## Cobaya: error with DES likelihood

Use of Cobaya. camb, CLASS, cosmomc, compilers, etc.
Zhuangfei Wang
Posts: 27
Joined: October 09 2021
Affiliation: Simon Fraser University

### Cobaya: error with DES likelihood

Hello:

I just encountered an error when running Cobaya with mcmc option and CAMB package. For the likelihood, I simply used "planck_2018_lowl.TT" and "des_y1.joint" with the default parameter settings. But after an hour, it stopped and returned an error as:

---
cobaya.log.LoggedError: Not possible to extrapolate to k=3029.872768378632 1/Mpc (maximum k possible is 3000 1/Mpc).
---
I didn't modify the source of Cobaya or the CAMB theory code. Besides, there is no such error when I ran with --test or --minimizer option, so it is quite confusing to me.

Any reply or suggestion will be appreciated.

Antony Lewis
Posts: 1842
Joined: September 23 2004
Affiliation: University of Sussex
Contact:

### Re: Cobaya: error with DES likelihood

Lowl TT is very unconstraining so you are probably exploring very odd parts of parameter space if you have no priors imposed. You can plot the partial chains to see what it is doing.

Zhuangfei Wang
Posts: 27
Joined: October 09 2021
Affiliation: Simon Fraser University

### Re: Cobaya: error with DES likelihood

Hi Antony. Thanks for your reply. I removed the "planck_2018_lowl.TT" and only keep "des_y1.joint", but I still get the same error. Could it be any other reason?

FYI, the pre-settings for params and sampler in my input yaml file are the same as this example in the middle of the page: https://cobaya.readthedocs.io/en/latest/cosmo_basic_runs.html. Thanks a lot.

Antony Lewis
Posts: 1842
Joined: September 23 2004
Affiliation: University of Sussex
Contact:

### Re: Cobaya: error with DES likelihood

Using just DES is even less constraining. DES only constraints are usually done in a reduced parameter space with some priors.

Antony Lewis
Posts: 1842
Joined: September 23 2004
Affiliation: University of Sussex
Contact:

### Re: Cobaya: error with DES likelihood

You can also set

Code: Select all

stop_at_error=F
to reject points rather than stopping

Zhuangfei Wang
Posts: 27
Joined: October 09 2021
Affiliation: Simon Fraser University

### Re: Cobaya: error with DES likelihood

That makes sense to me. Thanks a lot.

Sunanda Dey
Posts: 1
Joined: October 24 2022
Affiliation: Technion

### Re: Cobaya: error with DES likelihood

Hi,i am also facing the same issue. I tried to use stop_at_error : False in my yaml file. But i am geeting an error. Any one please help me

Minh Nguyen
Posts: 11
Joined: March 21 2016
Affiliation: Leinweber Center for Theoretical Physics, University of Michigan
Contact:

### Re: Cobaya: error with DES likelihood

Hi,

I'm hijacking this thread because I'm running into the same issue. Is there then a way to re-produce fig. 19 and 20 in the Planck 2018 cosmological parameter paper: https://arxiv.org/abs/1807.06209 using cobaya? One probably only need the correct prior ranges in the .yaml file, right? I tried to specify only DES in cobaya-cosmo-generator but the priors do not seem to match what listed in Table 1 of DESY1 paper: https://arxiv.org/abs/1708.01530

According to the paper, the contours were obtained from an implementation of DES likelihood in CosmoMC which I assume to be the virtually same as the one in cobaya. So the .yaml files with the proper priors should be available somewhere, shouldn't they?

Antony Lewis
Posts: 1842
Joined: September 23 2004
Affiliation: University of Sussex
Contact:

### Re: Cobaya: error with DES likelihood

The Cobaya implementation is the same as CosmoMC yes. But it is up to you to set the cosmological parameter priors when you run the chains if what you want is not currently in the cosmo generator. For the Planck results we ran "CMB lensing priors" and "DES priors" which are different. (DES also varied the neutrino mass for most results)

Minh Nguyen
Posts: 11
Joined: March 21 2016
Affiliation: Leinweber Center for Theoretical Physics, University of Michigan
Contact:

### Re: Cobaya: error with DES likelihood

Thanks, Antony! I'm indeed asking for some further hints on how the "DES priors" were set in the Planck analysis (Fig. 19+20). Your answer seems to indicate that for constraints that only include DES, neutrino mass is not fixed to 0.06eV? Further, DES put priors on Omega_m, Omega_b, A_s; I'm not entirely sure how to place those prior ranges on them in cobaya as they would be derived parameters...

Antony Lewis
Posts: 1842
Joined: September 23 2004
Affiliation: University of Sussex
Contact:

### Re: Cobaya: error with DES likelihood

You can sample them, and have omch2, ombh2 etc be derived parameters instead. Planck did not vary neutrino mass by default, but many of the DES paper results did.

Minh Nguyen
Posts: 11
Joined: March 21 2016
Affiliation: Leinweber Center for Theoretical Physics, University of Michigan
Contact:

### Re: Cobaya: error with DES likelihood

Thank you for the hint, really! Today I eventually got the time to try your suggestion. This is probably trivial, and just me being dense, but if I try to do

Code: Select all

likelihood:
des_y1.joint: null
params:
omegab:
prior:
[...]
ref:
[...]
ombh2:
derived: 'lambda omegam, omegab, H0: (omegam-omegab)*(H0/100)**2'

Then I get the error

Code: Select all

*ERROR* Could not find anything to use input parameter(s) {'omegab'}.

As a side note, I found that the DES Y1 flat priors on and is already defined in cosmo_input/input_database.py, and used in

Code: Select all

lensingonly_DES_model
.
How do I make use of that and impose those priors by default for all runs where only DES likelihoods are used? Will I need new preset(s)?

EDIT: Declaring the prior for omegab directly in each .yaml file under cobaya/likelihoods/des_y1/ seems to work for me.

Antony Lewis
Posts: 1842
Joined: September 23 2004
Affiliation: University of Sussex
Contact:

### Re: Cobaya: error with DES likelihood

You probably want "value" rather than "derived" since ombh2 is an indirect input parameter.

You can include snippets of yaml in other yaml using !defaults if you want to share multiple parameters (e.g. see how the Planck likelihoods use it).