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Deep Neural Networks use thousands of mostly incomprehensible features to identify a single class, a decision no human can follow. We propose an interpretable sparse and low dimensional final decision layer in a deep neural network with…

Computer Vision and Pattern Recognition · Computer Science 2023-08-08 Thomas Norrenbrock , Marco Rudolph , Bodo Rosenhahn

An open or hyperbolic Friedmann-Robertson-Walker spacetime dominated by tachyonic dark matter can exhibit an ``inflected'' expansion -- initially decelerating, later accelerating -- similar but not identical to that of now-standard…

Cosmology and Nongalactic Astrophysics · Physics 2024-03-22 Samuel H. Kramer , Ian H. Redmount

The light curves of Type Ia supernovae are routinely used to constrain cosmology models. Driven by radioactive decay of 56Ni, the light curves steadily decline over time, but >150 days past explosion, the near-infrared portion is poorly…

High Energy Astrophysical Phenomena · Physics 2019-10-10 Or Graur , Kate Maguire , Russell Ryan , Matt Nicholl , Arturo Avelino , Adam G. Riess , Luke Shingles , Ivo R. Seitenzahl , Robert Fisher

The use of Type Ia supernovae (SNe Ia) as cosmological standard candles is a key to solving the mystery of dark energy. Improving the calibration of SNe Ia increases their power as cosmological standard candles. We find tentative evidence…

Astrophysics · Physics 2009-06-23 Yun Wang , Nicholas Hall

Supernovae Type-Ia (SNeIa) play a significant role in exploring the history of the expansion of the Universe, since they are the best-known standard candles with which we can accurately measure the distance to the objects. Finding large…

Instrumentation and Methods for Astrophysics · Physics 2017-12-01 Akisato Kimura , Ichiro Takahashi , Masaomi Tanaka , Naoki Yasuda , Naonori Ueda , Naoki Yoshida

The peculiar velocities of supernovae and their host galaxies are correlated with the large-scale structure of the Universe, and can be used to constrain the growth rate of structure and test the cosmological model. In this work, we measure…

Accurate and interpretable air pollution forecasting is crucial for public health, but most models face a trade-off between performance and interpretability. This study proposes a physics-guided, interpretable-by-design spatiotemporal…

Machine Learning · Computer Science 2026-01-23 Zhiguo Zhang , Xiaoliang Ma , Daniel Schlesinger

We present a method of extrapolating the spectroscopic behavior of Type Ia supernovae (SNe Ia) in the near-infrared (NIR) wavelength regime up to 2.30 $\mu$m using optical spectroscopy. Such a process is useful for accurately estimating…

We present an empirical method that uses multicolor light curve shapes (MLCS) to estimate the luminosity, distance, and total line-of-sight extinction of Type Ia supernovae (SN Ia). The empirical correlation between the MLCS and the…

Astrophysics · Physics 2009-07-09 Adam Riess , William Press , Robert Kirshner

We improve estimates of stellar mass and mass-weighted average age of Type Ia supernova (SN Ia) host galaxies by combining UV and near-IR photometry with optical photometry in our analysis. Using 206 SNe Ia drawn from the full three-year…

We present ugriz light curves for 146 spectroscopically confirmed or spectroscopically probable Type Ia supernovae from the 2005 season of the SDSS-II Supernova survey. The light curves have been constructed using a photometric technique…

For spectroscopically confirmed Type Ia supernovae we evaluate models of intrinsic brightness variations with detailed data/Monte Carlo comparisons of the dispersion in the following quantities: Hubble-diagram scatter, color difference…

Due to their inherent capabilities of capturing non-local dependencies, Transformer neural networks have quickly been established as the paradigmatic architecture for large language models and image processing. Next to these traditional…

Quantum Gases · Physics 2024-08-01 Abhinav Suresh , Henning Schlömer , Baran Hashemi , Annabelle Bohrdt

We present optical light curves, redshifts, and classifications for 365 spectroscopically confirmed Type Ia supernovae (SNe Ia) discovered by the Pan-STARRS1 (PS1) Medium Deep Survey. We detail improvements to the PS1 SN photometry,…

We present a new method to photometrically delineate between various sub-types of type Ia supernovae (SNe Ia). Using the color-stretch parameters, $s_{BV}$ or $s_{gr}$, and the time of i-band primary maximum relative to the B-band or g-band…

I present an analysis for fitting cosmological parameters from a Hubble Diagram of a standard candle with unknown intrinsic magnitude dispersion. The dispersion is determined from the data themselves, simultaneously with the cosmological…

Cosmology and Nongalactic Astrophysics · Physics 2015-05-27 Alex Kim

Substantial effort has been devoted to the characterization of transient phenomena from photometric information. Automated approaches to this problem have taken advantage of complete phase-coverage of an event, limiting their use for…

Instrumentation and Methods for Astrophysics · Physics 2023-07-06 Alexander Gagliano , Gabriella Contardo , Daniel Foreman-Mackey , Alex I. Malz , Patrick D. Aleo

While O is often seen in spectra of Type Ia supernovae (SNe Ia) as both unburned fuel and a product of C burning, C is only occasionally seen at the earliest times, and it represents the most direct way of investigating primordial white…

Cosmology and Nongalactic Astrophysics · Physics 2016-03-02 Jeffrey M. Silverman , Alexei V. Filippenko

Optimal observables provide statistically powerful probes of small deformations from a reference theory, but in realistic collider measurements they are rarely available in compact analytic form. We show that interpretable event-level…

High Energy Physics - Phenomenology · Physics 2026-05-15 Jiahui Lin , Yandong Liu

We study the utility of a large sample of type Ia supernovae that might be observed in an imaging survey that rapidly scans a large fraction of the sky for constraining dark energy. We consider information from the traditional luminosity…

Astrophysics · Physics 2011-02-11 Andrew R. Zentner , Suman Bhattacharya