LDMX HCal read-out chip board characterization and diagnostics
(2026) FYSM64 20261Department of Physics
Particle and nuclear physics
- Abstract
- The search for the dark matter (DM) particle has recently found itself at the forefront of physics research. Numerous experiments designed to capture the invisible particle have contributed to constraining its possible energy range. The Light Dark Matter eXperiment (LDMX) aims to delve into the yet unexplored sub-GeV region, searching for “light” DM (LDM). The experiment is designed to infer LDM production from the missing-momentum of electrons, scattered via the “dark bremsstrahlung” process. To identify this event, LDMX employs a wide array of trackers and detectors. Due to the dark bremsstrahlung’s low likelihood, LDMX must diligently exclude all other energy signatures. The hadronic calorimeter (HCal) plays an important role in... (More)
- The search for the dark matter (DM) particle has recently found itself at the forefront of physics research. Numerous experiments designed to capture the invisible particle have contributed to constraining its possible energy range. The Light Dark Matter eXperiment (LDMX) aims to delve into the yet unexplored sub-GeV region, searching for “light” DM (LDM). The experiment is designed to infer LDM production from the missing-momentum of electrons, scattered via the “dark bremsstrahlung” process. To identify this event, LDMX employs a wide array of trackers and detectors. Due to the dark bremsstrahlung’s low likelihood, LDMX must diligently exclude all other energy signatures. The hadronic calorimeter (HCal) plays an important role in ensuring the experiment’s high veto power. In this study, we characterize and evaluate the reliability of the HCal’s front-end read-out - the High Granularity Read Out Chip (HGCROC) board. We examine the read-out of ten H2GCROCv3b boards using internal charge-injections. The injections follow a novel automated testing procedure, configured via the pflib control software. We review the results over four evaluation metrics to assess the health of the chip board and the control software. Using these metrics, we identify and classify various read-out irregularities. Furthermore, we develop a read-out irregularity identification algorithm, as part of the pflib library. (Less)
- Popular Abstract
- There is a fundamental issue with our current understanding of the Universe. Looking through a telescope, we may observe curious behaviour of some stars and galaxies - movements that could only be explained by some invisible gravitational source. Stranger still, after summing up all the masses of the visible objects in the Universe, we find a notable discrepancy between predication and measurement. This missing mass has become known as ‘dark matter’, and, over the years, as one physics’ ‘big questions’. Despite various answers having been put forward, none have garnered as much attention as the particle hypothesis.
For dark matter particles to exist, they must have two properties. First, they must have mass to interact through the... (More) - There is a fundamental issue with our current understanding of the Universe. Looking through a telescope, we may observe curious behaviour of some stars and galaxies - movements that could only be explained by some invisible gravitational source. Stranger still, after summing up all the masses of the visible objects in the Universe, we find a notable discrepancy between predication and measurement. This missing mass has become known as ‘dark matter’, and, over the years, as one physics’ ‘big questions’. Despite various answers having been put forward, none have garnered as much attention as the particle hypothesis.
For dark matter particles to exist, they must have two properties. First, they must have mass to interact through the gravitational force. Second, they must be functionally invisible, that is, not interact with photons. Though the idea of a massive invisible particle might sound strange, the Standard Model already contains one that fits the bill - neutrinos. That said, because of their high energies during the early stages of the Universe, neutrinos are not a viable solution. However, we can use techniques similar to those used in neutrino searches, in pursuit of dark matter particles. As such, numerous experiments were designed to do just that. Although they have not quite managed to capture the particle, they have considerably constrained its possible mass range. After years of searching, the Light Dark Matter Experiment (LDMX) sets its sights on particles that may weigh as much as an electron.
The light dark matter hypothesis proposes a new force which would allow Standard Model particles to interact with dark matter. This force would be carried by a ‘dark photon’, much like a normal photon ‘carries’ the electromagnetic force. These carriers could be emitted by, for example, an electron. With this in mind, LDMX monitors the energy and momenta of accelerated electrons, deflected off of a thin tungsten target. Upon scattering, the electrons lose some of their energy, usually emitting a photon. Both the electrons and photons would then be identified by a detector - specifically, an electromagnetic calorimeter (ECal). However, in rare cases, an electron will emit a dark photon, which would turn into dark matter particles. As these would not show up on the ECal read-out, the electron will have lost its energy to seemingly nothing. This will allow LDMX to infer the existence of dark matter particles. As with any experiment, however, the LDMX detectors will be subjected to various sources of background noise and interference. As such, due to its innate sensitivity, LDMX must exclude all other possible particle signatures. Among the ECal and other detectors, LDMX employs a hadronic calorimeter (HCal). Its primary goal is identifying any heavy neutral particles, which may be produced throughout the experiment.
In this study, we evaluate the reliability of ten high granularity calorimeter read-out chip (HGCROC) boards in preparation for the experiment. The chip boards are mainly responsible for reading and processing the HCal signal. Using an automated testing procedure, we measure the response of the boards to a suite of electrical tests. We found that most of the boards performed adequately, though some may contain malfunctioning components. Furthermore, we suggest improvements to the boards’ control software, which we found responsible for some of the read-out anomalies. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/student-papers/record/9238077
- author
- Rozmarynowicz, Blazej Andrzej LU
- supervisor
-
- Hannah Herde LU
- Erik Wallin LU
- organization
- course
- FYSM64 20261
- year
- 2026
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- LDMX, HGCROC, HCal, dark matter, characterization, diagnostics, read-out chip
- language
- English
- id
- 9238077
- date added to LUP
- 2026-06-16 08:34:03
- date last changed
- 2026-06-16 08:34:03
@misc{9238077,
abstract = {{The search for the dark matter (DM) particle has recently found itself at the forefront of physics research. Numerous experiments designed to capture the invisible particle have contributed to constraining its possible energy range. The Light Dark Matter eXperiment (LDMX) aims to delve into the yet unexplored sub-GeV region, searching for “light” DM (LDM). The experiment is designed to infer LDM production from the missing-momentum of electrons, scattered via the “dark bremsstrahlung” process. To identify this event, LDMX employs a wide array of trackers and detectors. Due to the dark bremsstrahlung’s low likelihood, LDMX must diligently exclude all other energy signatures. The hadronic calorimeter (HCal) plays an important role in ensuring the experiment’s high veto power. In this study, we characterize and evaluate the reliability of the HCal’s front-end read-out - the High Granularity Read Out Chip (HGCROC) board. We examine the read-out of ten H2GCROCv3b boards using internal charge-injections. The injections follow a novel automated testing procedure, configured via the pflib control software. We review the results over four evaluation metrics to assess the health of the chip board and the control software. Using these metrics, we identify and classify various read-out irregularities. Furthermore, we develop a read-out irregularity identification algorithm, as part of the pflib library.}},
author = {{Rozmarynowicz, Blazej Andrzej}},
language = {{eng}},
note = {{Student Paper}},
title = {{LDMX HCal read-out chip board characterization and diagnostics}},
year = {{2026}},
}