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In the data obtained by laser interferometric gravitational wave detectors, transient noise with non-stationary and non-Gaussian features occurs at a high rate. This often results in problems such as detector instability and the hiding and/or imitation of gravitational-wave signals. This transient noise has various characteristics in the time–frequency representation, which is considered to be associated with environmental and instrumental origins. Classification of transient noise can offer clues for exploring its origin and improving the performance of the detector. One approach for accomplishing this is supervised learning. However, in general, supervised learning requires annotation of the training data, and there are issues with ensuring objectivity in the classification and its corresponding new classes. By contrast, unsupervised learning can reduce the annotation work for the training data and ensure objectivity in the classification and its corresponding new classes. In this study, we propose an unsupervised learning architecture for the classification of transient noise that combines a variational autoencoder and invariant information clustering. To evaluate the effectiveness of the proposed architecture, we used the dataset (time–frequency two-dimensional spectrogram images and labels) of the Laser Interferometer Gravitational-wave Observatory (LIGO) first observation run prepared by the Gravity Spy project. The classes provided by our proposed unsupervised learning architecture were consistent with the labels annotated by the Gravity Spy project, which manifests the potential for the existence of unrevealed classes.

Transient noise appearing in the data from gravitational-wave detectors frequently causes problems, such as instability of the detectors and overlapping or mimicking gravitational-wave signals. Because transient noise is considered to be associated with the environment and instrument, its classification would help to understand its origin and improve the detector's performance. In a previous study, an architecture for classifying transient noise using a time-frequency 2D image (spectrogram) is proposed, which uses unsupervised deep learning combined with variational autoencoder and invariant information clustering. The proposed unsupervised-learning architecture is applied to the Gravity Spy dataset, which consists of Advanced Laser Interferometer Gravitational-Wave Observatory (Advanced LIGO) transient noises with their associated metadata to discuss the potential for online or offline data analysis. In this study, focused on the Gravity Spy dataset, the training process of unsupervised-learning architecture of the previous study is examined and reported.

In the data obtained by laser interferometric gravitational wave detectors, transient noise with non-stationary and non-Gaussian features occurs at a high rate. This often results in problems such as detector instability and the hiding and/or imitation of gravitational-wave signals. This transient noise has various characteristics in the time--frequency representation, which is considered to be associated with environmental and instrumental origins. Classification of transient noise can offer clues for exploring its origin and improving the performance of the detector. One approach for accomplishing this is supervised learning. However, in general, supervised learning requires annotation of the training data, and there are issues with ensuring objectivity in the classification and its corresponding new classes. By contrast, unsupervised learning can reduce the annotation work for the training data and ensure objectivity in the classification and its corresponding new classes. In this study, we propose an unsupervised learning architecture for the classification of transient noise that combines a variational autoencoder and invariant information clustering. To evaluate the effectiveness of the proposed architecture, we used the dataset (time--frequency two-dimensional spectrogram images and labels) of the Laser Interferometer Gravitational-wave Observatory (LIGO) first observation run prepared by the Gravity Spy project. The classes provided by our proposed unsupervised learning architecture were consistent with the labels annotated by the Gravity Spy project, which manifests the potential for the existence of unrevealed classes.

The physics program of future lepton colliders such as the ILC, will benefit from a jet energy resolution in the range 3-4%. The International Large Detector (ILD) reaches this goal over a large range of jet energies. In this paper, we report on the dependence of the simulated ILD performance on various parameters of its silicon-tungsten ECAL. We investigate the effects of dead areas in the silicon sensors, the thickness of the PCB at the heart of the detector, and the robustness of its performance with respect to dead channels, noise, mis-calibration and cross-talk.

The International Large Detector (ILD) is a proposed detector for the International Linear Collider (ILC). It has been designed to achieve an excellent jet energy resolution by using Particle Flow Algorithms (PFA), which rely on the ability to separate nearby particles within jets. PFA requires calorimeters with high granularity. The ILD Electromagnetic Calorimeter (ECAL) is a sampling calorimeter with thirty tungsten absorber layers. The total thickness of this ECAL is about 24 X\\(_0\\), and it has between 10 and 100 million channels to make high granularity. Silicon sensors are a candidate technology for the sensitive layers of this ECAL. Present prototypes of these sensors have 256 5.5\\(\\times\\)5.5 mm\\(^2\\) pixels in an area of 9\\(\\times\\)9 cm\\(^2\\).We have measured various properties of these prototype sensors: the leakage current, capacitance, and full depletion voltage. We have also examined the response to an infrared laser to understand the sensor's response at its edge and between pixel readout pads, as well the effect of different guard ring designs. In this paper, we show results from these measurements and discuss future works.