Paris workshop on Bayesian Deep Learning for Cosmology and Time Domain Astrophysics

Session

Time Domain Astrophysics

21 juin 2022, 11:25

Buffon Amphitheater (APC laboratory, Université Paris Cité)

98. SNAD: anomaly detection for large scale time-domain astronomy (timedomain3)

Konstantin Malanchev (University of Illinois at Urbana-Champaign)

21/06/2022 11:25

Time Domain Astrophysics

Talk

97. A Convolutional Neural Network Approach to Classifying Supernova Time-Series Photometry (timedomain2)

Helen Qu (University of Pennsylvania)

21/06/2022 11:45

Time Domain Astrophysics

Talk

95. Bayesian DL methods in extragalactic, time-domain studies

Prof. Victoria Ashley Villar (Penn State university)

21/06/2022 14:15

Time Domain Astrophysics

96. Searching for changing-state AGNs in massive datasets with anomaly detection (timedomain1)

Paula Sanchez Saez (European Southern Observatory (ESO))

21/06/2022 15:15

Time Domain Astrophysics

Talk

130. Adapting to big data: active anomaly detection on ZTF (timedomain4)

Emille Ishida (Universit ́e Clermont Auvergne, CNRS/IN2P3, LPC)

21/06/2022 15:35

Talk

63. A Convolutional Neural Network Approach to Classifying Supernova Time-Series Photometry (timedomain2)

Helen Qu (University of Pennsylvania)

Time Domain Astrophysics

Talk

The use of type Ia supernovae (SNe Ia) as standardizable candles led to the Nobel Prize-winning discovery of the accelerating expansion of the universe and cemented their role in the quest to understand the nature of dark energy. Accurate cosmological parameter estimation requires a sample of pure SNe Ia with minimal non-Ia contamination, but supernova type confirmation using spectroscopy is...

78. Adapting to big data: active anomaly detection on ZTF (timedomain4)

Dr Emille Ishida (CNRS/LPC-Clermont)

Time Domain Astrophysics

Talk

In order to explore the potential of adaptive learning techniques to big data sets, the SNAD team used Active Anomaly Detection (AAD) as a tool to search for new supernova (SN) candidates in the photometric data from the first 9.4 months of the Zwicky Transient Facility (ZTF) survey – between 2018 March 17 and December 31 (58194 ≤ MJD ≤ 58483). We analyzed 70 ZTF fields with high galactic...

55. Searching for changing-state AGNs in massive datasets with anomaly detection (timedomain1)

Paula Sanchez Saez (European Southern Observatory (ESO))

Time Domain Astrophysics

Talk

The classic classification scheme for Active Galactic Nuclei (AGNs) was recently challenged by the discovery of the so-called changing-state (changing-look) AGNs (CSAGNs). The physical mechanism behind this phenomenon is still a matter of open debate and the samples are too small and of serendipitous nature to provide robust answers. In order to tackle this problem, we need to design methods...

74. SNAD: anomaly detection for large scale time-domain astronomy (timedomain3)

Dr Konstantin Malanchev (University of Illinois at Urbana-Champaign)

Time Domain Astrophysics

Talk

Modern large scale astronomical surveys scan wide areas of the sky following a predetermined cadence. As a result, their data sets contain records of both, known and unknown (or not yet observationally confirmed) astrophysical phenomena. Such datasets hold an immense potential for discovery, however, their volume and complexity makes successfully identifying such events a far from trivial...