A5042859670- Gamma-ray bursts and supernovae
- Galaxies: Formation, Evolution, Phenomena
- Astronomy and Astrophysical Research
- Stellar, planetary, and galactic studies
- Astrophysics and Cosmic Phenomena
- CCD and CMOS Imaging Sensors
- Cosmology and Gravitation Theories
- Astronomical Observations and Instrumentation
- Spectroscopy Techniques in Biomedical and Chemical Research
- Non-Invasive Vital Sign Monitoring
- Explainable Artificial Intelligence (XAI)
- Space Science and Extraterrestrial Life
- EEG and Brain-Computer Interfaces
- Machine Learning and Data Classification
- Computational Physics and Python Applications
- Bayesian Modeling and Causal Inference
- Adaptive optics and wavefront sensing
- Radio Astronomy Observations and Technology
- Software Engineering Research
- Spectroscopy and Chemometric Analyses
- Astrophysical Phenomena and Observations
- Particle Detector Development and Performance
- RNA and protein synthesis mechanisms
- History and Developments in Astronomy
- Atmospheric and Environmental Gas Dynamics
University of Pennsylvania
2021-2024
Ludwig-Maximilians-Universität München
2024
Lancaster University
2024
Centre National de la Recherche Scientifique
2024
Aix-Marseille Université
2024
Austin Peay State University
2024
University of Zurich
2024
American Public University System
2024
Oak Ridge National Laboratory
2024
California University of Pennsylvania
2023-2024
We present constraints on cosmological parameters from the Pantheon+ analysis of 1701 light curves 1550 distinct Type Ia supernovae (SNe Ia) ranging in redshift $z=0.001$ to 2.26. This work features an increased sample size, span, and improved treatment systematic uncertainties comparison original Pantheon results a factor two improvement constraining power. For Flat$\Lambda$CDM model, we find $\Omega_M=0.334\pm0.018$ SNe alone. Flat$w_0$CDM measure $w_0=-0.90\pm0.14$ alone, H$_0=73.5\pm1.1$...
Abstract We present a recalibration of the photometric systems in Pantheon+ sample Type Ia supernovae (SNe Ia) including those SH0ES distance-ladder measurement H 0 . utilize large and uniform sky coverage public Pan-STARRS stellar photometry catalog to cross calibrate against tertiary standards released by individual SN surveys. The most significant updates over “SuperCal” calibration used for previous Pantheon analyses are: (1) expansion number (now 25) filters 105), (2) solving all filter...
We present the full Hubble diagram of photometrically-classified Type Ia supernovae (SNe Ia) from Dark Energy Survey supernova program (DES-SN). DES-SN discovered more than 20,000 SN candidates and obtained spectroscopic redshifts 7,000 host galaxies. Based on light-curve quality, we select 1635 photometrically-identified SNe with redshift 0.10$< z <$1.13, which is largest sample any single survey increases number known $z>0.5$ by a factor five. In companion paper, cosmological results...
ABSTRACT We report constraints on a variety of non-standard cosmological models using the full 5-yr photometrically classified type Ia supernova sample from Dark Energy Survey (DES-SN5YR). Both Akaike Information Criterion (AIC) and Suspiciousness calculations find no strong evidence for or against any we explore. When combined with external probes, AIC agree that 11 15 are moderately preferred over Flat-$\Lambda$CDM suggesting additional flexibility in our may be required beyond constant....
Abstract We present the full Hubble diagram of photometrically classified Type Ia supernovae (SNe Ia) from Dark Energy Survey supernova program (DES-SN). DES-SN discovered more than 20,000 SN candidates and obtained spectroscopic redshifts 7000 host galaxies. Based on light-curve quality, we select 1635 identified SNe with redshift 0.10 < z 1.13, which is largest sample any single survey increases number known > 0.5 by a factor 5. In companion paper, cosmological results combined 194...
Abstract We present griz photometric light curves for the full 5 yr of Dark Energy Survey Supernova (DES-SN) program, obtained with both forced point-spread function photometry on difference images ( DiffImg ) performed during survey operations, and scene modelling (SMP) search processed after survey. This release contains 31,636 19,706 high-quality SMP curves, latter which contain 1635 photometrically classified SNe that pass cosmology quality cuts. sample spans largest redshift z range...
Redshift measurements, primarily obtained from host galaxies, are essential for inferring cosmological parameters type Ia supernovae (SNe Ia). Matching SNe to galaxies using images is non-trivial, resulting in a subset of with mismatched hosts and thus incorrect redshifts. We evaluate the galaxy mismatch rate biases on simulations modeled after Dark Energy Survey 5-Year (DES-SN5YR) photometric sample. For both DES-SN5YR data simulations, we employ directional light radius method matching. In...
Abstract We present a novel method of classifying Type Ia supernovae using convolutional neural networks, network framework typically used for image recognition. Our model is trained on photometric information only, eliminating the need accurate redshift data. Photometric data preprocessed via 2D Gaussian process regression into two-dimensional images created from flux values at each location in wavelength-time space. These “flux heatmaps” supernova detection, along with “uncertainty...
This note presents an initial survey design for the Nancy Grace Roman High-latitude Time Domain Survey. is not meant to be a final or exhaustive list of all strategy choices, but instead viable path towards achieving desired precision and accuracy dark energy measurements using Type Ia supernovae (SNe Ia). We describe that use six filters (RZYJH F) prism on Wide Field Instrument. has two tiers, one "wide" which targets SNe at redshifts up 1 "deep" targeting 1.7; each, four are used (with Y J...
