Related papers: The Atacama Cosmology Telescope: Machine Learning …
We describe the calibration and data processing methods used to generate full-sky maps of the cosmic microwave background (CMB) from the first year of Wilkinson Microwave Anisotropy Probe (WMAP) observations. Detailed limits on residual…
We present the first data release of the ALMA-IMF Large Program, which covers the 12m-array continuum calibration and imaging. The ALMA-IMF Large Program is a survey of fifteen dense molecular cloud regions spanning a range of evolutionary…
We apply machine learning techniques in an attempt to predict and classify stellar properties from noisy and sparse time series data. We preprocessed over 94 GB of Kepler light curves from MAST to classify according to ten distinct physical…
We describe a method of reconstructing air showers induced by cosmic rays using deep learning techniques. We simulate an observatory consisting of ground-based particle detectors with fixed locations on a regular grid. The detector's…
In this work, a multi-stage Machine Learning (ML) pipeline is proposed for pipe leakage detection in an industrial environment. As opposed to other industrial and urban environments, the environment under study includes many interfering…
Modern time-domain surveys continuously monitor large swaths of the sky to look for astronomical variability. Astrophysical discovery in such data sets is complicated by the fact that detections of real transient and variable sources are…
We present the data reduction software and the distribution of Level 1 and Level 2 products of the Stratospheric Terahertz Observatory 2 (STO2). STO2, a balloon-borne Terahertz telescope, surveyed star-forming regions and the Galactic plane…
We describe an FPGA based Front-End Data Acquisition Module (FEDAM) for implementing Time-over-Threshold (ToT) Time-to-Digital conversion (TDC) of pulses obtained from the COSMICi astroparticle telescope detector system photomultiplier…
Analyzing air pollution data is challenging as there are various analysis focuses from different aspects: feature (what), space (where), and time (when). As in most geospatial analysis problems, besides high-dimensional features, the…
Neurophysiological time series, such as electromyographic signal and intracortical recordings, are typically composed of many individual spiking sources, the recovery of which can give fundamental insights into the biological system of…
Observations of the HI 21cm transition line promises to be an important probe into the cosmic dark ages and epoch of reionization. One of the challenges for the detection of this signal is the accuracy of the foreground source removal. This…
The COBE time ordered data (TOD) was reanalysed for anisotropies in the cosmic microwave background with a novel technique that is much simpler and more robust than used in the official analysis of the COBE TOD. The technique extends the…
We describe the first instrument of a Cosmic Microwave Background experiment for mapping cosmological structures on medium angular scales (the COSMOSOMAS experiment) and diffuse Galactic emission. The instrument is located at Teide…
Data mining techniques, including clustering and classification tasks, for the automatic information extraction from large datasets are increasingly demanded in several scientific fields. In particular, in the astrophysical field, large…
This paper presents a systematic literature review focusing on the application of machine learning techniques for deriving observational constraints in cosmology. The goal is to evaluate and synthesize existing research to identify…
Object detection applied to LiDAR point clouds is a relevant task in robotics, and particularly in autonomous driving. Single frame methods, predominant in the field, exploit information from individual sensor scans. Recent approaches…
Systematic reviews, which entail the extraction of data from large numbers of scientific documents, are an ideal avenue for the application of machine learning. They are vital to many fields of science and philanthropy, but are very…
Direction of arrival (DoA) estimation is a fundamental task in array processing. A popular family of DoA estimation algorithms are subspace methods, which operate by dividing the measurements into distinct signal and noise subspaces.…
Ambient noise tomography relies on the assumption that the seismic wavefield is equipartitioned. In practice, ambient noise sources are spatially and temporally heterogeneous, producing biased estimates of the Green's function between…
Background: Wearable spectrometers enable field quantification of biologically relevant light, yet reproducible pipelines for contextual classification remain under-specified. Objective: To establish and validate a subject-wise evaluated,…