Energy Reconstruction with Autoencoders for Dual-Phase Time Projection Chambers Ivy Li, Aarón Higuera, Shixiao Liang, Christopher Tunnell | CHEP 2023 Energy Reconstruction for XENONnT References Weight Layer ELU(𝛼=1) x Weight Layer F(x) F(x) + x x identity Residual Block (ResBlock) ELU(𝛼=1) Input Hit Pattern (494,) Reconstructed Hit Pattern (494,) Latent Space Representation (50,) 5 ResBlocks (494,494) 1 LinBlock (494,50) 1 LinBlock (50,494) 1 LinBlock (494,494) ENCODER DECODER 5 ResBlocks (494,494) Inferring Number of Electrons in the Gas Gap Example Reconstruction of Top Array Hit Pattern , , Input Hit Pattern (494,) Reconstructed Hit Pattern (494,) Inferred ne True ne Loss = (1 - 𝛽) MSE + 𝛽 MSE Loss Function with Cyclic Annealing Inferred ne True ne ENCODER: encodes input into a lower dimensional latent space↑ LATENT SPACE REPRESENTATION: constrains one value as the number of electrons in the gas gap but allows the others to evolve freely ↗ DECODER: decodes input from latent space representation ↑ LOSS FUNCTION: weighted sum of the mean squared error (MSE) losses below ↓ CYCLIC ANNEALING: 𝛽 evolves from 𝛽=0 (prioritize reconstruction) to 𝛽=1 (prioritize latent space constraint) Weight Layer Linear Block (LinBlock) x ELECTRIC FIELD DRIFTS ELECTRONS TO GAS GAP: not all of the freed electrons from the interaction point reach the gas gap SCINTILLATION SIGNAL (S1): incoming particle interacts with xenon and causes a scintillation signal with energy which is proportional to the initial signal’s number of photons IONIZATION SIGNAL (S2): electrons which reach the gas gap cause a brighter ionization signal with energy which is proportional to the number of electrons at the interaction point XENONnT has a detector target of 6 tonnes of ultra-pure liquid xenon [1] ↗ HIT PATTERNS: Photosensors above and below measure signals → Future Work TOTAL ENERGY 6163194 trainable parameters Adam optimizer, starting learning rate 5e-4 Reduce learning rate by factor of 0.1 if loss does not decrease after 5 epochs Semi-Supervised Autoencoder DATASET: simulated hit patterns from a given [0, 2000] ne INPUT SIZE: 494 photosensors total TRAIN/VALIDATION/TEST SPLIT: 447500/447500/100000 hit patterns NUMBER OF ELECTRONS IN GAS GAP Inference Difference Normalized Difference DYNAMIC RANGE: autoencoder can reconstruct hit patterns well without normalizing or log-scaling hit patterns due to skip connections [3] ENERGY RESOLUTION: precise reconstruction is critical for rare event searches such as the search for dark matter evidence [2] INTERPRETABILITY: constraining the latent space to be physically meaningful e.g. a variational autoencoder constrains latent parameters as probability distributions → meaningful parameter uncertainties COMPUTATIONAL EFFICIENCY: simulating data through Monte Carlo methods is computationally expensive and time-consuming → ongoing efforts to create more efﬁcient machine-learning algorithms for fast simulation [0] Check out our experiment! xenonexperiment.org [1] Aprile, E., et al. "Search for new physics in electronic recoil data from XENONnT." Physical Review Letters 129.16 (2022): 161805. [2] Billard, Julien, et al. "Direct detection of dark matter—APPEC committee report." Reports on Progress in Physics 85.5 (2022): 056201. [3] Dieng, Adji B., et al. "Avoiding latent variable collapse with generative skip models." The 22nd International Conference on Artiﬁcial Intelligence and Statistics. PMLR, (2019). GOAL TO IMPROVE RESOLUTION: the spread of the inferred number of electrons in the gas gap decreases as the inverse square root for increasing number of electrons ← UNDERPREDICTION: WHY? currently the autoencoder underpredicts the number of electrons in the gas gap ↑ Ground Truth Gas Gap Reconstructed True ne Inferred ne True ne (X) Inferred ne (Y) Counts