SCREAM adapts the CATHODE method to treat stellar streams as feature-space over-densities, incorporates measurement uncertainties into neural network training, and achieves F1=0.745 on GD-1 while recovering faint members and a diffuse cocoon missed by prior methods.
Characterizing the GD-1 Stream with DESI DR2 Data: Thin Stream and Hot Cocoon
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
abstract
GD-1 is among the longest, coldest stellar streams in the Milky Way, making it an ideal target for probing dark matter substructure through dynamical heating. We present a catalog of 608 spectroscopically confirmed GD-1 members from the first three years of Dark Energy Spectroscopic Instrument (DESI) observations. This constitutes the largest homogeneous spectroscopic sample of GD-1, doubling the number of members previously available only through heterogeneous compilations combining multiple surveys with different systematics. Using these data, we derive updated stream tracks in sky position, proper motion, and radial velocity that extend over $100^\circ$ of the stream. We apply a Gaussian mixture model to decompose the stream into a dynamically cold thin component ($\sigma_V = 2.49\pm 0.28$ km s$^{-1}$, width $= 0.23\pm0.01^\circ$) and a kinematically hot cocoon ($\sigma_V = 6.13\pm0.75$ km s$^{-1}$, width $= 2.18\pm0.17^\circ$). The cocoon contains $\sim30\%$ of members and its velocity dispersion is consistent with $\sim11$ Gyr of heating by cold dark matter subhalos. We also detect a large proper motion dispersion ($41.36\pm4.98$ km s$^{-1}$) along the stream direction in the cocoon component. This feature indicates a significant line-of-sight distance spread in the cocoon, and its origin will be further explored in a forthcoming paper. These measurements demonstrate the power of DESI spectroscopy for characterizing the multi-component phase-space structure of stellar streams and constraining small-scale dark matter substructure.
fields
astro-ph.GA 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A differentiable forward model for weakly perturbed stellar streams forecasts that adding kinematics to density spectra improves dark matter free-streaming cutoff precision from 1.2 dex to 0.25 dex for a 5 Gyr stream.
citing papers explorer
-
Characterizing Stellar Streams with Error-Aware Machine Learning
SCREAM adapts the CATHODE method to treat stellar streams as feature-space over-densities, incorporates measurement uncertainties into neural network training, and achieves F1=0.745 on GD-1 while recovering faint members and a diffuse cocoon missed by prior methods.
-
A differentiable forward model for weakly perturbed stellar streams: substructure forecasts from density and kinematics spectra
A differentiable forward model for weakly perturbed stellar streams forecasts that adding kinematics to density spectra improves dark matter free-streaming cutoff precision from 1.2 dex to 0.25 dex for a 5 Gyr stream.