Time Tagger Software Release v 2.20 - Major Updates to Time Tagger Lab, Network Workflows, and Measurement Tools

| on 12 December 2025

Time Tagger Software v2.20 is now available. This release focuses on expanded capabilities for the Time Tagger Lab GUI, improved workflows for networking applications, new processors for creating software-defined virtual channels, and additional measurements.

What’s New?

1. Network Client and Network Merger: Remote Access to Time Tagger Lab

Time Tagger Lab in v2.20 now introduces full Network Client support, including for merging streams from multiple servers. Although previously available in the Time Tagger API, users can now connect to one or multiple Time Tagger servers using the graphical user interface, and operate their devices from anywhere on the network.

Time tags are streamed over USB to the server PC, then streamed from the server to the client over the network. This functionality is particularly suitable for experiments that require physical separation from their experimental set-up, who want to share a single Time Tagger between multiple users simultaneously, or who want to simultaneously merge the measurements from different time taggers in geographically remote locations.

Relevant applications are in optics, quantum communication, and educational settings. Further use cases include real-time merging of remote data sources such as for data server synchronization, quantum key distribution or telescope arrays.

Two Client PCs are remotely connected to a Server PC, which is physically connected to the Time Tagger Device. Each PC displays a different instance of the Time Tagger Lab, demonstrating the independence of each Client. — Graphic demonstrating that multiple Client PCs can be connected to a Server PC, allowing for remote analysis using Time Tagger Lab.

2. New Processors in Time Tagger Lab: Combiner & Combinations Virtual Channels

2.1. Combiner Virtual Channel

With the Combiner Virtual Channel, users can merge two or more physical or virtual channels into a single channel, allowing them to process the data as if it were acquired on a single input channel. This virtual channel is implementable on all active channels and allows for more powerful data analysis than previously possible. For instance, users can monitor the total count rate on a subset of channels by merging them into a Combiner Virtual Channel and running a count rate measurement.

Screenshot detail of the Time Tagger Lab GUI, highlighting the properties view of a Combiner Processor. Four channels are selected as inputs. The processor creates a single Virtual Channel that will be used for flexible analysis of Time Tag measurements. — The configuration of a Combiner processor in Time Tagger Lab, where channels 1 through 4 are combined into a single Virtual Channel.

2.2. Combinations Virtual Channel

New to Time Tagger Lab, the Combinations Virtual Channel allows users to evaluate exclusive coincidences across up to 20 input channels. These virtual channels contain instances of coincident clicks on a specific subset of channels, with no coincident clicks on others, as defined by the user. The analysis of exclusive events is particularly useful for those working in quantum information and Photon Number Resolution (PNR).

Screenshot detail of the Time Tagger Lab GUI, highlighting the properties view of a Combinations Processor. Four channels are selected as inputs. The processor creates three Virtual Channels that will be used for flexible analysis of Time Tag measurements. — The configuration of a Combinations processor in Time Tagger Lab, using 4 input channels and 3 configured output channels. The first output channel contains coincident events in channels 1 and 2, which did not coincide with any events in either channels 3 and 4.

3. Phase Noise Analysis with Time Tagger Lab

While previously available in the backend, users can now run Phase Noise Analysis in the Time Tagger Lab. This software-defined measurement enables the estimation of the noise spectral density for periodic signals such as clocks and oscillators.

The Time Tagger timestamps zero crossings with picosecond precision, then locks the time tag stream to an external standard via a software PLL. Directly from the time tags, the software computes the single-sideband (SSB) L(f). The algorithm relies on Welch’s method to estimate the Power Spectral Density (PSD), and provides a phase noise estimator with spectral samples distributed quasi-logarithmically over frequency offset.

Optimised for applications that require low-noise oscillators, frequency metrology, and precision timing systems, this measurement specifically targets the timing and frequency space.

4. New Measurement Tool: `CorrelationPairs`, `HistogramCustomBins`

4.1. Correlation Pairs

Designed for users’ ease, the CorrelationPairs Measurement creates a bidirectional histogram for all two-fold combinations or pairs of a list of selected channels with just one line of code, quickly and easily allowing for coincidence analysis across many channels.

4.2. HistogramCustomBins

The HistogramCustomBins measurement allows users to define custom bin edges, providing a fine adjustment of bin edges when every picosecond matters. This measurement is particularly advantageous for those using periodic pulsed lasers in Fluorescence Correlation Spectroscopy (FCS), since it provides laser pulse artifact-free correlation on all timescales.

Comparison of a data set for Fluorescence Correlation Spectroscopy (FCS) with a pulsed laser analyzed with log bins, shown in red, and custom bins, shown in blue. The log bins show data that is noisy from t=0.1-10 us, whereas the custom bins show a clean curve from t=0.1 us and above. — Comparison of FCS autocorrelation measurements, by using logarithmic binning (HistogramLogBins) and by using laser-pulse-adjusted custom binning (HistogramCustomBins) on a system driven by a pulsed laser. Differences are visible for short lag-times and fine binning. Fine adjustment of the custom bin edges is described in the FCS Tutorial.

5. Fluorescence Correlation Spectroscopy (FCS) Tutorial

This tutorial explains how to perform Fluorescence Correlation Spectroscopy (FCS) and advanced variants, such as Fluorescence Cross-Correlation Spectroscopy (FCCS), Pulsed Interleaved Excitation (PIE), and Raster Image Correlation Spectroscopy (RICS), using the Time Tagger hardware and software for data acquisition and live analysis.

Schematic diagram of a typical Fluorescence Correlation Spectroscopy (FCS) and Fluorescence Cross Correlation Spectroscopy (FCCS) experimental setup. A sample is illuminated by a pulsed laser. The lasers are pulsed using a Swaabian Instruments Pulse Streamer. The incident photons from the sample are concentrated on to the respective single photon detectors. The arrival times are timestamped by a time-to-digital converter, in this case, a Swabian Instruments Time Tagger. A correlation g2 curve is derived and displayed on a PC. — Schematic diagram of a typical FCS experimental setup, as well as additional components for the advanced versions (FCCS, PIE & RICS). In the simplest scenario, a sample is illuminated by a laser and the photons of the fluorophore are detected by a single photon detector. Photon arrival times are timestamped by a Time Tagger. The software then calculates the g2 correlation directly on the fly, by using HistogramLogBins or HistogramCustomBins.

6. Python Library Access

The Time Tagger Python libraries will now be conveniently available under the package name Swabian-TimeTagger through PyPI on Linux. Our software tools have been integrated into the package manager, which offers an alternative way to install the Python libraries than through the official installer.

Download V2.20 here

Any questions or feedback?
We would love to hear from you! Please send us a message at solutions@swabianinstruments.com.