PhyTS-team/PhyTS-bench

PhyTS Dataset

Abstract

We introduce PhyTS, a benchmark suite of precision scientific time series datasets for machine learning, spanning experiments in gravitational-wave detection, dark matter searches, neutrino mass determination, and stellar variability detection. Despite their diverse scientific goals, these domains share a common challenge: recovering weak, structured signals and estimating underlying physical parameters from noise-dominated measurements. Unlike standard sequence modeling benchmarks such as audio and speech, these data exhibit non-Gaussian and nonstationary noise, long-range temporal correlations, detector-specific systematics, irregular sampling, and signals that are sparse, weak, or only partially modeled. As a result, they provide a challenging testbed for evaluating whether modern AI methods can support downstream scientific inference. We provide standardized tasks, data splits, and evaluation protocols for denoising, signal recovery, and parameter inference across physics domains, along with baseline results. By unifying diverse weak-signal inference problems under a common framework, this benchmark aims to enable reproducible evaluation and accelerate the development of more robust, interpretable, and physically grounded methods for scientific time series analysis.

📌 Github repo: kyoon-mit/TimeSeriesPhysics

LIGO

LIGO Dataset Field Description

TESS

The TESS dataset is broken into training, validation and test data, which are identically constructed. There is a set for classification '/tess/split/tess_classification_{train,val,test}.parquet' and one for regression '/tess/split/tess_regression_{train,val,test}.parquet. The dataset was split 80/10/10 into training, validation, and test sets. Since multiple TESS light curves, from different TESS sectors, are available per unique Gaia Data Release 3 (DR3) identifier (ID), the split ensures that no Gaia DR3 ID appears in more than one split to avoid label leakage, with the exception of when different light curves for the same Gaia DR3 ID have different labels due to variations in dominant variability detected in separate TESS sectors. The data includes the following

TESS Dataset Field Description

Parameter	Type / Size	Description
GaiaID	dtype=int	Unique Gaia DR3 Identifier
TIC	dtype=int	TESS Input Catalog Identifier
sector	dtype=int	TESS Observing Sector (https://tess.mit.edu/observations/)
label	dtype=string	Class label [FOR THE CLASSIFICATION TASK]
frot	dtype=double	Near-core rotation rate of the star [FOR THE REGRESSION TASK]
frot_err	dtype=double	Near-core rotation rate error [FOR THE REGRESSION TASK]
time	dtype=list	Time stamps in BTJD (Barycentric TESS Julian Date. Corrected for light travel time to solar system barycenter)
flux	dtype=list	Time series measurements in normalized flux

TIDMAD

The TIDMAD dataset is broken into training and validation data which are identically constructed differing only by the time at which the detector data was taken. This temporal difference results in variable detector noise conditions. There are 20 hdf5 files (~ 4 GB/file) in both training and validation datasets. The total dataset size is 163 GB. Each file contains ~ 200 examples and the associated meta data. Each example is a set of ch1 time series, ch2 time series, and injected frequency. This data in each hdf5 file includes the following

TIDMAD Dataset Field Description

Parameter	Type / Size	Description
chunk_length	dtype=int	Number of samples per example
n_chunks	dtype=int	Number of examples per file
sample_rate_hz	dtype=int	Data taking sample rate in Hz
signal_freq_choices	size=(618,1), dtype=int	Array of signal frequencies injected across the full dataset, all files
signal_frequency	size=(200,1), dtype=int	Injected frequency for the example in Hz (signal_frequency[i] is the injected signal frequency for example i)
time_series_ch1	size=(200,10M), dtype=int	Noisy time series for the example in DAQ units. Conversion factor from DAQ units to mV is 40mV/128. (time_series_ch1[i] is the ch1 time series for example i)
time_series_ch2	size=(200,10M), dtype=int	Signal time series for the example in DAQ units. Conversion factor from DAQ units to mV is 40mV/128. (time_series_ch2[i] is the ch2 time series for example i)

Project 8

The Project 8 dataset is broken into training, validation, and test datasets which are identically constructed.
All contain a set of electron events with randomized energy, pitch angle, and radius parameters. The data are divided into hdf5 files with ~5000 electrons per file; each file is about 5 GB, with the exception of one, which has fewer electrons and is smaller.
There are about 50,000 electrons total in the dataset. The total dataset size is about 50 GB.
The train dataset contains 8 hdf5 files, the validation dataset has 1 hdf5 file, and the test dataset has 2 (including the smaller file). The experiments presented in the publication use output_ts_I + output_ts_I_cav_noise and output_ts_Q + output_ts_Q_cav_noise as inputs (noisy in-phase and quadrature time series). The target for regression is energy_eV (truth value for energy of the electron, in eV). This data in each hdf5 file includes the following

Project 8 Dataset Field Description

Parameter	Type / Size	Description
output_ts_I	size=(24576,1), dtype=float32	In-phase signal time series
output_ts_Q	size=(24576,1), dtype=float32	Quadrature signal time series
output_ts_I_cav_noise	size=(24576,1), dtype=float32	In-phase cavity (frequency-dependent) noise time series
output_ts_Q_cav_noise	size=(24576,1), dtype=float32	Quadrature cavity (frequency-dependent) noise time series
output_ts_I_gauss_noise	size=(24576,1), dtype=float32	In-phase Gaussian noise time series
output_ts_Q_gauss_noise	size=(24576,1), dtype=float32	Quadrature Gaussian noise time series
energy_eV	dtype=float32	Truth value for energy of the electron (target for regression), in eV
avg_carrier_frequency_Hz	dtype=float32	Truth value for average cyclotron frequency of the electron, in Hz
start_carrier_frequency_Hz	dtype=float32	Truth value for starting cyclotron frequency of the electron, in Hz
avg_axial_frequency_Hz	dtype=float32	Truth value for average axial frequency of the electron, in Hz
pitch_angle_deg	dtype=float32	Truth value for pitch angle (direction of the electron’s momentum with respect to the magnetic field) of the electron, in degrees
radius_input_m	dtype=float32	Truth value for starting radius of the electron before one trajectory step (input value), in meters
radius_m	dtype=float32	Truth value for starting radius of the electron after one trajectory step, in meters
radius_phase	dtype=float32	Truth value for instantaneous polar angle in the X-Y plane of the electron’s starting position, in meters
slope_Hz	dtype=float32	Truth value for slope of the track in spectrogram space, in Hz/s