We present constraints on cosmological parameters from the Pantheon+ analysis of 1701 light curves 1550 distinct Type Ia supernovae (SNe Ia) ranging in redshift $z=0.001$ to 2.26. This work features an increased sample size, span, and improved treatment systematic uncertainties comparison original Pantheon results a factor two improvement constraining power. For Flat$Λ$CDM model, we find $Ω_M=0.334\pm0.018$ SNe alone. Flat$w_0$CDM measure $w_0=-0.90\pm0.14$ alone, H$_0=73.5\pm1.1$ km...
Wavelength-dependent atmospheric effects impact photometric supernova flux measurements for ground-based observations. We present corrections on from the Dark Energy Survey Supernova Program's 5YR sample (DES-SN5YR) differential chromatic refraction (DCR) and wavelength-dependent seeing, we show their cosmological parameters $w$ $\Omega_m$. use $g-i$ colors of Type Ia supernovae (SNe Ia) to quantify astrometric offsets caused by DCR simulate point spread functions (PSFs) using GalSIM package...
Abstract We present a study of the potential for convolutional neural networks (CNNs) to enable separation astrophysical transients from image artifacts, task known as “real–bogus” classification, without requiring template-subtracted (or difference) image, which requires computationally expensive process generate, involving matching on small spatial scales in large volumes data. Using data Dark Energy Survey, we explore use CNNs (1) automate real–bogus classification and (2) reduce...
In this work, we present classification results on early supernova lightcurves from SCONE, a photometric classifier that uses convolutional neural networks to categorize supernovae (SNe) by type using lightcurve data. SCONE is able identify SN types at any stage, the night of initial alert end their lifetimes. Simulated LSST SNe were truncated 0, 5, 15, 25, and 50 days after trigger date used train Gaussian processes in wavelength time space produce wavelength-time heatmaps. these heatmaps...
As the scale of cosmological surveys increases, so does complexity in analyses. This can often make it difficult to derive underlying principles, necessitating statistically rigorous testing ensure results an analysis are consistent and reasonable. is particularly important multi-probe analyses like those used Dark Energy Survey upcoming Legacy Space Time, where accurate uncertainties vital. In this paper, we present a method test consistency contours produced these analyses, apply Pippin...
Abstract Upcoming photometric surveys will discover tens of thousands Type Ia supernovae (SNe Ia), vastly outpacing the capacity our spectroscopic resources. In order to maximize scientific return these observations in absence information, we must accurately extract key parameters, such as SN redshifts, with information alone. We present Photo- z SNthesis, a convolutional neural network-based method for predicting full redshift probability distributions from multi-band supernova lightcurves,...
In addition to adenosine-to-inosine RNA editing activities, ADAR1 has been shown have various editing-independent activities including modulation of RNAi efficacy. We previously reported that forms a heterodimer complex with DICER and facilitates processing pre-miRNAs mature miRNAs. miRNA synthesis, is involved in long dsRNAs into small RNAs (endo-siRNAs). Generation retrotransposon-derived endo-siRNAs by their functions regulation transcripts mouse oocytes reported. However, the synthesis...
We present here a re-calibration of the photometric systems used in Pantheon+ sample Type Ia supernovae (SNe Ia) including those for SH0ES distance-ladder measurement H$_0$. utilize large and uniform sky coverage public Pan-STARRS stellar photometry catalog to cross-calibrate against tertiary standards released by individual SN surveys. The most significant updates over `SuperCal' cross-calibration previous Pantheon analyses are: 1) expansion number (now 25) filters 105), 2) solving all...
Models trained on a labeled source domain (e.g., images from wildlife camera traps) often generalize poorly when deployed an out-of-distribution (OOD) target new trap locations). In the adaptation setting where unlabeled data is available, self-supervised pretraining masked autoencoding or contrastive learning) promising method to mitigate this performance drop. Pretraining improves OOD error generic augmentations used masking cropping) connect and domains, which may be far apart in input...
ABSTRACT Cosmological analyses with Type Ia Supernovae (SNe Ia) have traditionally been reliant on spectroscopy for both classifying the type of supernova and obtaining reliable redshifts to measure distance–redshift relation. While a host-galaxy spectroscopic redshift most SNe is feasible small-area transient surveys, it will be too resource intensive upcoming large-area surveys such as Vera Rubin Observatory Legacy Survey Space Time, which observe order millions SNe. Here, we use data from...
Feature-based methods are commonly used to explain model predictions, but these often implicitly assume that interpretable features readily available. However, this is not the case for high-dimensional data, and it can be hard even domain experts mathematically specify which important. Can we instead automatically extract collections or groups of aligned with expert knowledge? To address gap, present FIX (Features Interpretable eXperts), a benchmark measuring how well collection aligns...
We present the MULTIMODAL UNIVERSE, a large-scale multimodal dataset of scientific astronomical data, compiled specifically to facilitate machine learning research. Overall, UNIVERSE contains hundreds millions observations, constituting 100\,TB multi-channel and hyper-spectral images, spectra, multivariate time series, as well wide variety associated measurements "metadata". In addition, we include range benchmark tasks representative standard practices for methods in astrophysics. This...
Abstract We present the Multimodal Universe , a new framework collating over 100 TB of multimodal astronomical data for its first release, spanning images, spectra, time series, tabular and hyper-spectral data. This unified collection enables wide variety machine learning (ML) applications research across domains. The dataset brings together observations from multiple surveys, facilities, wavelength regimes, providing standardized access to diverse types. By uniform this data, aims...