Jay tells us about how he has used the CAMELS suit of simulations to improve upon existing galaxy cluster scaling relations (i.e. trends we use to measure cluster masses using observational probes).

One example is using the concentration of ionised gas in a cluster to add a little bit more precision to a Sunyaev Zeldovich effect – mass scaling relation. The value of the concentration makes a small change to the prediction.

Jay specifically uses symbolic regression (or similar algorithms) to find expressions that link the properties of interest (e.g. mass, concentration and SZ effect), thus allowing us as human beings to also gain some intuition from what the machine finds.

Jay: https://jaywadekar.github.io/
Paper: https://arxiv.org/abs/2201.01305

CAMELS playlist: https://www.youtube.com/watch?v=6Vgc72a_VpY&list=PLvy7h0l2rJHq03inVPqYnC3llKt0IwwLT&t=0s

Paco tells us about how CAMELS have used machine learning to be able to predict, with a single galaxy’s properties, the value of Ωm used to simulate the galaxy.

This is fascinating and if a real physical effect has some far-reaching consequences.

– We might one day be able to learn cosmological parameters by studying the Milky Way
– Running a hydrodynamical simulation with the wrong Ωm would mean, in principle, that you’ll never reproduce the exact properties of a galaxy correctly.

CAMELS: https://www.camel-simulations.org/
Paco: https://franciscovillaescusa.github.io/
Paper: https://arxiv.org/abs/2201.02202

CAMELS series overview: https://www.youtube.com/watch?v=6Vgc72a_VpY&t=0s
Playlist of CAMELS video series: https://www.youtube.com/playlist?list=PLvy7h0l2rJHq03inVPqYnC3llKt0IwwLT

This is the first video in a series of videos covering research that the CAMELS group have done. CAMELS are applying machine learning to cosmology, using a suite of 1000s of simulations to train neural networks, see what the networks learn and then try to unveil what it learned in a way we mere humans can understand.

This video does a brief intro to CAMELS as well as the data release (and how to access the data).

CAMELS: https://www.camel-simulations.org/
Paper: https://arxiv.org/abs/2201.01300

CAMELS talk at Cosmology from Home: https://www.youtube.com/watch?v=NxR_kDlHhGM&t=0s
Playlist of CAMELS video series: https://www.youtube.com/playlist?list=PLvy7h0l2rJHq03inVPqYnC3llKt0IwwLT

Dillon Brout, Adam Riess and Dan Scolnic talk about the latest SH0ES measurement of the Hubble parameter, making use of the new Pantheon+ supernovae data set.

The measurement accuracy has reached ± 1.0 km s⁻¹ Mpc⁻¹, and they analysed the data in 67 different possible ways and every time reach a result that is in significant tension with Planck + ΛCDM. Their baseline analysis, the one with the best χ² with the fewest free parameters, is now in 5σ tension, on its own, with Planck.

It isn’t clear where the solution to the Hubble tension will come from, but the fact that all local measurements of H₀ come in above Planck, and that the most accurate measurement is now in 5σ tension is very interesting. It’s worth also noting that the prediction from the early universe + ΛCDM doesn’t rely uniquely on Planck. Other CMB experiments give the same small value, and even just Big Bang Nucleosynthesis and local Baryon Acoustic Oscillation measurements, combined with ΛCDM give a small value of H₀.

So, if this isn’t evidence of new physics in cosmology, it will be a very strange series of errors that is causing it.

Dillon: http://djbrout.github.io/
Adam: https://physics-astronomy.jhu.edu/directory/adam-riess/
Dan: https://scholars.duke.edu/person/dscolnic

The paper (SH0ES): https://arxiv.org/abs/2112.04510
The paper (Pantheon+ data): https://arxiv.org/abs/2112.03863

Charles tells us about his recent work with Camille Bonvin on the dipole anisotropy tension.

We expect there to be dipoles in most observables because of our motion through the (statistically) homogeneous and isotropic universe. However, there appears to be a 4.9σ tension between the magnitude of the dipole as measured from the CMB and as measured from quasars in the local-ish universe. Continue reading →

Tilman tells us about his recent work combining KiDS cosmic shear measurements and Planck measurements of the thermal Sunyaev Zeldovich (tSZ) effect from the cosmic microwave background scattering off hot gas in galaxy clusters and galaxy groups. The long term goal is to use cross-correlation of shear and the tSZ effect to help constrain (or essentially measure) baryon feedback and thus push to smaller scales. Continue reading →

Hironao tells about how the Hyper Suprime Cam survey collaboration have taken their own data, and the BOSS data from SDSS to do a joint cosmology constraint.

Specifically, they take the autocorrelation function of the BOSS galaxies and the cross-correlation of HSC weak lensing data with the BOSS galaxies to break degeneracies between cosmology and galaxy bias, thus allowing the full information in the galaxy data to be usable (or at least, lots more of it).

They work hard to extract information from the small scales in these two probes, using a cosmology emulator and halo occupation model, and they include various consistency tests to show that their analysis is robust. (They also did the analysis blind, to avoid human bias as much as possible).

The final constraints are comparable to recent similar DES and KiDS constraints, and consistent with both, but this analysis have a somewhat different degeneracy in the Ωm and σ8 plane.

Personally, I’d now love to see some sort of combined analysis of all three weak lensing probes as they’re all consistent with each other and I expect the combined constraints would be in a lot of tension with Planck (especially because of this different degeneracy direction).

Hironao: https://sites.google.com/view/hironaomiyatake/home

The paper: https://arxiv.org/abs/2111.02419

Song Huang and Alexie Leauthaud tell us about their new galaxy cluster finder, which uses the stellar mass in the outer region of a galaxy as a method to determine the mass of the galaxy’s cluster.

It feels a bit like magic (to me) that the stars in individual galaxies can be used to weigh the mass of the whole cluster, but like other mass proxies one can devise a scaling relationship between the proxy and the mass – and then the proof is in the empirical pudding. Continue reading →

James Alvey tells us about the general state of BBN in 2021.

He gives a really nice pedagogical overview of the physics that goes into BBN calculations relevant for 2021 observations, talking through each of the relevant epochs (neutrino decoupling, the deuterium bottleneck, etc). Continue reading →

Helen and Francisco (Paco) tell us about their recent work using neural networks to predict the masses of subhalos within simulations. They find that the neural network trained on a subset of the subhalos is very good at predicting subhalo masses for the rest of the data. Continue reading →