Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Gilda, Sankalp"'
In time series analysis, traditional bootstrapping methods often fall short due to their assumption of data independence, a condition rarely met in time-dependent data. This paper introduces tsbootstrap, a python package designed specifically to addr
Externí odkaz:
http://arxiv.org/abs/2404.15227
In this paper, we present results on improving out-of-domain weather prediction and uncertainty estimation as part of the \texttt{Shifts Challenge on Robustness and Uncertainty under Real-World Distributional Shift} challenge. We find that by leverag
Externí odkaz:
http://arxiv.org/abs/2401.04144
Autor:
Gilda, Sankalp
Publikováno v:
Astronomy 2024, 3, 14-20
Traditional spectral energy distribution (SED) fitting techniques face uncertainties due to assumptions in star formation histories and dust attenuation curves. We propose an advanced machine learning-based approach that enhances flexibility and unce
Externí odkaz:
http://arxiv.org/abs/2312.14212
Autor:
Gilda, Sankalp
Traditional spectral analysis methods are increasingly challenged by the exploding volumes of data produced by contemporary astronomical surveys. In response, we develop deep-Regularized Ensemble-based Multi-task Learning with Asymmetric Loss for Pro
Externí odkaz:
http://arxiv.org/abs/2311.03738
Publikováno v:
Astronomy 2024, 3(3), 189-207
The prevalent paradigm of machine learning today is to use past observations to predict future ones. What if, however, we are interested in knowing the past given the present? This situation is indeed one that astronomers must contend with often. To
Externí odkaz:
http://arxiv.org/abs/2112.14072
Autor:
Gilda, Sankalp, Draper, Stark C., Fabbro, Sebastien, Mahoney, William, Prunet, Simon, Withington, Kanoa, Wilson, Matthew, Ting, Yuan-Sen, Sheinis, Andrew
We leverage state-of-the-art machine learning methods and a decade's worth of archival data from CFHT to predict observatory image quality (IQ) from environmental conditions and observatory operating parameters. Specifically, we develop accurate and
Externí odkaz:
http://arxiv.org/abs/2107.00048
Traditional spectral energy distribution (SED) fitting codes used to derive galaxy physical properties are often uncertain at the factor of a few level owing to uncertainties in galaxy star formation histories and dust attenuation curves. Beyond this
Externí odkaz:
http://arxiv.org/abs/2101.04687
Autor:
Gilda, Sankalp, Ting, Yuan-Sen, Withington, Kanoa, Wilson, Matthew, Prunet, Simon, Mahoney, William, Fabbro, Sebastien, Draper, Stark C., Sheinis, Andrew
Intelligent scheduling of the sequence of scientific exposures taken at ground-based astronomical observatories is massively challenging. Observing time is over-subscribed and atmospheric conditions are constantly changing. We propose to guide observ
Externí odkaz:
http://arxiv.org/abs/2011.03132
Autor:
Dainotti, Maria, Petrosian, Vahé, Bogdan, Malgorzata, Miasojedow, Blazej, Nagataki, Shigehiro, Hastie, Trevor, Nuyngen, Zooey, Gilda, Sankalp, Hernandez, Xavier, Krol, Dominika
Gamma-ray bursts (GRBs) are spectacularly energetic events, with the potential to inform on the early universe and its evolution, once their redshifts are known. Unfortunately, determining redshifts is a painstaking procedure requiring detailed follo
Externí odkaz:
http://arxiv.org/abs/1907.05074
Autor:
Gilda, Sankalp, Slepian, Zachary
Publikováno v:
Monthly Notices of the Royal Astronomical Society 490.4 (2019): 5249-5269
We propose a non-parametric method to denoise 1D stellar spectra based on wavelet shrinkage followed by adaptive Kalman thresholding. Wavelet shrinkage denoising involves applying the Discrete Wavelet Transform (DWT) to the input signal, `shrinking'
Externí odkaz:
http://arxiv.org/abs/1903.05075