Sensor operating point calibration and monitoring of the ALICE Inner Tracking System during LHC Run 3

TL;DR

The paper documents the design and operational calibration of the ALICE ITS2, a large-scale MAPS-based vertex detector with $12.6 \times 10^{9}$ pixels across seven concentric layers and a $0.36\%$ $X_{0}$ per inner layer, deployed for LHC Run 3. It presents a comprehensive calibration framework and a suite of scans (threshold, VCASN, ITHR, VRESETD, pulse-shape, noise) executed on a distributed computing farm to tune in-pixel thresholds, mask noisy pixels, monitor AVDD-related drifts, and characterize the analogue pixel response. The results demonstrate precise, chip-wide threshold tuning to about $100\ e^-$, a chip-threshold dispersion of $\sim 3.8\ e^-$, a low bad-pixel fraction (~$0.1\%$), and a stable fake-hit rate well below the design limit of $10^{-6}$ hits/event/pixel, with only periodic retunings needed as radiation accumulates. The calibration framework enables timely re-calibration between LHC fills and ongoing monitoring of detector health, ensuring robust data-taking for Run 3 and providing a model for large-scale MAPS-based detectors in high-energy physics.

Abstract

The new Inner Tracking System (ITS2) of the ALICE experiment began operation in 2021 with the start of LHC Run 3. Compared to its predecessor, ITS2 offers substantial improvements in pointing resolution, tracking efficiency at low transverse momenta, and readout-rate capabilities. The detector employs silicon Monolithic Active Pixel Sensors (MAPS) featuring a pixel size of 26.88$\times$29.24 $μ$m$^2$ and an intrinsic spatial resolution of approximately 5 $μ$m. With a remarkably low material budget of 0.36% of radiation length ($X_{0}$) per layer in the three innermost layers and a total sensitive area of about 10 m$^2$, the ITS2 constitutes the largest-scale application of MAPS technology in a high-energy physics experiment and the first of its kind operated at the LHC. For stable data taking, it is crucial to calibrate different parameters of the detector, such as in-pixel charge thresholds and the masking of noisy pixels. The calibration of 24120 monolithic sensors, comprising a total of 12.6$\times$10$^{9}$ pixels, represents a major operational challenge. This paper presents the methods developed for the calibration of the ITS2 and outlines the strategies for monitoring and dynamically adjusting the detector's key performance parameters over time.

Figures (20)

• Figure 1: Scheme of the ALPIDE in-pixel analogue front-end.
• Figure 2: Simplified schematic view of the ALPIDE pixel cell.
• Figure 3: Number of hits normalized to the number of injections per charge point as a function of the injected charge (in electrons) for a pixel of ITS2 in a threshold scan. The errors are evaluated as Binomial errors based on the Clopper--Pearson Clopper:1934sws method with a confidence level of 68.27%, as provided by the ROOT TEfficiency class tefficiency. The red line represents a fit to the data performed with an error function. See text for more details.
• Figure 4: Sketch of the ITS2 hardware and software chain needed to run and analyse calibration scans.
• Figure 5: Pixel threshold distributions for every chip of ITS2 from two different full threshold scans, both recorded in June 2025. The top figure shows the untuned case where default settings for VCASN and ITHR (50 DACs) have been adopted. The bottom panel refers to the case where a threshold tuning to 100 $e^-$ has been performed. The $x$ axis of both plots is split into two parts: IB chips on the left, and OB ones on the right. The $y$ axis maximum is set to 450 $e^-$ since above this limit the threshold cannot be reliably extracted, given that the maximum injected change is 500 $e^-$. See text for more details on outlier chips.