相关论文: Data-driven detection of multi-messenger transient…
Transient sources such as supernovae (SNe) and tidal disruption events are candidates of high energy neutrino sources. However, SNe commonly occur in the universe and a chance coincidence of their detection with a neutrino signal cannot be…
Visual change detection, aiming at segmentation of video frames into foreground and background regions, is one of the elementary tasks in computer vision and video analytics. The applications of change detection include anomaly detection,…
The application of deep learning toward discovery of data-driven models requires careful application of inductive biases to obtain a description of physics which is both accurate and robust. We present here a framework for discovering…
We present the S-PLUS Transient Extension Program (STEP): a supernova and fast transient survey conducted in the southern hemisphere using data from the Southern Photometric Local Universe Survey (S-PLUS) Main Survey and the T80-South…
Fermi Gamma-ray Space Telescope has detected a diverse range of gamma-ray transients since its launch in 2008. Over the years, Fermi has accumulated an extensive public archive of transient events. Traditional classification methods for…
With the wide adoption of mobile devices, today's location tracking systems such as satellites, cellular base stations and wireless access points are continuously producing tremendous amounts of location data of moving objects. The ability…
We present a novel strategy to uncover indirect signs of new physics in collider data using the Standard Model Effective Field Theory (SMEFT) framework, offering notably improved sensitivity compared to traditional global analyses. Our…
A change points detection aims to catch an abrupt disorder in data distribution. Common approaches assume that there are only two fixed distributions for data: one before and another after a change point. Real-world data are richer than…
Most domains of science are experiencing a paradigm shift due to the advent of a new generation of instruments and detectors which produce data and data streams at an unprecedented rate. The scientific exploitation of these data, namely…
We study the constraints on neutrino masses that could be derived from the observation of a Galactic supernova neutrino signal with present and future neutrino detectors. Our analysis is based on a recently proposed method that uses the…
Sensitive dark matter (DM) experiments can be well exploited beyond their designated targets, allowing to explore a breadth of physics topics. As we discuss, future large direct DM detection experiments constitute impressive telescopes,…
Unsupervised ensemble learning emerged to address the challenge of combining multiple learners' predictions without access to ground truth labels or additional data. This paradigm is crucial in scenarios where evaluating individual…
High-energy neutrinos originating in astrophysical sources should be accompanied by gamma-rays at production. Depending on the properties of the emission environment and the distance of the source to the Earth, these gamma-rays may be…
The practical implementation of maximum likelihood detection is limited by its high complexity as well as requiring perfect channel state information. Although conventional blind detection techniques reduce complexity, they degrade…
The recent association between IC-170922A and the blazar TXS0506+056 highlights the importance of real-time observations for identifying possible astrophysical neutrino sources. Thanks to its near-100\% duty cycle, 4$\pi$ steradian field of…
The detection of high-energy astrophysical neutrinos by IceCube has opened new windows for neutrino astronomy, but their sources remains largely unresolved. We study a methodology to address this - deep-stacking - that exploits correlations…
Temporal sampling does more than add another axis to the vector of observables. Instead, under the recognition that how objects change (and move) in time speaks directly to the physics underlying astronomical phenomena, next-generation…
Following the discovery of the brightest high-energy neutrino sources in the sky, the further detection of fainter sources is more challenging. A natural solution is to combine fainter source candidates, and instead of individual…
The scientific interest in studying high-energy transient phenomena in the Universe has largely grown for the last decade. Now, multiple ground-based survey projects have emerged to continuously monitor the optical (and multi-messenger)…
Physics-driven discovery in an autonomous experiment has emerged as a dream application of machine learning in physical sciences. Here we develop and experimentally implement a deep kernel learning workflow combining the correlative…