A5082528850- Gamma-ray bursts and supernovae
- Astronomy and Astrophysical Research
- Anomaly Detection Techniques and Applications
- Astrophysics and Cosmic Phenomena
- Astronomical Observations and Instrumentation
- Stellar, planetary, and galactic studies
- Time Series Analysis and Forecasting
- Solar and Space Plasma Dynamics
- Pulsars and Gravitational Waves Research
- Scientific Computing and Data Management
- Computational Physics and Python Applications
- Radiation Detection and Scintillator Technologies
- Molecular spectroscopy and chirality
- Particle Accelerators and Free-Electron Lasers
- Advanced Radiotherapy Techniques
- Artificial Immune Systems Applications
- Fault Detection and Control Systems
- Galaxies: Formation, Evolution, Phenomena
- Data-Driven Disease Surveillance
- Radiation Therapy and Dosimetry
- Mental Health Research Topics
- Statistical Methods and Inference
- Evolutionary Algorithms and Applications
- Metabolomics and Mass Spectrometry Studies
- Influenza Virus Research Studies
Université Clermont Auvergne
2022-2025
Institut National de Physique Nucléaire et de Physique des Particules
2024-2025
Clermont Université
2022-2025
Centre National de la Recherche Scientifique
2022-2024
Laboratoire de Physique Corpusculaire
2022
We present Rainbow, a physically motivated framework which enables simultaneous multi-band light curve fitting. It allows the user to construct 2-dimensional continuous surface across wavelength and time, even in situations where number of observations each filter is significantly limited. Assuming electromagnetic radiation emission from transient can be approximated by black-body, we combined an expected temperature evolution parametric function describing its bolometric curve. These three...
Abstract We describe the SNAD Viewer , a web portal for astronomers which presents centralized view of individual objects from Zwicky Transient Facility’s (ZTF) data releases, including gathered multiple publicly available astronomical archives and sources. Initially built to enable efficient expert feedback in context adaptive machine learning applications, it has evolved into full-fledged community asset that centralizes public information provides multi-dimensional ZTF For users, we...
ABSTRACT Most of the stars in Universe are M spectral class dwarfs, which known to be source bright and frequent stellar flares. In this paper, we propose new approaches discover M-dwarf flares ground-based photometric surveys. We employ two approaches: a modification traditional method parametric fit search machine learning algorithm based on active anomaly detection. The algorithms applied Zwicky Transient Facility (ZTF) data release 8, includes from ZTF high-cadence survey, allowing us...
Context. We provide the first results from complete SNAD adaptive learning pipeline in context of a broad scope data large-scale astronomical surveys. Aims. The main goal this work is to explore potential techniques application big sets. Methods. Our team used Active Anomaly Discovery (AAD) as tool search for new supernova (SN) candidates photometric 9.4 months Zwicky Transient Facility (ZTF) survey, namely, between March 17 and December 31, 2018 (58 194 ≤ MJD 58 483). analysed 70 ZTF fields...
Symbolic regression (SR) searches for analytical expressions representing the relationship between a set of explanatory and response variables. Current SR methods assume single dataset extracted from experiment. Nevertheless, frequently, researcher is confronted with multiple sets results obtained experiments conducted different setups. Traditional may fail to find underlying expression since parameters each experiment can be different. In this work we present Multi-View Regression (MvSR),...
In this paper, we explore the possibility of detecting M-dwarf flares using data from Zwicky Transient Facility releases (ZTF DRs). We employ two different approaches: traditional method parametric fit search and a machine learning algorithm originally developed for anomaly detection. analyzed over 35 million ZTF light curves visually scrutinized 1168 candidates suggested by algorithms to filter out artifacts, occultations star an asteroid, known variable objects other types. Our final...
For the evaluation of biological effects, Monte Carlo toolkits were used to provide an RBE-weighted dose using databases survival fraction coefficients predicted through biophysical models. Biophysics models, such as mMKM and NanOx have previously been developed estimate a dose. Using model, we calculated saturation corrected mean specific energy z1D* (Gy) at 10% D10 for human salivary gland (HSG) cells Track Structure codes LPCHEM Geant4-DNA, compared these with data from literature...
Abstract The SNAD team has developed an adaptive learning algorithm, named Pine Forest (PF), to enhance anomaly detection in astronomical data. Recognizing the essential role of human engagement discovery process, PF presents outliers a expert for review, and filters out trees which disagree with feedback provided. During sixth annual workshop ( https://snad.space/2023/ ), held 2023 July, we applied Zwicky Transient Facility’s DR17 Interesting discoveries include long-duration objects such...
In the task of anomaly detection in modern time-domain photometric surveys, primary goal is to identify astrophysically interesting, rare, and unusual objects among a large volume data. Unfortunately, artifacts -- such as plane or satellite tracks, bad columns on CCDs, ghosts often constitute significant contaminants results from analysis. contexts, Active Anomaly Discovery (AAD) algorithm allows tailoring output pipelines according what expert judges be scientifically interesting. We...
We present coniferest, an open source generic purpose active anomaly detection framework written in Python. The package design and implemented algorithms are described. Currently, static outlier analysis is supported via the Isolation forest algorithm. Moreover, Active Anomaly Discovery (AAD) Pineforest available to tackle problems. performance evaluated on a series of synthetic datasets. also describe few success cases which resulted from applying real astronomical data tasks within SNAD project.
Abstract The SNAD team reports the discovery of SNAD160 (AT2018lzi) within Zwicky Transient Facility third data release. transient has been found using active anomaly detection algorithm, an adaptive learning strategy aimed at incorporating expert knowledge into machine models. Our preliminary analysis shows that could be a superluminous supernova powered by pair-instability mechanism—its light curve behavior is consistent with observed slow rise and decay expected from these events.
We provide the first results from complete SNAD adaptive learning pipeline in context of a broad scope data large-scale astronomical surveys. The main goal this work is to explore potential techniques application big sets. Our team used Active Anomaly Discovery (AAD) as tool search for new supernova (SN) candidates photometric 9.4 months Zwicky Transient Facility (ZTF) survey, namely, between March 17 and December 31 2018 (58194 < MJD 58483). analysed 70 ZTF fields at high galactic...
We describe the SNAD Viewer, a web portal for astronomers which presents centralized view of individual objects from Zwicky Transient Facility's (ZTF) data releases, including gathered multiple publicly available astronomical archives and sources. Initially built to enable efficient expert feedback in context adaptive machine learning applications, it has evolved into full-fledged community asset that centralizes public information provides multi-dimensional ZTF For users, we provide...
We present the Active Galactic Nuclei (AGN) classifier as currently implemented within Fink broker. Features were built upon summary statistics of available photometric points, well color estimation enabled by symbolic regression. The learning stage includes an active loop, used to build optimized training sample from labels reported in astronomical catalogs. Using this method classify real alerts Zwicky Transient Facility (ZTF), we achieved 98.0% accuracy, 93.8% precision and 88.5% recall....