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77 records · Page 5

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Compressing the Cosmological Information in One-dimensional Correlations of the Lyman-$\alpha$ Forest

Observations of the Lyman-α forest from spectroscopic surveys such as the Baryon Oscillation Spectroscopic Survey or its extension, eBOSS, or the ongoing Dark Energy Spectroscopic Instrument (DESI) survey offer a unique window to study the growth of structure on megaparsec scales. Interpretation of these measurements is a complicated task, requiring hydrodynamical simulations to model and marginalize over the thermal and ionization state of the intergalactic medium. This complexity has limited the use of Lyα clustering measurements in joint cosmological analyses. In this work we show that the cosmological information content of the one-dimensional power spectrum (P 1D ) of the Lyman-α forest can be compressed into a simple two-parameter likelihood without any significant loss of constraining power. We simulate P 1D measurements from DESI using hydrodynamical simulations and show that the compressed likelihood is model independent and lossless, recovering unbiased results even in the presence of massive neutrinos or running of the primordial power spectrum.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Diffusion Codes: Self-Correction from Small(er)-Set Expansion with Tunable Non-locality

Optimal constructions of classical LDPC codes can be obtained by choosing the Tanner graph uniformly at random among biregular graphs. We introduce a class of codes that we call ``diffusion codes'', defined by placing each edge connecting bits and checks on some graph, and acting on that graph with a random SWAP network. By tuning the depth of the SWAP network, we can tune a tradeoff between the amount of randomness -- and hence the optimality of code parameters -- and locality with respect to the underlying graph. For diffusion codes defined on the cycle graph, if the SWAP network has depth $\sim Tn$ with $T> n^{2β}$ for arbitrary $β>0$, then we prove that almost surely the Tanner graph is a lossless ``smaller set'' vertex expander for small sets up size $δ\sim \sqrt T \sim n^β$, with bounded bit and check degree. At the same time, the geometric size of the largest stabilizer is bounded by $\sqrt T$ in graph distance. We argue, based on physical intuition, that this result should hold more generally on arbitrary graphs. By taking hypergraph products of these classical codes we obtain quantum LDPC codes defined on the torus with smaller-set boundary and co-boundary expansion and the same expansion/locality tradeoffs as for the classical codes. These codes are self-correcting and admit single-shot decoding, while having the geometric size of the stabilizer growing as an arbitrarily small power law. Our proof technique establishes mixing of a random SWAP network on small subsystems at times scaling with only the subsystem size, which may be of independent interest.

Combinatorics (math.CO)↗

DOE OSTI2023

A Lookup Table Based Low Resource Usage Waveform Data Compression Block in FPGA

aA lookup table based lossless data compression scheme suitable for FPGA implementation, with very low silicon resource usage, is developed. Different algorithms can be implemented by simply changing the contents of the lookup tables in the functional block while keeping the logic circuits unchanged. It exhibits performance comparable to the zip compression software in Linux distributions. The compression block can fit into almost any existing FPGA used in the online data acquisition systems of high energy physics experiments. The decompression process can be performed in either software or in FPGA firmware, allowing users to process compressed data flexibly.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

burtscher/LICO

LICO is a fast lossless image compressor. It takes BMP files (in 24-bit BMP3 format) as input.

Burtscher, Martin [Texas State Univ., San Marcos, ↗

DOE OSTI2022

A Pattern Dictionary Method for Anomaly Detection

In this paper, we propose a compression-based anomaly detection method for time series and sequence data using a pattern dictionary. The proposed method is capable of learning complex patterns in a training data sequence, using these learned patterns to detect potentially anomalous patterns in a test data sequence. The proposed pattern dictionary method uses a measure of complexity of the test sequence as an anomaly score that can be used to perform stand-alone anomaly detection. We also show that when combined with a universal source coder, the proposed pattern dictionary yields a powerful atypicality detector that is equally applicable to anomaly detection. The pattern dictionary-based atypicality detector uses an anomaly score defined as the difference between the complexity of the test sequence data encoded by the trained pattern dictionary (typical) encoder and the universal (atypical) encoder, respectively. We consider two complexity measures: the number of parsed phrases in the sequence, and the length of the encoded sequence (codelength). Specializing to a particular type of universal encoder, the Tree-Structured Lempel–Ziv (LZ78), we obtain a novel non-asymptotic upper bound, in terms of the Lambert W function, on the number of distinct phrases resulting from the LZ78 parser. This non-asymptotic bound determines the range of anomaly score. As a concrete application, we illustrate the pattern dictionary framework for constructing a baseline of health against which anomalous deviations can be detected.

97 MATHEMATICS AND COMPUTING↗