Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1802.00489

Optimizing the signal-to-noise ratio of biphoton distribution measurements

Matthew Reichert·Hugo Defienne·Jason W. Fleischer
View original source ↗Read source document ↗Export metadata ↓

Abstract

Single-photon-sensitive cameras can now be used as massively parallel coincidence counters for entangled photon pairs. This enables measurement of biphoton joint probability distributions with orders-of-magnitude greater dimensionality and faster acquisition speeds than traditional raster scanning of point detectors; to date, however, there has been no general formula available to optimize data collection. Here we analyze the dependence of such measurements on count rate, detector noise properties, and threshold levels. We derive expressions for the biphoton joint probability distribution and its signal-to-noise ratio (SNR), valid beyond the low-count regime up to detector saturation. The analysis gives operating parameters for global optimum SNR that may be specified prior to measurement. We find excellent agreement with experimental measurements within the range of validity, and discuss discrepancies with the theoretical model for high thresholds. This work enables optimized measurement of the biphoton joint probability distribution in high-dimensional joint Hilbert spaces.

Explore related subjects

physics.ins-detphysics.opticsquant-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Matthew Reichert, Hugo Defienne, Jason W. Fleischer. 2018-02-05. Optimizing the signal-to-noise ratio of biphoton distribution measurements. https://doi.org/10.1103/physreva.98.013841

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

The Remote Analog to Digital Conversion DAQ System for the TRISTAN Detector Upgrade

The TRISTAN detector is an upgrade to the KATRIN experiment to enable a differential measurement of the tritium $β$-decay spectrum to search for sterile neutrinos with keV masses. This entails performing precision electron spectroscopy with over one thousand silicon drift detector pixels, each responsible for recording incident electron rates of $10^5$ counts per second. A project specific data acquisition (DAQ) system is developed to meet the experimental challenges through a remote analog to digital conversion (RADC) design. In this work, the conceptual design of the RADC DAQ is presented along with the built system for operating the TRISTAN detector upgrade. The system includes flexible signal processing logic and data management that is optimized for the high-rate precision measurement.

physics.ins-det↗
arXiv2026

True Alternating Current Scanning Tunneling Microscope (ACSTM): tunneling on insulators

Scanning Tunneling Microscopy (STM) has revolutionized our atomic scale understanding of surfaces and accelerated progress in nanotechnology. This technique, however, is restricted to metal or semiconducting samples, as it requires a tiny current to stabilize the tip-sample distance with atomic scale precision. We developed a new imaging and feedback method that relies on true alternating current (AC) without any direct current (DC) component. This technique does not only enable the imaging on non-conducting surfaces with atomic step resolution, like (thin) glass and oxides, it provides also access to high-frequency electronic signal coming from the sample. We demonstrate that it is possible to measure on 25nm thick silicon oxide with 10 MHz tunneling current.

physics.ins-det↗
arXiv2026

Charged-particle topology reconstruction with an in-liquid SiPM array

Liquid scintillator detectors instrumented with photosensors inside the scintillation volume preserve local optical information that is largely lost in conventional boundary-readout geometries. We demonstrate that this information is sufficient for charged-particle topology reconstruction using a sparse three-dimensional lattice of silicon photomultipliers. After validating the Geant4 detector response against measured photon-count distributions, a simulation-trained, time-informed convolutional neural network reconstructs the entry and exit points of through-going muons with median residuals of 1.91~cm and 2.39~cm, respectively. The reconstructed endpoints are geometrically consistent with acceptance regions defined by external trigger counters in cosmic-ray muon data. The same framework also reconstructs the production vertices of simulated positron starting-track events with a median residual of about 4.5~cm. These results establish the feasibility of topology-sensitive reconstruction using sparse in-liquid photosensor arrays in homogeneous liquid scintillator detectors.

physics.ins-det↗