Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 63 tok/s
Gemini 2.5 Pro 44 tok/s Pro
GPT-5 Medium 31 tok/s Pro
GPT-5 High 32 tok/s Pro
GPT-4o 86 tok/s Pro
Kimi K2 194 tok/s Pro
GPT OSS 120B 445 tok/s Pro
Claude Sonnet 4.5 35 tok/s Pro
2000 character limit reached

Studying the Supernova Absolute Magnitude Constancy with Baryonic Acoustic Oscillations (2404.07182v2)

Published 10 Apr 2024 in astro-ph.CO

Abstract: In this proceeding we review and expand on our recent work investigating the constancy of the absolute magnitude $M_B$ of Type Ia supernovae. In it, we used baryonic acoustic oscillations (BAO) to calibrate the supernova data and to check whether the resulting $M_B$ is constant. We used non-parametric methods like Gaussian processes and artificial neural networks to reconstruct $M_B(z)$. Here we elaborate on the results by putting them in the context of other studies investigating possible non-constant $M_B$ and the impact of the distance-duality relation. We also present some numerical details on the calculations in the original paper and new non-parametric reconstructions, including a conservative model-independent fit, confirming its main results. Notably, we see that $M_B$ remains constant within $1\sigma$, with a possible jump around $z = 0.01 - 0.15$. Furthermore, the observed distribution of $M_B(z)$ cannot be described by a single Gaussian, displaying multiple peaks and tails. The choice of the only remaining parameter -- the sound horizon $r_d$ leads to a tension in the $M_B-r_d$ plane. Fitting different non-constant $M_B(z)$ models does not significantly improve the fit and there is no preference for any of the models by the statistical measures we employ.

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Authors (1)

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets

This paper has been mentioned in 3 posts and received 5 likes.