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The Light Dark Matter eXperiment

Tamas Almos Vami

Abstract

Searching for dark matter (DM) at colliders is one of the biggest challenges in high-energy physics today. Significant efforts have been made to detect DM within the mass range of 1-10,000 GeV at the Large Hadron Collider and other experiments. However, the lower mass range of 0.001-1 GeV remains largely unexplored, despite strong theoretical motivation from thermal dark matter models in that mass range. The Light Dark Matter eXperiment (LDMX) is a proposed fixed-target experiment at SLAC's LCLS-II 8 GeV electron beamline, specifically designed for the direct production of sub-GeV dark matter. The experiment operates on the principle of detecting missing momentum and missing energy signatures. In this talk, we will present the experimental design of LDMX detector and discuss strategies for detecting dark matter. The talk will detail traditional discriminants-based methods using the electromagnetic and hadronic calorimeters as a veto for Standard Model processes. Additionally, the application of advanced machine learning techniques, such as boosted decision trees and graph neural networks, for distinguishing signal from background will be discussed.

The Light Dark Matter eXperiment

Abstract

Searching for dark matter (DM) at colliders is one of the biggest challenges in high-energy physics today. Significant efforts have been made to detect DM within the mass range of 1-10,000 GeV at the Large Hadron Collider and other experiments. However, the lower mass range of 0.001-1 GeV remains largely unexplored, despite strong theoretical motivation from thermal dark matter models in that mass range. The Light Dark Matter eXperiment (LDMX) is a proposed fixed-target experiment at SLAC's LCLS-II 8 GeV electron beamline, specifically designed for the direct production of sub-GeV dark matter. The experiment operates on the principle of detecting missing momentum and missing energy signatures. In this talk, we will present the experimental design of LDMX detector and discuss strategies for detecting dark matter. The talk will detail traditional discriminants-based methods using the electromagnetic and hadronic calorimeters as a veto for Standard Model processes. Additionally, the application of advanced machine learning techniques, such as boosted decision trees and graph neural networks, for distinguishing signal from background will be discussed.

Paper Structure

This paper contains 8 sections, 6 figures, 3 tables.

Figures (6)

  • Figure 1: The LDMX detector showing the trigger scintillators, the tagging tracker, the target inside the spectrometer dipole, the recoil tracker, the ECal, and the side and back HCal.
  • Figure 2: Relative rates of different SM processes that are the backgrounds in the experiment .
  • Figure 3: The logarithmic transform of the BDT scores for signal and background MC events passing the trigger and fiducial selections. The signals with different masses are displayed with red, green, blue and yellow. Magenta and cyan show the ECal conversion, and photo-nuclear samples, respectively, while the dark blue and gray show the same processes occurring in the target. Dark green is for the target electro-nuclear processes.
  • Figure 4: The summed energy deposited in the ECal after trigger selection. The signals with different masses are displayed with red, green, blue and yellow. Magenta and cyan show the ECal conversion, and photo-nuclear samples, respectively, while the dark blue and gray show the same processes occurring in the target. Dark green is for the target electro-nuclear processes.
  • Figure 5: Expected limits of the missing-momentum search assuming a range of background events left.
  • ...and 1 more figures