboration meetings.
In the first year of his PhD, Thiago implemented the primordial feature analysis pipeline and applied it to two existing datasets (BOSS and eBOSS), which has now been published. The attached image shows the constraints on linear primordial features and in particular, it shows how the different datasets allow extending the parameter space to higher frequencies. The paper by Thiago did investigate how to optimally exploit these datasets to obtain the best possible constraints. This analysis pipeline is now ready to be applied to DESI and Thiago has already started working with internal mock datasets to include the DESI-specific systematics mitigation.
I am personally leading the effort on the analysis of relativistic effects in DESI where I am organising the weekly Zoom calls, helping to prepare the mock catalogues needed for the analysis and setup the modelling pipeline. The work on relativistic mocks was supported by Dr. Alex Smith who joined the project on a 6 months contract last year.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

I am very pleased with the results we have achieved so far. We used the first half of this grant to prepare for the analysis, which will start soon with the first DESI data catalogue already being prepared. We are well set up to lead the primordial feature analysis (Thiago) and several publications on the bispectrum (Mike, Richard and Samuel). To publish the bispectrum estimator has been a big challenge and took a significant amount of Mike Wang's time. However, this estimator has now been published with user-friendly documentation and we already have received interest from a group in Germany, Italy, the US and the UK to use this estimator. I expect this estimator to be used widely and significantly impact the first analysis of DESI with the first exciting results to be expected in early 2024.
Our forecasts suggest that we should be able to have a first detection of relativistic effects in DESI and given that the year 1 analysis already contains 50% of the bright sample due to the excellent performance of the instrument, the first detection might well happen in this first sample. This is a very novel approach which hasn't been tried so far. The study of instrumental and theoretical systematics will take a few more months, but I do believe that we can finish this project alongside the main DESI science analysis this year.
As part of the project, we also aim to publish the statistical analysis tools. So far we have published two tools for statistical parameter inference (Zeus, https://zeus-mcmc.readthedocs.io/en/latest/ and Pocomc, https://pocomc.readthedocs.io/en/latest/)) which now have acquired >70 000 downloads. We have also published an emulator for the galaxy power spectrum (matryoshka, https://matryoshka-emu.readthedocs.io/en/latest/) and the bispectrum estimator (triumvirate, https://triumvirate.readthedocs.io/en/latest/tutorials/QuickGuide.html).

primordial-feature.png