# Exploring the CMB Power Suppression in Canonical Inflation Models

## TL;DR

This paper investigates whether canonical inflation models with step-like features in the potential can explain the observed suppression in CMB power at certain multipoles, finding that such models generally do not fit the data well without fine-tuning.

## Contribution

The study demonstrates that canonical two-derivative inflation models with sharp potential steps cannot adequately reproduce the CMB power suppression without fine-tuning or breakdown of slow-roll approximations.

## Key findings

- Standard slow-roll approximations are insufficient for sharp features.
- Numerical solutions show poor fit to observed CMB multipoles.
- Fine-tuning is required for models to match the data.

## Abstract

There exists some evidence of a suppression in power in the CMB multipoles around $l \sim 20-30$. If taken seriously, this is in tension with the simplest inflationary models driven by a single scalar field with a standard type of slowly varying potential function $V(\phi)$. Such potential functions generate a nearly scale invariant spectrum and so they do not possess the requisite suppression in power. In this paper we explore if canonical two-derivative inflation models, with a step-like feature in the potential, can improve agreement with data. We find that improvement can be made when one utilizes the standard slow-roll approximation formula for the power spectrum. However, we find that in order to have a feature in the power spectrum that is sufficiently localized so as to not significantly disrupt higher $l$ or lower $l$, the potential's step-like feature must be so sharp that the standard slow-roll approximations break down. This leads us to perform an exact computation of the power spectrum by solving for the Bunch-Davies mode functions numerically. We find that the corresponding CMB multipoles do not provide a good agreement with the data. We conclude that, unless there is fine-tuning, canonical inflation models do not fit this suppression in the data.