kartheikiyer

2024-09-26 09:20:12:

Headline: Unveiling the Cosmos: From Ultracool Dwarfs to Stellar Explosions and Exoplanet Discoveries

Recent advancements in astrophysics and cosmology have unveiled a wealth of new knowledge about the universe, ranging from the discovery of ultracool dwarfs to the intricate processes behind stellar explosions. Here’s a look at some of the most exciting developments.

Machine Learning and the Search for Ultracool Dwarfs
In a groundbreaking study, Brooks et al. (2024) have utilized a novel machine learning tool, SMDET, to analyze time series data from the Wide-field Infrared Survey Explorer (WISE), leading to the identification of 118 new ultracool dwarf candidates. This innovative approach enhances the efficiency of discovering these faint celestial objects, which are crucial for understanding stellar evolution and the distribution of spectral classes. Previous methods relied heavily on photometric surveys, but this new technique marks a s

kartheikiyer

2024-09-26 09:17:04:

Headline: From Machine Learning to Gravitational Waves: A New Era in Astrophysics Unfolds

Recent advancements in astrophysics and cosmology are reshaping our understanding of the universe, driven by innovative methodologies and cutting-edge technology. This month, a series of papers highlight significant breakthroughs in areas ranging from the discovery of ultracool dwarfs to the detection of gravitational waves, showcasing the power of machine learning and experimental techniques in modern astronomy.

Machine Learning and Stellar Discoveries: Unveiling Ultracool Dwarfs and Exoplanets
In a groundbreaking study, Brooks et al. (2024) have harnessed the power of machine learning to identify 118 new ultracool dwarf candidates using the SMDET tool, significantly enhancing the efficiency of data analysis from the Wide-field Infrared Survey Explorer (WISE). This advancement builds on previous efforts that relied on manual classification, allowing

kartheikiyer

2024-09-26 09:14:44:

Headline: From Stellar Explosions to Quantum Black Holes: Unraveling the Mysteries of the Cosmos

Recent advancements in astrophysics and cosmology have unveiled exciting insights into the universe, ranging from the dynamics of supernovae to the enigmatic behavior of black holes. This column highlights several groundbreaking studies that push the boundaries of our understanding.

Neutrino Transport and Supernova Dynamics A significant leap in modeling core-collapse supernovae has been achieved by Santos-Pérez et al. (2024), who introduced a minimally implicit Runge-Kutta method for the numerical integration of neutrino transport equations. This innovative approach addresses the computational challenges posed by stiff equations in optically thick environments, enhancing the accuracy and efficiency of simulations. By applying this method to realistic supernova models, the authors provide a clearer picture of neutrino interactions with matter, w

kartheikiyer

2024-09-27 09:05:12:

Headline: From Machine Learning to Moon Mysteries: Unveiling the Latest in Astrophysics and Exoplanet Research

Recent advancements in astrophysics and cosmology have opened new windows into the universe, from the discovery of ultracool dwarf stars to the atmospheric dynamics of exoplanets. Here’s a look at some of the most exciting developments.

Machine Learning Meets Ultracool Dwarfs
In a groundbreaking study, Brooks et al. (2024) have harnessed the power of machine learning to identify 118 new ultracool dwarf candidates using the SMDET tool. This innovative approach allows for the efficient analysis of vast datasets from the Wide-field Infrared Survey Explorer (WISE), significantly improving the accuracy of spectral type estimations. By validating spectral types through spectroscopy for two candidates, the authors demonstrate a shift towards automated discovery processes in astronomy, building on previous methods that were often time-co

kartheikiyer

2024-09-27 09:02:28:

Headline: New Insights into Dark Energy, Galaxy Clusters, and the Cosmic Web: A Glimpse into the Latest Astrophysical Discoveries

Recent advancements in astrophysics and cosmology have unveiled exciting new insights into dark energy, galaxy dynamics, and the elusive cosmic web. A collection of studies has made significant strides in understanding these complex phenomena, offering fresh perspectives and methodologies that could reshape our comprehension of the universe.

Dark Energy Dynamics: A Multifaceted Approach
The nature of dark energy remains one of the most profound mysteries in cosmology. Giarè et al. (2024) provide a comprehensive analysis of cosmic microwave background (CMB) data from multiple sources, revealing inconsistencies between Planck results and other CMB experiments regarding dynamic dark energy (DDE). Their findings suggest that while Planck strongly supports DDE, other datasets dilute this evidence, highlighting the n

kartheikiyer

2024-09-27 09:09:37:

Headline: From Machine Learning to Cosmic Mysteries: Recent Breakthroughs in Astrophysics and Cosmology

In the ever-evolving landscape of astrophysics and cosmology, recent studies have unveiled exciting advancements across various domains, from the identification of ultracool dwarfs to the dynamics of galaxy clusters. Here’s a look at some of the most intriguing findings.

Machine Learning and the Search for Ultracool Dwarfs
A groundbreaking study by Brooks et al. (2024) has harnessed the power of machine learning to identify 118 new ultracool dwarf candidates, significantly expanding our understanding of these faint celestial objects. Utilizing the SMDET tool, the researchers processed vast datasets from time series images, allowing for a more systematic classification of M, L, and T dwarfs, including subcategories like T subdwarfs. This automated approach marks a departure from traditional methods that relied heavily on manual classific

kartheikiyer

#-----------------------

def make_smooth_plot(truevals, predvals, lb, ub, nbins = 10, force_bw = True, bw = 0.3):
    
    xgrid = np.linspace(lb, ub, nbins)
    if force_bw == True:
        xbw = (ub-lb) * bw
    else:
        xbw = np.mean(np.diff(xgrid))

kartheikiyer

1. Create a new environment

module load python
module load jupyter-kernels
module load fi-utils
VENVDIR=/mnt/home/kiyer/jhive/
python -m venv --system-site-packages $VENVDIR/jhive_env
source $VENVDIR/jhive_env/bin/activate

2. Clone the FSPS repository

kartheikiyer

import scipy.stats
# given an SedFit() object from dense basis, this code allows you to sample from the z-M* posterior
# and can be adapted to more general cases as needed

def sample_from_2d_dist(atlas, sedfit, nsamples, zbins = np.linspace(0, 3, 201), massbins = np.linspace(6,12,201)):
    
    zbin_centers = zbins[0:-1] + np.mean(np.diff(zbins))/2
    massbin_centers = massbins[0:-1] + np.mean(np.diff(massbins))/2
    hist = np.histogram2d(atlas['mstar']+np.log10(sedfit.norm_fac), 
                      atlas['zval'].ravel(), 

kartheikiyer

# get the dispersion function from:
# https://jwst-docs.stsci.edu/jwst-near-infrared-spectrograph/nirspec-instrumentation/nirspec-dispersers-and-filters
# and put it in the _data_path folder

from astropy.table import Table
from sedpy.smoothing import smoothspec 

_data_path = 'disp_curves/'
grating = 'prism'
disp = Table.read(f'{_data_path}jwst_nirspec_{grating}_disp.fits')