Pith sign in

REVIEW 1 cited by

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1210.7768 v1 pith:OHV5I5S2 submitted 2012-10-29 astro-ph.IM

The POLARBEAR Experiment

classification astro-ph.IM
keywords polarbearexperimentalongb-modecharacterizationchilefocalgravitational
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

We present the design and characterization of the POLARBEAR experiment. POLARBEAR will measure the polarization of the cosmic microwave background (CMB) on angular scales ranging from the experiment's 3.5 arcminute beam size to several degrees. The experiment utilizes a unique focal plane of 1,274 antenna-coupled, polarization sensitive TES bolometers cooled to 250 milliKelvin. Employing this focal plane along with stringent control over systematic errors, POLARBEAR has the sensitivity to detect the expected small scale B-mode signal due to gravitational lensing and search for the large scale B-mode signal from inflationary gravitational waves. POLARBEAR was assembled for an engineering run in the Inyo Mountains of California in 2010 and was deployed in late 2011 to the Atacama Desert in Chile. An overview of the instrument is presented along with characterization results from observations in Chile.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. BROOM: a python package for model-independent analysis of microwave astronomical data

    astro-ph.CO 2026-04 unverdicted novelty 4.0

    BROOM is a Python package that applies ILC and GILC techniques for model-independent separation of CMB, SZ, and foreground signals in microwave data along with diagnostic and simulation utilities.