Andrej Dvornik and Constance Mahony tell us about the Kilo-Degree Survey (KiDS) and their progress at probing the non-linear scales with data from the survey.

Constance talks about her recent paper showing that non-linear halo bias is absolutely necessary if you want to avoid errors larger than 5σ on the small, non-linear scales.

Andrej takes this and does the analysis on KiDS data to obtain cosmological constraints.

Constance: INSPIRE author page

Constance’s paper: arxiv.org/abs/2202.01790

Andrej: Personal Website

Andrej’s paper: arxiv.org/abs/2210.03110

Silvia tells us about her recent use of Planck satellite data to search for dark matter. She doesn’t use the CMB though, instead she is looking for microwave emission from the galactic centre. For her, the CMB is noise!

If dark matter decay/annihilation leads to electron positron emission then those electrons and positrons would emit microwave light through synchrotron radiation as they travel through the magnetic fields at the galactic centre.

She found that the polarisation signal is a more sensitive probe of this effect than the intensity. The main theory for why this is is that the observed signal in the polarisation has more features (coherent hot and cold spots), whereas the expected dark matter signal should be more smooth. Whereas both the intensity measurement and signal should be more smooth. Therefore, the observed polarisation “cold spots” can best constrain the dark matter signal, especially close to the galactic centre, where the dark matter signal should still be strong. Continue reading →

Colin Hill, Adrien La Posta and Even McDonough tell us about the state of play with early dark energy (EDE). They cover both observation, primarily the CMB; and fundamental physics models that might generate EDE phenomenology. Continue reading →

Alex Amon and Naomi Robertson talk about their recent work analysing all of the Dark Energy Survey (DES), the Hyper Suprime Cam survey (HSC), and the Kilo Degree Survey (KiDS) and the Baryon Oscillation Spectroscopic Survey (BOSS). In particular they look to see whether the three lensing surveys (DES, HSC and KiDS) are consistent with the clustering of galaxies in BOSS. Continue reading →

Dillon Brout tells us about the recent Pantheon+ supernovae catalogue and cosmology results. The cosmology results are excellent, including a factor of two improvement in the “figure of merit” compared to the previous Pantheon result (essentially a halving of error bars). Continue reading →

Sultan tells us about his work training neural networks on the neutral hydrogen density fields in the CAMELS simulations.

He uses a process known as normalising flows to find a mapping between the non-linear, very non-Gaussian 2D projected density field and a different Gaussian field. Once this mapping is found, the idea is that one can do full statistics on the non-linear field, by sampling from the Gaussian one. The bold ambition is to use this process to reduce the need for running computationally expensive hydrodynamical simulations – making it more feasible to get precise cosmological constraints from future surveys. Continue reading →

Lucia tells us about her work with CAMELS trying to overcome the biggest barrier CAMELS faces, small box size. It might never be possible to run 1000s of large volume hydrodynamical simulations simply because the hierarchy of scales is too big (baryon feedback happens on small scales, overcoming sample variance requires very large boxes).

Therefore, to get many many simulation boxes, with baryonic effects in them one option is semi-analytic models. This is what Lucia has and is doing and what she discusses in the video…

Lucia: https://isearch.asu.edu/profile/2606833

Paper: Constraining cosmology with machine learning and galaxy clustering: the CAMELS-SAM suite [2204.02408]

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