Hello everyone,
my name is Michael Lehmann. I’m an independent cosmologist and the author of Relaxation-Driven Cyclic Cosmology (RDCC), a minimal one-parameter CPT-symmetric framework that makes sharp, correlated predictions for late-time observables such as fσ₈ and weak lensing.
I have been following the LSST project and the DESC forecasting efforts with great interest. The unprecedented precision of LSST data will allow powerful tests of beyond-ΛCDM models, and I believe RDCC could offer an interesting and falsifiable test case in that context.
I have just joined the forum and would be grateful for any advice on how independent researchers can best contribute to the community and the forecasting work.
Thank you for this open and collaborative space. I look forward to learning from you and hopefully contributing something useful.
Best regards,
Michael Lehmann
1 Like
Hello again,
thank you for approving my account and for the warm welcome to the forum.
Since many of you are working on DESC forecasting and large-scale structure analyses, I wanted to mention a very specific and concrete aspect where my framework (RDCC) makes a testable prediction that might be relevant for LSST: the growth-rate observable fσ₈(z).
In RDCC the single infrared parameter α_IR (fixed to ≈ 0.017 from the scalar spectral tilt) induces a small but tightly correlated percent-level shift
Δ(fσ₈)/fσ₈ ∼ α_IR
that is directly linked to the same parameter controlling n_s − 1 = −2α_IR and ΔN_eff ∼ α_IR.
I was wondering how the current DESC forecasting pipeline handles small, scale-independent modifications to the growth rate at this level. Are there already Fisher forecasts or analysis templates that could test such correlated deviations?
I’m very happy to share the relevant papers (especially Companion V with the global fit and Companion III with the growth-rate derivation) if anyone is interested, they are all openly available on Zenodo.
My main goal is to learn from the community and understand how independent researchers can best contribute to the forecasting efforts. Any advice or pointers would be greatly appreciated.
Thank you again for this open and collaborative space!
Best regards,
Michael Lehmann