What we do
Astronomy has large amounts of complex data from observations and simulations, which are traditionally used in isolation. This is where we come in.
- We build a global model of the sky from all available observations.
- We optimize the full experimental design process from instrumentation to survey strategy and data analysis.
People
Christian Jespersen
Graduate Student
Yan Liang
Graduate Student
Reese Owen
Undergraduate Student
Friends
Anthony Harness
Princeton
Former members
- Ben Horowitz (postdoc)
- Yunona Iwasaki (junior thesis)
- Satyue Lacayo (junior thesis)
- Andrew Chen (independent work)
- Remy Joseph (postdoc)
- Tianshu Wang (grad student)
- Lucas Makinen (senior thesis)
- Charles Minns (senior thesis)
- Charles Zhao (junior thesis)
Selected Papers
-
Autoencoding Galaxy Spectra II: Redshift Invariance and Outlier Detection
Liang, Y.; Melchior, P.; Lu, S.; Goulding, A.; Ward, C.
arXiv:2302.02496 -
Plausible Adversarial Attacks on Direct Parameter Inference Models in Astrophysics
Horowitz, B.; Melchior, P.
arXiv:2211.14788 -
Autoencoding Galaxy Spectra I: Architecture
Melchior, P.; Liang, Y.; Hahn, C.; Goulding, A.
arXiv:2211.07890 -
Mangrove: Learning Galaxy Properties from Merger Trees
Jespersen, C.; Cranmer, M.; Melchior, P.; Ho, S.; Somerville, R.; Gabrielpillai, A.
The Astrophysical Journal, 2022, 941, 7 -
Lightweight starshade position sensing with convolutional neural networks and simulation-based inference
Chen, A.; Harness, A.; Melchior, P.
arXiv:2204.03853 -
Accelerated Bayesian SED Modeling using Amortized Neural Posterior Estimation
Hahn, C.; Melchior, P.
The Astrophysical Journal, 2022, 938, 11 -
Graph Neural Network-based Resource Allocation Strategies for Multi-Object Spectroscopy
Wang, T.; Melchior, P.
Machine Learning: Science and Technology, 2022, 3, 015023 -
The challenge of blending in large sky surveys
Melchior, P.; Joseph, R.; Sanchez, J.; MacCrann, N.; Gruen, D.
Nature Reviews Physics, 2021, 3, 712 -
Joint survey processing: combined resampling and convolution for galaxy modelling and deblending
Joseph, R.; Melchior, P.; Moolekamp, F.
arXiv:2107.06984 -
Unsupervised Resource Allocation with Graph Neural Networks
Cranmer, M.; Melchior, P.; Nord, B.
PMLR, 2021, 148, 272-284 -
deep21: a Deep Learning Method for 21cm Foreground Removal
Makinen, T L; Lancaster, L.; Villaescusa-Navarro, F.; Melchior, P. and 3 co-authors
JCAP, 2021, 081 -
Hybrid Physical-Deep Learning Model for Astronomical Inverse Problems
Lanusse, F.; Melchior, P.; Moolekamp, F.
arXiv:1912.03980 -
Proximal Adam: Robust Adaptive Update Scheme for Constrained Optimization
Melchior, P.; Joseph, R.; Moolekamp, F.
arXiv:1910.10094 -
Filling the gaps: Gaussian mixture models from noisy, truncated or incomplete samples
Melchior, P.; Goulding, A. D.
A & C, 2018, 25, 183 -
SCARLET: Source separation in multi-band images by Constrained Matrix Factorization
Melchior, P. and 6 co-authors
A & C, 2018, 24, 129
Image credit: Dr. Hideaki Fujiwara - Subaru Telescope, NAOJ