SpecPT (Spectroscopy Pre-trained Transformer) Model for Extragalactic Spectroscopy. I. Architecture and Automated Redshift Measurement
We introduce the Spectroscopy Pre-trained Transformer (SpecPT), a transformer-based model designed to analyze spectroscopic data, with applications in spectrum reconstruction and redshift measurement. Using the Early Data Release (EDR) of the Dark Energy Spectroscopic Instrument (DESI) survey, we ev...
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IOP Publishing
2025-01-01
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| Series: | The Astrophysical Journal |
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| Online Access: | https://doi.org/10.3847/1538-4357/ade053 |
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| author | Rohan Pattnaik Jeyhan S. Kartaltepe Clive Binu |
| author_facet | Rohan Pattnaik Jeyhan S. Kartaltepe Clive Binu |
| author_sort | Rohan Pattnaik |
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| description | We introduce the Spectroscopy Pre-trained Transformer (SpecPT), a transformer-based model designed to analyze spectroscopic data, with applications in spectrum reconstruction and redshift measurement. Using the Early Data Release (EDR) of the Dark Energy Spectroscopic Instrument (DESI) survey, we evaluate SpecPT’s performance on two distinct data sets: the Bright Galaxy Survey (BGS) and Emission Line Galaxy (ELG) samples. SpecPT successfully reconstructs spectra, accurately capturing emission lines, absorption features, and continuum shapes while effectively reducing noise. For redshift prediction, SpecPT achieves competitive accuracy, with normalized median absolute deviation values of 0.0006 and 0.0008, and catastrophic outlier fractions of 0.20% and 0.80% for BGS and ELG, respectively. Notably, SpecPT performs consistently well across the full redshift range (0 < z < 1.6), demonstrating its versatility and robustness. By leveraging its learned latent representations, SpecPT lays the groundwork for a foundational spectroscopic model, with potential applications in outlier detection, interstellar medium property estimation, and transfer learning to other data sets. This work represents a first step in building a generalized framework for spectroscopic analysis, capable of scaling to the full DESI data set and beyond. |
| format | Article |
| id | doaj-art-3f2e8cf05a9b4a03bf655a87da125876 |
| institution | Kabale University |
| issn | 1538-4357 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
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| series | The Astrophysical Journal |
| spelling | doaj-art-3f2e8cf05a9b4a03bf655a87da1258762025-08-20T03:28:01ZengIOP PublishingThe Astrophysical Journal1538-43572025-01-01988113910.3847/1538-4357/ade053SpecPT (Spectroscopy Pre-trained Transformer) Model for Extragalactic Spectroscopy. I. Architecture and Automated Redshift MeasurementRohan Pattnaik0https://orcid.org/0000-0003-3835-9898Jeyhan S. Kartaltepe1https://orcid.org/0000-0001-9187-3605Clive Binu2https://orcid.org/0009-0009-4635-9442Laboratory for Multiwavelength Astrophysics, School of Physics and Astronomy, Rochester Institute of Technology , 84 Lomb Memorial Drive, Rochester, NY 14623, USALaboratory for Multiwavelength Astrophysics, School of Physics and Astronomy, Rochester Institute of Technology , 84 Lomb Memorial Drive, Rochester, NY 14623, USALaboratory for Multiwavelength Astrophysics, School of Physics and Astronomy, Rochester Institute of Technology , 84 Lomb Memorial Drive, Rochester, NY 14623, USAWe introduce the Spectroscopy Pre-trained Transformer (SpecPT), a transformer-based model designed to analyze spectroscopic data, with applications in spectrum reconstruction and redshift measurement. Using the Early Data Release (EDR) of the Dark Energy Spectroscopic Instrument (DESI) survey, we evaluate SpecPT’s performance on two distinct data sets: the Bright Galaxy Survey (BGS) and Emission Line Galaxy (ELG) samples. SpecPT successfully reconstructs spectra, accurately capturing emission lines, absorption features, and continuum shapes while effectively reducing noise. For redshift prediction, SpecPT achieves competitive accuracy, with normalized median absolute deviation values of 0.0006 and 0.0008, and catastrophic outlier fractions of 0.20% and 0.80% for BGS and ELG, respectively. Notably, SpecPT performs consistently well across the full redshift range (0 < z < 1.6), demonstrating its versatility and robustness. By leveraging its learned latent representations, SpecPT lays the groundwork for a foundational spectroscopic model, with potential applications in outlier detection, interstellar medium property estimation, and transfer learning to other data sets. This work represents a first step in building a generalized framework for spectroscopic analysis, capable of scaling to the full DESI data set and beyond.https://doi.org/10.3847/1538-4357/ade053Convolutional neural networksNeural networksExtragalactic astronomyGalaxy spectroscopyRedshift surveys |
| spellingShingle | Rohan Pattnaik Jeyhan S. Kartaltepe Clive Binu SpecPT (Spectroscopy Pre-trained Transformer) Model for Extragalactic Spectroscopy. I. Architecture and Automated Redshift Measurement The Astrophysical Journal Convolutional neural networks Neural networks Extragalactic astronomy Galaxy spectroscopy Redshift surveys |
| title | SpecPT (Spectroscopy Pre-trained Transformer) Model for Extragalactic Spectroscopy. I. Architecture and Automated Redshift Measurement |
| title_full | SpecPT (Spectroscopy Pre-trained Transformer) Model for Extragalactic Spectroscopy. I. Architecture and Automated Redshift Measurement |
| title_fullStr | SpecPT (Spectroscopy Pre-trained Transformer) Model for Extragalactic Spectroscopy. I. Architecture and Automated Redshift Measurement |
| title_full_unstemmed | SpecPT (Spectroscopy Pre-trained Transformer) Model for Extragalactic Spectroscopy. I. Architecture and Automated Redshift Measurement |
| title_short | SpecPT (Spectroscopy Pre-trained Transformer) Model for Extragalactic Spectroscopy. I. Architecture and Automated Redshift Measurement |
| title_sort | specpt spectroscopy pre trained transformer model for extragalactic spectroscopy i architecture and automated redshift measurement |
| topic | Convolutional neural networks Neural networks Extragalactic astronomy Galaxy spectroscopy Redshift surveys |
| url | https://doi.org/10.3847/1538-4357/ade053 |
| work_keys_str_mv | AT rohanpattnaik specptspectroscopypretrainedtransformermodelforextragalacticspectroscopyiarchitectureandautomatedredshiftmeasurement AT jeyhanskartaltepe specptspectroscopypretrainedtransformermodelforextragalacticspectroscopyiarchitectureandautomatedredshiftmeasurement AT clivebinu specptspectroscopypretrainedtransformermodelforextragalacticspectroscopyiarchitectureandautomatedredshiftmeasurement |