Pablo talks about an actual observational result from CAMELS, the measurement of the masses of the Milky Way and Andromeda. The results are in agreement with other methods we’ve used to measure the masses of these galaxies.

Pablo: https://pablovd.github.io/
Paper: https://arxiv.org/abs/2111.14874

CAMELS playlist: https://youtu.be/6Vgc72a_VpY&list=PLvy7h0l2rJHq03inVPqYnC3llKt0IwwLT

Leander tells us about work using CAMELS simulations and neural networks to forecast how well future spectral distortion measurements will be able to constrain baryon feedback. The answer is “very well” as it seems the measurements of PIXIE would give even % level measurements of some feedback mechanisms.

Leander: https://phy.princeton.edu/people/leander-thiele
Paper: https://arxiv.org/abs/2201.01663

CAMELS playlist: https://youtu.be/6Vgc72a_VpY&list=PLvy7h0l2rJHq03inVPqYnC3llKt0IwwLT

Andrina tells us about her work using CAMELS and machine learning to constrain baryon feedback using the electron density power spectrum.

The electron density is not itself an observable thing, but it is a good proxy for observable things like the thermal Sunyaev Zeldovich effect and Fast Radio Burst dispersion (or they are good proxies for the electron density).

Andrina is able to get nice constraints on baryon feedback and cosmological parameters within the CAMELS simulations. This sort of observational probe of baryon feedback is going to be an important tool for cosmologists if we want to use smaller scales to do cosmology, and the sort of connections spotted by Andrina and CAMELS will be valuable for improving these probes.

Andrina: https://www.astro.princeton.edu/~anicola/
Paper: https://arxiv.org/abs/2201.04142

CAMELS playlist: https://youtu.be/6Vgc72a_VpY&list=PLvy7h0l2rJHq03inVPqYnC3llKt0IwwLT

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

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 →

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 →